Farrukh Iqbal Sheikh - Aaltoautsys.aalto.fi/en/attach/SpaceMaster-SecondRound-07-08/Draft... ·...

AB HELSINKI UNIVERSITY OF TECHNOLOGY

Department of Automation and Systems Technology

Farrukh Iqbal Sheikh

Real-Time Human Arm Motion Translationfor the WorkPartner Robot (Draft Version 1.0)

Thesis submitted in partial fulfillment of the requirements for the degree

of Master of Science in Technology

Espoo June 25, 2008

Supervisors:

Professor Aarne Halme Professor Kalevi Hyyppä

Helsinki University of Technology Luleå University of Technology

Instructor:

Research Scientist Sami Terho

Helsinki University of Technology

HELSINKI UNIVERSITY ABSTRACT OF THE

OF TECHNOLOGY MASTER’S THESIS

ii

Author: Farrukh Iqbal Sheikh

Title of the thesis: Real-Time Humar Arm Motion

Translation for WorkPartner Robot

Date: June 25, 2008 Number of pages: 87

Department: Automation & System Technology

Professorship: e.g. Automation Technology Code: e.g. AS-84

Supervisor: Prof Dr. Aarne Halme

Instructor: Research Scientist. Sami Terho

In response of ever increasing demand of intelligent robot in our society, the

natural ways of human robot interaction have been investigated in terms of

speech, vision and physical interfaces. As the speech and vision methods require

power processing and intensive calibration which makes them hard to implement.

Thus, the physical interfaces are still in use. These interfaces are improving the

cooperative behavior of man and machine.

The aim of thesis is to design and investigate the use of emerging motion capture

technique for the future robot. In thesis the human arm motion has been utilized

to control the human like manipulator of the robot in real-time dynamic task

environment. This technique offers great benefit in advance teleoperation and

robotic control through motion learning. Keeping main objective in mind the

various techniques of motion capture is reviewed. Based on reliability, accuracy

and real-time performance the inertial based MoCAP technique is selected. The

approach is validated by the construction of low cost 3D orientation sensor includ-

ing miniature accelerometer, gyroscope and magnetometer. The accuracy of the

3D orientation measurement has been enhanced by the implementation of sensor

fusion based real-time extended Kalman filter. The 3D orientation performance

using extended Kalman Filter is tested and verified. In the following an idea

of sensor sleeve comprises using four 3D orientation sensors is presented which

allow the user to control the human like robot arm directly using his arm motion.

Further, the virtual 3D kinematic simulator of the WorkPartner body included

manipulator is developed which was required to prevent damaging the real robot.

Finally the result of real-time human arm motion translation is presented and

briefly discussed.

Keywords: Motion Capture MoCAP, 3D orientation sensor,

Extended Kalman Filter, WorkPartner Simulator, Real-time motion conversion.

iii

TEKNILLINEN DIPLOMITYÖN

KORKEAKOULU TIIVISTELMÄ

Tekijä: Etunimi Sukunimi

Työn aihe: Työn otsikko

... voi olla pitkä

Päivämäärä: 25. kesäkuuta 2008 Sivumäärä: 87

Osasto: Osastosi

Professuuri: esim. Automaatiotekniikka Koodi: esim. AS-84

Työn valvoja: Etunimi Sukunimi

Työn ohjaaja: Etunimi Sukunimi

Suomennettu tiivistelmä tänne.

Avainsanat: avainsana-1, avainsana-2.

iv

Contents

1 Introduction 1

1.0.1 Thesis Objective . . . . . . . . . . . . . . . . . . . . 3

1.0.2 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . 4

1.1 Introduction of Space Mission Scenario . . . . . . . . . . . . 5

2 Literature Review 7

2.1 History of Biomechanics . . . . . . . . . . . . . . . . . . . . 8

2.2 Human Motion Capture (MoCAP) Systems . . . . . . . . . 10

2.2.1 Magnetic MoCAP . . . . . . . . . . . . . . . . . . . . 13

2.2.2 Acoustic MoCAP . . . . . . . . . . . . . . . . . . . . 14

2.2.3 Inertial And Magnetic MoCAP . . . . . . . . . . . . 15

2.3 Use of MoCAP in Robotics . . . . . . . . . . . . . . . . . . . 16

2.4 The WorkPartner Robot . . . . . . . . . . . . . . . . . . . . 18

2.4.1 Specification of the WorkPartner . . . . . . . . . . . 19

2.4.2 TorsoController . . . . . . . . . . . . . . . . . . . . . 20

2.5 Measurement Constrain in Human Arm Model . . . . . . . . 22

2.5.1 Gesture Recognition using Accelerometer . . . . . . . 24

3 Sensors for Angle Measurement 28

3.1 Working Principle of Inertial and Magnetic Sensor . . . . . . 29

3.1.1 Accelerometer . . . . . . . . . . . . . . . . . . . . . . 29

3.1.2 Gyroscope . . . . . . . . . . . . . . . . . . . . . . . . 31

3.1.3 Magnetometer . . . . . . . . . . . . . . . . . . . . . . 33

3.1.4 Bend Sensor . . . . . . . . . . . . . . . . . . . . . . . 35

v

3.2 Performance Evaluation of Angle Sensor . . . . . . . . . . . 36

3.2.1 Hardware Setup . . . . . . . . . . . . . . . . . . . . . 36

3.2.2 Inclination Angle using Inertial Measurement Unit

(IMU) . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.2.3 Heading Angle using Magnetometer . . . . . . . . . . 40

4 3D Orientation Measurement Using Inertial And Mag-

netic Sensor 44

4.1 Sensor Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1.1 Accelerometer Measurement Model . . . . . . . . . . 48

4.1.2 Gyroscope Measurement Model . . . . . . . . . . . . 49

4.1.3 Magnetometer Measurement Model . . . . . . . . . . 49

4.2 Filter Structure . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.2.1 Quaternion System State Model . . . . . . . . . . . . 51

4.2.2 Measurement Model . . . . . . . . . . . . . . . . . . 54

4.2.3 Update State Covariance . . . . . . . . . . . . . . . . 59

4.2.4 Error Covariance . . . . . . . . . . . . . . . . . . . . 60

4.3 Experiement . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.4.1 Case 0: Influence of Environment Noises . . . . . . . 64

4.4.2 Case 1: Influence of the external magnetic disturbance 66

4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

5 Kinematic Analysis & Modeling of WorkPartner Manip-

ulator 72

5.1 Specification of WorkPartner Manipulator . . . . . . . . . . 74

5.2 Kinematic Modeling of WP Manipulator . . . . . . . . . . . 75

5.2.1 WorkEnvelop . . . . . . . . . . . . . . . . . . . . . . 76

5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 78

5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

6 Results 79

vi

7 Summary and Conclusions 80

References 81

A Calibration of Orientation Sensor Module 88

A.1 Experiment 1: Determination of Qk and Rk . . . . . . . . . 90

B Name of the 2nd appendix 97

vii

List of Figures

2.1 Davinci Human Arm Drawing . . . . . . . . . . . . . . . . . 9

2.2 a)Borelli’s Observation (Toulmin, 2001),b)Human motion anal-

ysis (Etenne Marey) . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Gypsy ElectroMechanical MoCAP (Gypsy6, 2008) . . . . . . 12

2.4 Optical MoCAP for Animating "Lord of the Ring"character

Gollum (Yu, 2007) . . . . . . . . . . . . . . . . . . . . . . . 13

2.5 Magnetic MoCAP, Magnetic Field Transmitter located (James F

and Hodgins, 2000) . . . . . . . . . . . . . . . . . . . . . . . 14

2.6 Inertial and Magnetic Based MoCAP (Xsens, 2008) . . . . . 16

2.7 NASA Robonaut, Hybrid robot designed to use in future

space mission (Moreno, 2007) . . . . . . . . . . . . . . . . . 18

2.8 Artistic and Real WorkPartner Robot (TKK, 2008) . . . . . 19

2.9 Kinematic Model of WorkPartner Robot (TKK, 2008) . . . . 20

2.10 Torso Controller for WorkPartner (Suomela, 2004) . . . . . 21

2.11 Mechanical Model of Human Arm, bottom right part indi-

cate shoulder joint (Karim Abdel-Malek, 2003) . . . . . . . 23

2.12 Physical segment model of attached sensor frame, angle mea-

surement of joint segment using inertial sensor (Rong Zhu,

2004) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.13 Accelerometer configuration for elbow angle measurement (Satoshi KU-

RATA, 1999) . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.14 principle of shoulder angle measurement (Satoshi KURATA,

1999) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

viii

3.1 Three cases of single axis Accelerometer,A single axis ac-

celerometer mass suspended by the string, displacement of

mass from it position of equilibrium measure the accelera-

tion exert by the force,+ve and −ve indicates expansion and

compression . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2 The configuration of tri Axes Accelerometer using single axis

accelerometers. . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.3 Practical example of Coriolis AccelerationMovement of per-

son on the rotating platform exhibit coriolis acceleration which

is proportional to angular velocity of the frame (John Geen,

2008). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.4 Inner structure of Analog Devices iMEMs Gyroscope (John Geen,

2008). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.5 Principle of Hall Effect and Magneto resistive Magnetometer 34

3.6 Flexible bend Sensor (Sensors, 2008) . . . . . . . . . . . . . 35

3.7 Data Acquisition Hardware,Composed of single microcon-

troller with multiple SPI and ADC port for Inertial, magnetic

and bend sensor, two USART for RX,TX data communication. 37

3.8 Analog ADIS16350 Inertial Measurement Unit, Composed

of tri Axes accelerometer, Gyro and temperature sensor . . . 38

3.9 Inclination Measurement using IMU Tri Axes Accelerometer,

First Graph indicates the vector component, Second Graph

is the measured of corresponding inclination angle . . . . . . 39

3.10 Inclination Measurement using of IMU Tri Axes Gyros, Ob-

served angular rate of each axis of gyro vector . . . . . . . . 40

3.11 Integrated tri axis Gyro rotations, Roll, Pitch and Yaw Angles 41

3.12 Heading Measurement using Tri Axes Magnetometer, Top

graph is the measurement of magnetic vector, middle graph

is heading angle output and bottom is the magnetic loop

calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

ix

4.1 Structure of Sensor Fusion, Accelerometer As , Magnetome-

ter Ms and Gyroscope GS are combined to estimate quater-

nion based orientation, ZA and ZM are the calibrated sensor

outputs respectively, Qm is measurement quaternion obtained

by ZA and ZM , the estimated error in Qm is corrected with

the fusion of calibrated gyro ZG using Kalman filter based on

noise covariance Rk and Qk. . . . . . . . . . . . . . . . . . . 47

4.2 Magnetic inclination Angle,H is the projection of the F mag-

netic field vector on XY Plane of Earth surface, I is dip angle

by the surface. . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.3 Process Flow of Quaternion Based Extended Kalman Filter,

P (k|k) is state covariance, Q(k) is measurement Covariance,

R(k) is System Covariance, w(k) represents Measurement

Noise, z(k) represents observations equivalent to measure-

ment quaternion Qm , v(k) represents additional System Noise,

u(k) is gyro rate input equivalent to ZG and q(k) is the quater-

nion state vector to be estimated by the predictive process of

extended Kalman filter, q, z are state and measurement pre-

diction respectively and v(k + 1) measurement residual. . . . 52

4.4 Orientation in Earth Fixed Frame . . . . . . . . . . . . . . . 55

4.5 3D Orientation Sensor Module, Module is depicted in var-

ious views with their corresponding sensor reference frame

is indicated by the x(Red) ,y(Black) and Z( Green) at the

bottom corner . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4.6 Orientation sensor rotation about Roll φ , Pitch θ and Yaw

ψ, anticlockwise about an each axis is +ve deg and clockwise

is -ve deg. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.7 30min Tri Axis Acceleration, Gryroscope and Magnetometer

observation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

4.8 3D Euler orientation measurement at rest. . . . . . . . . . . 66

x

4.9 Histogram comparison of 3D orientation with and without

Kalman estimator. . . . . . . . . . . . . . . . . . . . . . . . 67

4.10 Tri Axis Magnetic Vector disturbance due to ferromagnetic

electric iron. . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.11 Yaw angle measurement with and without Kalman filter un-

der magnetic disturbance. . . . . . . . . . . . . . . . . . . . 68

4.12 BoxPlot of Yaw measurement with and without Kalman. . . 69

5.1 3D CAD model of WorkPartner Robot . . . . . . . . . . . . 73

5.2 Joint Specification of WorkPartner Manipulator . . . . . . . 74

5.3 DH Kinematic Model of WP Manipulator . . . . . . . . . . 76

5.4 WorkEnvelop of Single WP Manipulator,a)Left Side view, b)

Top viewShoulder motion range is -45 deg-45 deg in tilt and

-90 deg-90 deg in inclination, Elbow motion is in 0 deg-140

deg inclination followed by shoulder motion, Wrist motion is

-90 deg-90 deg in both inclination and rotation followed by

the shoulder and elbow motion. . . . . . . . . . . . . . . . . 77

5.5 3D Simulator for the WorkPartner . . . . . . . . . . . . . . . 78

A.1 Orientation sensor Modules,Left one module is developed by

Xsens Technologies, Right one is Self developed using ADIS16350

(Analog Devices) and HM55B (Hitachi). . . . . . . . . . . . 89

A.2 Tri Axis Accelerometer,Gyroscope and Magnetometer mea-

surement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

A.3 Histogram fit of Accelerometer,Gyroscope and Magnetometer. 92

A.4 Histogram fit of Quaternion Measurement Vector Components 93

A.5 Quaternion Observation using Discrete Integration Algorithm 94

A.6 Histogram fit of Quaternion Vector Components using Gyro

Angular rate. . . . . . . . . . . . . . . . . . . . . . . . . . . 95

A.7 Tri Axis Temperature Observation (30min) . . . . . . . . . . 96

xi

Symbols and Abbreviations

Ak,i[β] angle difference function

Bk,in,m matrix element correlation function

symbol explanation

HMI Human Robot Interaction

MoCAP Motion Capture

WP WorkPartner Robot

IMU Inertial Measurement Unit

xii

Chapter 1

Introduction

A constantly increasing the growth of complex autonomous and teleoper-

ated robots are becoming widespread in our society. A significant progress

made in the fields of Electronic, Mechanical and Computer Science are

equally contributing to introduce sophisticated artificial algorithms and in-

novative sensors and actuators designs that making robots more dynamic

and humanoid. In the same time, increase in processing power giving them

freedom to process complex autonomous algorithms for indoor and outdoor

robots. However, all the advancement and achievement to make robots ar-

tificially intelligent and capable to adopt environmental changes still far

from the present. Therefore, Human robot interaction is necessary up to

certain extent.

The Human Robot Interaction (HMI) is the field in which various methods

have been researched to develop the various schemes of interaction between

human and robots. Traditionally some of the techniques are utilizing nat-

ural way of interaction such as speech recognition, symbolic and vision

systems. These techniques require more processing and complex procedure

2

which makes them slow on today’s processor. Thus, the new techniques

have been researched in which human motion could be utilized to control

the robot for autonomous and non autonomous tasks. It is also known as

gesture recognition.

In last decade, the scope of human motion was limited for the application of

animating virtual character, biomedical etc. Nowadays the newly developed

motion capture techniques are being considered for the robotics application,

especially for the tele-operation of robotic arm. Today’s the concept of

humanoid and hybrid robots are popular. They have complete and semi

mechanical structure inspired by the human skeleton. Research made on

these robots are planned to be use in Space Planetary exploration and

rehabilitation of patient etc. The main idea of utilizing human arm motion

is to enhance the capability of such robots in complex task environment

through teleoperation and real-time direct operation. The WorkPartner

hybrid structured field service robot (TKK, 2008) is also being developed

to provide help to the user in complex task environment through natural

way of human interaction. The proposed researched is conducted for the

WorkPartner Robot. Using this approach semi autonomous feature of the

WorkPatner will be augmented which enables the user to control the motion

of manipulator of robot such that a specific object could be picked and

grab. While in autonomous mode, it could be used for motion teaching of

the robot. It will increase the ability of robot to adopt new motion through

motion learning mechanism. The advantage of using this approach is not

limited for field and service robots but it can be directly applied for future

humanoid robot and human like 7Dof robotics arm.

3

1.0.1 Thesis Objective

The focus of the thesis is on the real time translation of the human arm

motion sequences into the equivalent motion of human like manipulator of

the robots. In order to accomplish the main goal of the thesis work, it has

been structured in two main challenges. Each challenge is comprises on sub

problem that led to the specific goal of that challenge. The task hierarchy

is presented as follows.

1. Motion Capture technique of Human Arm Sequence

• Research and analyzed of traditionally developed various human

motion capture techniques.

• Study the possible measurement constrain.

• Develop a low cost motion sensor.

• Design and implement the sensor fusion based estimation algo-

rithm.

• Evaluate the performance of orientation sensor module for real-

time application.

• Record joint motion sequence.

1. Translation of real-time human arm motion sequence

• Researched and analyzed mode of operation of the WorkPartner

Manipulator.

• Analyzed motion translation constrains.

• Design and implement the 3D Kinematic Simulator of Work-

Partner Manipulator.

• Design and develop the central Graphical User Interface (GUI)

for real-time motion translation.

4

1.0.2 Thesis Outline

The Thesis report is organized in to the following chapters.

Chapter 1 provides the general overview of the thesis and possible chal-

lenges including space mission scenario designed for the use of MoCAP in

future planetary space exploration is discussed.

Chapter 2 the primitive motion capture technique followed by the history of

biomechanics and their use in various multidisciplinary fields are discussed.

Further the specification of the WorkPartner Robot is illustrated in con-

text of thesis. While in last the possible measurement constrain in Human

arm model in case of positioning sensor for an accurate joint measurement

based on inertial and low cost accelerometers are discussed.

Chapter 3 covers the basic principle of miniature inertial (accelerometer

and gyroscope), magnetometer and bend sensor. Based on their working

principle each sensor is evaluated for angle measurement. Finally, the effect

of noises present in each sensor is briefly described with the help of practical

experiment using developed data acquisition hardware and sensor circuitry.

Chapter 4 the effect of disturbance and noises in inertial (Accelerometer

and Gyroscope) and Magnetometer are mathematically modeled. Based on

their measurement state models, the Sensor Fusion based quaternion ex-

tended Kalman filter technique for an accurate complete 3d orientation

measurement using inertial (accelerometer and Gyroscope) and magne-

tometer is presented. Further the estimation filter design aspects such

as Measurement model, System state model, Update covariance and error

1.1 Introduction of Space Mission Scenario 5

covariance is covered. Finally, the performance of the filter is evaluated by

an experiments and their results are discussed.

1.1 Introduction of Space Mission Scenario

The motivation of this thesis is to provide the use of human motion cap-

ture technique for the future space mission. The typical scenario can be

imagined, when the human and robot will land together on the surface of

Mars for the exploration of life.

To define the mission, we need to make some assumption. These assump-

tions are as follows.

• Robot is WorkPartner, which has human like manipulator of 7 DOF

(Degree of Freedom).

• Astronaut suit which has some additional micro motion sensing ca-

pability using MEMS sensors.

• Portable micro computer is installed on the left arm of Astronaut

suit.

• The space camp has also provided by the space ship on the Mars.

• Solar panel is installed at the space camp.

Now, the mission is ready to imagine. Under all these assumption provided.

When the WorkPartner robot with semi autonomous mode (autonomous

and non autonomous) capability will starts to explore the field of Mars.

During the exploration the human astronaut will receive its moves, simul-

taneously by means of wireless link that works within 1Km range between

1.1 Introduction of Space Mission Scenario 6

astronaut computer and the robot (Martin, 1999).

First, the human astronaut starts the robot GUI on his computer, through

which human will interact with robot. This GUI is composed of all neces-

sary features of tele-robotics and navigational control etc. Now robot has

received the command by the astronaut to explore the field within circle

of 900m. Robot started and exploring the field. In the mean time during

exploration, human found some useful objects that can help to proof the

existence of human life. Here, the human Astronaut has taken the control

by switching the operational mode. After getting the control, he indicates

the object of interest by highlighting the object in GUI on his computer.

At this point the human astronaut has two choices one is to leave on the

robot to pick that object autonomously and other is to use the motion

translation mechanism for the precise and accurate teleoperation. The de-

cision of the choice is depends on the nature of object. If the object seems

to be sensitive and small then human motion translation would be the wise

choice. Otherwise autonomous operation would be better.

If we suppose that robot has some artificial intelligence capability like,

learning from experience. Still the human motion can be used to provide the

fastest and easiest way of teaching the robot about task specific motions.

Chapter 2

Literature Review

In order to develop the better understanding of proposed thesis work, re-

view of multi disciplinary works are necessary. Therefore, some relevant

papers and research articles from the fields of Biomechanics, Computer

Sciences, Robotic and Electronics have been reviewed. As the nature of

proposed researched is based on the utilization of human motion technique

for the future robot which can be categorized into the field of bio-mechanic

and robotics. Thus, the following sections covered the introduction of the

evolution of the field of bio-mechanics concerning human anatomy, studied

of various state of the art technologies of motion capture and their mea-

surement constrain in respect of real-time motion translation for the robot.

Further, the recent use of motion capture technique in the field of space

robotics is discussed. At last the possible constrain in motion measurement

technique using inertial and low cost miniature accelerometer is presented

followed by the WorkPartner specification.

2.1 History of Biomechanics 8

2.1 History of Biomechanics

In early centuries the human motion analysis was unimagined and rigorous

to comprehend. By the passage of time when the history of science com-

menced with primeval Greeks, who were first inquired human attributes to

comprehend the nature in nexus of our perception. As Socrates the great

scientist of Greek’s time taught that "we could not understand the world

unless we not able to understand our own Nature" (Martin, 1999). After his

execution, the intellectual concepts of Socrates had profound effect on his

student Plato. Plato was the first, who introduced basic theories of philol-

ogy and Mathematics concerning nature. Aristotle (384 BC - 322 BC),

the great physician went to the Athens and studied at Plato’s academy.

He had extraordinary capabilities of observing anatomy of living things as

mechanical system. The book "De Motu Animalium" has written by Aris-

totle was the proof of his advancement made in Biomechanics. Certainly,

he could be considered the first biomechanics. He was the author of book

called "De Motu Animalium"- which was based on the analysis of animal

anatomy (Martin, 1999).

After contribution of these philosophers the rebirth of biomechanics had

evolved once again due to the work made by the great Artist Lenoardo da

Vinci (1453-1519).He was the famous artist whose work has judged as an

engineer. He had the better understanding of today’s terminology of forces

vector, friction coefficient and the acceleration of falling object exact be-

fore their evolution (leonardo-da-vinci biography, 2008). He analyzed the

insight attributes of human structure such as muscles forces, joint motion

for his artwork as depicted in figure 2.1.

Later, the father of mechanics and modern science Galileo Galilee (1564-

1643) who structured the early studied of biomechanics mathematically.

2.1 History of Biomechanics 9

Figure 2.1: Davinci Human Arm Drawing

The progress made by Galileo, Borelli (1608-1679) able to define the force

required for equilibrium in various joints of human body earlier than New-

ton’s 3 laws of motion as shown in Figure 2. Perhaps, He was the first who

defined the center of gravity position of human structure. The work and ef-

fort made by these pioneer was followed by "Newton (1642-1727),Bernoulli

(1700-1783),Euler (1707-1783), Poiseuille (1799-1869), Young (1773-1829)

and other equal fame" (Schneck, 2000). Hence, the evolution of biome-

chanics and human motion analysis was started. First practical studied

in this field for human motion analysis was carried by the Etenne Marey

(1830 - 1904) through the use of image sequences as shown in 2.2.

Figure 2.2: a)Borelli’s Observation (Toulmin, 2001),b)Human motion anal-

ysis (Etenne Marey)

2.2 Human MoCAP Systems 10

2.2 Human MoCAP Systems

Motion capture MoCAP is the recording of human body poses or other

movement for immediate or delayed analysis and playback. The informa-

tion captured can be as general as the simple position of the body in space

or as complex as the deformations of the face and muscle masses.

In 20th century, there are many advancement have made in this field by the

multidisciplinary scientists who make them dream of capturing human mo-

tion live for certain application true. The main intention of motion capture

was studying the human motion and its kinematics which revealed diverse

results in the form of kinematics and inverse kinematic models, such as

minimum-torque-change model and Donders’s law etc. These studies were

based on posture based methods as described in (Marjan A. Admiraal

and Gielen, 2004). Now these studies are also helping the scientists to find

better understanding of human anatomy like muscle contractions about

articulating joint movement, based on the theory other aspects of human

motor control system and gait dynamics is also being improved. Currently,

the use of human motion capture has revolutionized the "Entertainment in-

dustry" by animating virtual character for games and 3D animated movies

for example Tomb raider (game), Beowulf etc. Probably, the use of motion

capture for computer character animation is relatively new. Because the

requirement of human motion capture system for animating virtual char-

acter in movies had realized in late 1970 after the great cartoon movie

Pinocchio and Snow white produced by Walt Disney, which was based on

"rotoscoping" technique (Johnston, 1981). In this technique artist sketch

the cartoon character behavior directly over the video sequence of human

performer. Besides this the use of human motion have also been providing

an evident helped in medical in such a way that disabilities of patient after

an accident could be improved by analyzing human walking pattern and


motion. This is also known as rehabilitation. Usually these analysis helped

athletes in sports. Now the use of real-time human motion capture system

with some accuracy can be realized in robotics. In such a way, that any

part of the robot, which is similar to the part of human body, could be used

to control the robot. This realization can be used for the motion teaching

and advance telemetry operation for the robots.

There are various techniques of capturing human poses have developed so

far, which one is suitable in context of the thesis is evaluated by reviewing

earlier developed motion capture systems. In gerneral they can be catago-

rized as follows.

1. Electro Mechanical MoCAP

2. Optical MoCAP

3. Magnetic MoCAP

4. Acoustic MoCAP

5. Inertial and Magnetic MoCAP

Electro Mechanical MoCAP

It is one of the earliest developed methods for capturing human motion.

In this technique the combination of on/off mechanical switch and com-

plex motion tracking systems is used. The designed is based on a set of

armatures that are attached all over the performer’s body as shown in fig-

ure 2.3.In this approach armatures are connected to each other by using

a series of rotational and linear encoders. These encoders are wired to an

interface that can be simultaneously read all the encoders in order to pre-

vent data skewing. However, this technique provide clean rotational data


that can be collected in real time without any occlusion problems. Finally,

through a set of trigonometry functions, the performer’s motion are ana-

lyzed and recorded. The main disadvantage is the design restrictions which

seem to be quite difficult to overcome, and will probably limit the use of

these type of devices for character animation (Wes Trager, 1999).

Figure 2.3: Gypsy ElectroMechanical MoCAP (Gypsy6, 2008)

Optical MoCAP

Optical MoCAP is developed in late 80’s for capture human poses. It is

currently used in character animation of movies as shown in figure 2.4.

The principle on which it is working is based on triangulation method by

camera at the orthogonal position. There are two ways of implementing

an optical MoCAP. One is by placing passive reflectors markers (retro-

reflective material), on the human joint position that reflects existing light

present in the environment and second is by using Active Reflector marker

such as LED that blinks in timely manner.

Optical MoCAP utilizes proprietary video cameras to track the motion of

reflective markers (pulsed LED’s) attached to joints of the actor’s body.

Single or dual camera systems are suitable for facial capture, while 3 to 16

or more camera systems are necessary for full-body capture. Reflective op-

tical MoCAP uses Infra-red (IR) LED’s mounted around the camera lens,

along with IR pass filters placed over the camera lens. Optical motion cap-

ture systems based on Pulsed-LED’s measure the Infra-red light emitted


Figure 2.4: Optical MoCAP for Animating "Lord of the Ring" character

Gollum (Yu, 2007)

by the LED’s rather than light reflected from markers (Wes Trager, 1999).

This system suffer due to the occlusion (Line of Sight) problem. The noise

due to the occlusion is compenstated by the virtual rigid human human

skeleton for enhancing the accuracy of the Optical MoCAP (L. Herda and

Thalmann, 2000).The main disadvantage of this system is computationally

expensive,cost factor and large space requirement for the operation which

is not suitable for real-time and portable application.

2.2.1 Magnetic MoCAP

In Magnetic MoCAP the tri axis magnetic sensor are strapped on the hu-

man joints that measure the change in direction of magnetic field generated

by the transmitting source as shown in figure 2.5 . In this approach change

in vector direction provides the measure of 3D position and orientation of

human Joint in calibrated workspace. Finally, the resulting data stream

is usually applied in inverse kinematic model to animate the human skele-

ton. The main intent of this technique was to avoid the problem due to

occlusion problem as presented in optical MoCAP. However, the sense

magnetic field decreases as distance increases by the transmitter and can


be affected by the interference of addition magnetic field and ferromagnetic

material nearby. Therefore, these dependencies limit the use of system in

fixed calibrated environment. Hence, it cannot be applied for the robotics

application but it is feasible for teleoperation of robotic arm (James F and

Hodgins, 2000).

Figure 2.5: Magnetic MoCAP, Magnetic Field Transmitter lo-

cated (James F and Hodgins, 2000)

2.2.2 Acoustic MoCAP

Acoustic MoCAP is another method has been used for recording human

motion. In this technique the triad low cost audio receivers are located at

distance and arrays of audio transmitters are strapped to various joint parts

of the human body is used. These transmitters are sequentially triggered

to produces encoded sound signal and each receiver measures the time of

flight of the transmitted signal. Thus, the calculated distance of the three

receivers is triangulated to measure a point motion in 3D space. An inher-

ent issue with this approach is the sequential nature of the position data

which require additional processing. This position data is typically applied

to an inverse kinematics system that drives an animated skeleton.

One of the big advantages of this method is the lack of occlusion problems

normally associated with optical systems. However, the several negative


factors associated with this method made this approach complex. First,

there is the fact that the cables can be a hindrance to various types of

performances which can be avoided with use of wireless data transmitter.

Second, the limitation of number of sensors cannot be increased to capture

the completed motion sequences. Third is the size of the capture area,

which is limited by the speed of sound in air and the number of transmitters.

In addition, the accuracy of this approach can sometimes be affected by

spurious sound interference.

2.2.3 Inertial And Magnetic MoCAP

The motion capture technique based on miniature inertial and magnetic

sensor is recently revealed in the last few years. This system is currently

being used by Xsens Technology (Xsens, 2008). In this technique the combi-

nation of Inertial (gyroscope and accelerometer) and magnetic sensor is uti-

lized to measure the orientation of human joint. The MEMS (Micro Elector

Mechanical Sensor) technology made this approach feasible for human mo-

tion application. In this approach the ambulatory tracking of human poses

is implemented by fusing the tri axis gyro, tri axis accelerometer and tri axis

magnetic sensor data using complementary Kalman filter (Daniel Roeten-

berg and Veltink, 2007a; Rong Zhu, 2004).

A complementary Kalman filter that operated on error model rather than

system state model presented in (Daniel Roetenberg and Veltink, 2007a) is

used to estimate the complete 3D orientation. As the gyroscope measures

the angular velocity which can be integrated in time to obtain the orienta-

tion of the sensor module. However the small gyro drift caused the large in-

tegration error in time measurement. In order to compensate the gyro drift,

the absolute orientation reference is provided by the use of accelerometers

and magnetometers. In this approach the magnetic sensor measurement

2.3 Use of MoCAP in Robotics 16

could be affected by the Ferromagnetic materials nearby. This problem

can be categorized into two types, one as hard disturbance and other as

soft disturbance. The hard disturbance is due to the magnet object present

in the environment. It can be eliminated by means of system calibration

but the soft disturbance affect by the Ferros material nearby has been

overcome by the use of Kalman-based fusion algorithm (Daniel Roetenberg

and Veltink, 2007a).The typical coniguration of multiple inertial sensor on

human skeleton is indicated in figure 2.6.

Figure 2.6: Inertial and Magnetic Based MoCAP (Xsens, 2008)

However the use of Inertial and Magnetic motion capture allow us to ac-

curate implement the real-time ambulatory tracking of human posture and

movement for the robotics application. Although, there are some measure-

ment error still associated in this approach but it can be rectified further

by fusing redundant measurement through goniometry and optical MoCAP

measurement data.

2.3 Use of MoCAP in Robotics

Motion capture system can be applied in various robotics applications as

stated earlier, such as virtual robotics control, task teaching for humanoid

2.3 Use of MoCAP in Robotics 17

robot, entertainment, bio medical and telerobotics. The aim of this thesis

work is to implement the motion capture of human arm for the manipula-

tor of work partner robot. The advantage of capturing human arm motion

sequences is because of unique motion sequences that allow human to do

routinely task efficiently. This motion can be possibly applied for any hu-

man like robotic arm of 7 DOF.

The task teaching through learning and telerobotic are the potential ap-

plications of motion capture systems. Recently, the advancement made

in integrated MEMS (Micor Electro Mechanical System) sensors and com-

puter vision algorithm made this technique possible for the field human

robot interaction. As the work presented in article (Yasuyoshi YOKOKO-

HJI, 2002), in which author has proposed a stereo vision based motion

learning method for the robot. In this approach the humanoid robot is

used to adopt the human motion sequence directly from the work envi-

ronment. First the robot records the human motion sequence and then it

patterns the sequence such that the motion could be learned and reused.

Although it is the more natural way of interacting with robot but due to

the complexity of vision algorithm and dynamic task environment it is hard

to processed in real-time.

In similar way, the NASA is utilizing the inertial sensor based motion cap-

ture techniques to control the movement of Astronaut robot called Robo-

naut as presented in (Miller, 2004). In the presented work the motion

technique is utilized for the teleoperation of the robonaut. It is designed

to remotely control the robonaut in such a way the problem occurred in

Space Station could be fixed without intervention of human astronaut. The

typical view of robonaut is shown in figure 2.7.

2.4 The WorkPartner Robot 18

Figure 2.7: NASA Robonaut, Hybrid robot designed to use in future space

mission (Moreno, 2007)

2.4 The WorkPartner Robot

A mobile and service robot is being designed to work interactively with hu-

mans in urban environment by using natural communication means. The

development of such capabilities in the robots is one of the biggest chal-

lenges of future intelligent machines. The basic design attributes of service

robots are light weight structure, mobile, flexible and ability to adapt the

task motion and environmental changes through learning interface. Based

on some of these attributes the robot ASIMO (ASIMO, 2008) has been

developing but most of the features are still under developed. The design

of robot ASIMO is inspired by the human structure and its motion, such

robot are also called "Humanoid ROBOT".

By considering the future of intelligent service robot, the Department of

Automation and System technology of Helsinki University of Technology,

Finland have been developing the service robot called Workpartner, since

decade as shown in figure 2.8 . It is designed to perform daily routine tasks

through interaction with the people in the urban environment. Some of

interaction has been done in terms of speech recognition, symbolic repre-

sentation and human wearable mechanical gesture recognition device (Torso


controller). Further techniques of interaction are being researched for en-

hancing intelligence.

Figure 2.8: Artistic and Real WorkPartner Robot (TKK, 2008)

2.4.1 Specification of the WorkPartner

The complete structure of the work partner robot is hybrid. It comprises

on four legs with active body joint wheels that enhance the mobility of

the robot in narrow areas, while the wheel allow the robot to travel fast

enough on uneven terrain. The actuation system is electrical powered by

the means of batteries and the combustion engine generator. It can attain

maximum speed of 7 Km/h on hard terrain using wheel motion as pre-

sented in (Aarne Halme and Kettunen, 2003).

As can be seen in figure 2.8, the front part of the robot is comprise on two

human arms like manipulator, body and vision cameras installed at head

position. The Segments of arm are revolute joined by the rotating actu-

ators. These rotating actuators are simple DC motors with tailor made

planetary gears. The brake in each acutators allow the user to manullay

positioned the robotic manipulator.


The Robot WorkPartner single side manipulator is comprises on 5 revolute

joints as shown in figure 2.9. In which, 2 are used for wrist motion (one

is for inclination and one is for rotation), one is for elbow and 2 are used

to represent shoulder motion (one is for inclination and one is for yaw

rotation). All joints actuators are controlled by their dedicated controller.

The controller is connected with the main embedded board running on

QNX Embedded operating system through CAN (Controller Area Network)

bus. The function of embedded board is to decode the receive commands

received by the wireless link and generate the appropriate control signals

for the sub controllers.

Figure 2.9: Kinematic Model of WorkPartner Robot (TKK, 2008)

2.4.2 TorsoController

In order to control the robot manipulator motion for task teaching by the

use of direct teleoperation the "Torso Controller" was developed (Suomela,

2004). The torso controller was designed to controls the WP manipula-

tor directly by the operators arm movement. It can be categorized into

electromechnical motion capture system. In torso controller techniques op-

erator wear the elecrto mechanical controller suit on his shoulders as shown

in Figure 8. The controller suit is comprises of inertial sensor, which mea-

sure bend angle of human body using accelerometer and gyroscope, while


the position of human arm was carried out by the 2DOF gimbaled wire

potentiometer.

Figure 2.10: Torso Controller for WorkPartner (Suomela, 2004)

However, it is low cost solution which was specifically designed for the ma-

nipulators of WorkPartner but it is not feasible for the complete translation

of human arm motion which is comprises on 7DOF. The main disadvan-

tage of this approach is the use of wired potentiometer whose rotation is

tethered by the joystick which must be grabbed to control the manipulator

of the robot. Therefore, it can only be used to point the target object but

whenever the operator want to grab the object is not possible in natural

way using his palm and wrist fingers motion.

2.5 Measurement Constrain in Human Arm Model 22

2.5 Measurement Constrain in Human Arm

Model

The anatomy of human arm is the real complex entity. In order to do

the real-time translation of human arm motion for the robot then the un-

derstanding of the human arm anatomy is equally important as capturing

human arm posture. In general, the upper limb is composed of three me-

chanical like joints shoulder, Elbow and wrist, their combination allow to

move arm possibly in any direction. Due to the complex anatomy of human

arm, it has highest mobile part of the human body. The typical attached

reference frame on each joint in human arm can be seen in figure 2.11. In

total, the DOF of human arm are 9. If we simplify the DOF by ignoring

the joint motion q1 and q2, which is relatively less mobile as compare to

other joints then the total DOF will reduce to 7. It can be confirmed by

the Figure 10. In which q3, q4 and q5 are representing the joint motion

of shoulder, where we can place the reference frame for the translation of

motion. While the elbow joint can be seen as q6 that directly link with

the motion of shoulder joint. In the end, the wrist joint with 3 DOF q7,

q8 and q9 whose motion links with elbow and shoulder according to the

rigid body mechanism. Further detailed is presented in (W. Maurel, 2004;

Karim Abdel-Malek, 2003).

The knowledge of human arm anatomy helps to define the position of

sensors that could provide an accurate measurement of joint orientation.

There are two types of problem associated with human arm motion due to

anatomy. First there is no rotary mechanical type joint in between the two

segments of human arm. Secondly the flexion in human joint during motion,

which could results in small translation of segment in body attached sensor

frame but its magnitude is small enough and could be ignored and assumed

no translation in sensor attached frame. These measurement problems has

been considered and simplified with the use of kinematic and trigonometric


Figure 2.11: Mechanical Model of Human Arm, bottom right part indicate

shoulder joint (Karim Abdel-Malek, 2003)

theory as discussed in (Rong Zhu, 2004).

Figure 2.12: Physical segment model of attached sensor frame, angle mea-

surement of joint segment using inertial sensor (Rong Zhu, 2004)

According to kinematic theory, the orientation of human joint like elbow

can be determined using motion data acquired by the sensors (tri axis in-

ertial plus magnetic sensor module) positioned on segments as shown in

figure 2.12. As can be seen in Figure 11, there are two sensors frames at-

tached to the segments respectively. One sensor frame is represented by

frame i , while other one is by frame i+1 .


The respective attached tri-axis 3d motion sensor including accelerometer,

gyroscope and magnetometer on the segment provide the measurement

in sensor frame. Each sub sensors measure its corresponding x, y and

z component in sensor coordinate that has been combined using Kalman

Filter. It measures the orientation in sensor frame. As indicated earlier the

two sensors have been used, one is on upper segment and the other is on

lower segments. Finally the rotation around the elbow joint denoted by θ,

has been computed using rotation matrix as shown in (2-1).where Ki,i+1

= [ Kx, Ky,Kz], in frame Xi,Yi and Zi Versθ = 1- cosθ 2.1.

Rot(Ki+1

i , θ) =

K2xV ersθ + cos θ KxKyV ersθ −Kz sin θ KxKzV ersθ +Ky sin(θ)

KxKyV ersθ +Kz sin θ K2yV ersθ + cos θ KyKzV ersθ −Kx sin θ

KxKzV ersθ −Ky sin θ KyKzV ersθ +Kx sin θ K2yV ersθ + cos θ

(2.1)

The result using this approach shows that the two 3d motion sensors on

connected segment could be accurately used to measure the rotation about

the joint. The Author in (Rong Zhu, 2004) has tested the same principle

on the human arm to measure elbow joint rotation by positioning sensor

on upper and lower limb of human arm. It shows that the same method is

applicable to record human arm motion sequence.

2.5.1 Gesture Recognition using Accelerometer

The cost effective solution for upper limb joint motion recognition is also

possible by the use of low cost tri axes accelerometer as presented in (Satoshi KU-

RATA, 1999). In this method the author states that if two tri axes ac-

celerometers are positioned near to the joint then the difference of these

sensors output produces by the rotation around the joint can be used to

estimate the angle between the limbs.


The author has tested his method on elbow like joint that produces rotation

about one axis as shown in figure 2.13.

Figure 2.13: Accelerometer configuration for elbow angle measure-

ment (Satoshi KURATA, 1999)

In this approach the elbow motion has measured by the tri axes accelerom-

eter 1 and 2 both are positioned near to the actual joint motion. According

to the author the accuracy of method depends on how close these two sen-

sors are from the joint. This configuration is named by the author as "Both

near Sides" as presented in (Satoshi KURATA, 1999).

By the practical, the different amount of loaded acceleration a on ac-

celerometer 1 and acceleration a’ on accelerometer 2 due to the rotation of

rigid shoulder joint has observed. Using x and y component of acceleration

of accelerometer 1 (ax1, ay1), and 2 (ax2,ay2), the angle between two limbs

around elbow joint as rotation matrix is described as shown in 2.2.

(

ax1

ay1

)

=

(

cos(θ) − sin(θ)

sin(θ) cos(θ)

) (

ax2

ay2

)

(2.2)

Thus, the joint angle θ can be described by 2.3.

tan(θ) =ax2, ay1 − ax1.ay2

ax1.ax2 − ay1.ay2

(2.3)


This similar approach has also tested for the shoulder joint. In this case

the different configuration of two tri axes accelerometers mounted near to

shoulder joint is used as shown in figure 2.14.

Figure 2.14: principle of shoulder angle measurement (Satoshi KURATA,

1999)

In this configuration the acceleration a and a’ of two tri axis accelerometers

on the respective positions has combined with the Euler rotation matrix as

shown in equation 2.4.

ax1

ay1

az1

= Rxyz

ax2

ay2

az2

(2.4)

As stated earlier shoulder joint is complex, therefore the acceleration of

frames a and a’ cannot give the measure of orientation. This problem has

been resolved by the use of gravitational component of respective frame as

shown in equation 2.5.

gx1

gy1

gz1

= Rxyz

gx2

gy2

gz2

(2.5)


Using equation 2.5, the equation 2.6 and 2.7 has been derived to obtain

the rotation angles.

tan(α) =

√

(sin(Ψ)2 + (tan θ)2

cos(θ)(2.6)

tan(β) =sin(Ψ)

tan(θ)(2.7)

However, when the subject is either moving fast or at rest the equation 2.6

and 2.7 will no longer be applicable as discussed (Satoshi KURATA, 1999).

This indicates that the user cannot freely obtained its motion sequence for

the application of robotics but proposed method is suitable to implement

a low cost solution for only the elbow joint.

Conclusively, the most appropriate approach according to the requirements

of the thesis is leads to inertial and magnetic sensor motion capture scheme.

The advantages and portability of motion capture using inertial and mag-

netic sensor package with implementation of estimation algorithms can pro-

vide better results for the real-time translation of human motion sequences

to the manipulator of Robot.

Chapter 3

Sensors for Angle Measurement

Sensors are the most commonly use devices in any application. They are

used to convert the physical quantity such as acceleration, velocity, distance

etc into proportional electrical signals. This section describes the absolute

detail of sub sensors of orientation sensor module with the help of their

basic principle and numerous practical tests. The results and knowledge

obtained by the following tests are discussed in the following section. There

are two types of sensors, some sensors are based on the principle of MEMS

technology (Accelerometer and Gyroscope) and other sensors are based on

material deformation properties and basic electromagnetic law for measur-

ing physical quantity, such as the Earth’s magnetic field.

The low cost MEMS (Micro Electro Mechanical System) inertial sensors

have been widely used in variety of applications for measuring acceleration

and angular motion. The intention of developing the low cost MEMS is to

provide the valuable low cost solution for various applications, where cost,

size and power consumption are major concerned. A typical use of inertial

sensor can be seen in cruise attitude control, robotics navigation and also in

3.1 Working Principle of Inertial and Magnetic Sensor 29

today’s smart phone interfaces that allows user to interact with the future

mobile device in unconventional way. According to these applications it can

also be used to measure the orientation of human joint poses as presented

in (Stilson, 1996).

3.1 Working Principle of Inertial and Mag-

netic Sensor

3.1.1 Accelerometer

A single axis accelerometer measures the acceleration along sensitive axis.

It contains a mass suspended by the spring in sensor housing as shown in

figure 3.1. The mass is only allowed to displace in one direction by the

effect of force and acceleration due to gravity. The displacement of mass

in sensitive direction measures the acceleration (a) and direction of gravity

vector (g) in presence of environment noises.

The physical principal on which it is working is known as Hooke’s law.

Hooke’s law of elasticity states that the amount of deformation (compress

or expand) in term of displacement of mass attached by the spring from its

equilibrium position is linearly proportional to the force. Mathematically,

it can be expressed as 3.1.

F = −Kx (3.1)

Where, K is spring constant , F is the force exert on the material and x is

the distance of compressed and stretched from the equilibrium position or

the position of mass at zero force. Another important physical law called

Newton’s second law of motion which states that force on mass is directly


x = 0

M Se n s i tive

Ax is

M Se n s i tiv e

Ax is

x < 0 -v e

M Se n s i tive

Ax is

x > 0 + ve

Figure 3.1: Three cases of single axis Accelerometer,A single axis accelerom-

eter mass suspended by the string, displacement of mass from it position of

equilibrium measure the acceleration exert by the force,+ve and −ve indi-

cates expansion and compression

proportional to acceleration, if object mass remains constant. Mathemati-

cally it can be expressed as 3.2.

F = ma (3.2)

Where, m is the mass and constant of proportionality, ’a’ is the acceleration

caused by the force. Hence, by comparing these two physical laws governed

the relation of acceleration caused by the force in terms of displacement of

mass as (a = -k x/m). If we able to observe the displacement of mass con-

nected to a spring then acceleration can be measured. There are various

methods of sensing change in displacement which also defines the type of

sensor. One of popular type of sensing change is by the capacitance [2].

This physically phenomenon is fabricated in small housing for measure ac-

celeration caused by the force. In order to measure the acceleration in 3

axes the same single axis accelerometer can be duplicated at tri axes or-

thogonal positions, where each axis is 90 degree a part from its neighboring

axis as shown in figure 3.2.

Typical applications where the accelerometer could be utilized are incli-


Figure 3.2: The configuration of tri Axes Accelerometer using single axis

accelerometers.

nation or tilt angle measurement, sensing amount of linear acceleration

of moving object (Inertial Measurement) and Vibration Measurement etc

(James, 2007). In all these applications the most commonly use is the in-

clination measurement that also being utilized in the low cost orientation

measurements of human joint posse as discussed in (Satoshi KURATA,

1999).

3.1.2 Gyroscope

Gyroscope is also known as rate sensor which is used to measure the an-

gular rate. There are different types of gyros available such as laser gyro,

spinning motor gyro, and piezoelectric based vibrating mass gyro[6].In all

these types, the vibrating mass is being widely used in iMEMS due to

small in size and low power consumption. Therefore, it is ideally suited for

human motion analysis. In today’s integrated MEMS technology measure

angular rate by means of Coriolis Acceleration (John Geen, 2008). The

term coriolis acceleration had been introduced by the French mathemati-

cian Gaspard G. de Coriolis, 1792-1843. It can be described as, consider

person is standing on the rotating platform, near at the center as depicted


in figure 3. The relative tangential speed of person on the rotating disk

relative to the ground as shown by the blue arrow will increase in magni-

tude when person moves from center of rotating platform toward the outer

edge of the platform. This rate of change in tangential velocity is known

as Coriolis acceleration (John Geen, 2008).

Figure 3.3: Practical example of Coriolis AccelerationMovement of person

on the rotating platform exhibit coriolis acceleration which is proportional

to angular velocity of the frame (John Geen, 2008).

In practical the deflection of frame containing resonating mass produce

coriolis acceleration.The spring is used to attach the resonant mass with the

substrate. It is also called Coriolis sense fringes (John Geen, 2008)as shown

in figure 4. These fringes are capacitively coupled to sense the displacement

of the frame in reaction of the force generated by the resonating mass.

Figure 3.4: Inner structure of Analog Devices iMEMs Gyro-

scope (John Geen, 2008).

The force caused by the coriolis acceleration is also known as coriolis force.


By using Newton’s second law of motion the coriolis acceleration as the

function of angular velocity can be expressed as 3.3.

A = 2 ∗ V ∗W (3.3)

Where A is coriolis acceleration, V is mass speed and W is the angular

velocity of the rotating platform. Using this equation the angular velocity

of rotating platform can be obtained by the coriolis acceleration. Same as

3d Accelerometer the 3 axes gyroscope configuration can be designed.

3.1.3 Magnetometer

Magnetometer sensor is used to measures the strength of magnetic field.

There are many type of magnetometer sensor available, such as mechani-

cal, Fluxgate, magneto inductive, magneto resistive and Hall Effect mag-

netometer (Everett, 1995). Among all these the Hall effect and magneto

resistive are popular due to easy of sensing , low power consumption and

small in size. A single axis Hall Effect sensor work on the principle called

Hall Effect. It states that, if the electric current flows through the conduct-

ing plate in a magnetic field then the magnetic flux exert a transverse force

on mobile charges which tends to generate the potential across the plates

called Hall voltage as shown in figure 3.5. The amplitude of hall voltage

gives measures of the magnetic field strength (Magnet.fsu, 2008). While

in case of Magneto resistive the change in resistance due to the magnetic

field is used in wheat stone bridge configuration to measure magnetic field

strength (Stefan Hübschmann, 1996)as shown in figure 3.5.

One advantage of hall method is the inherent ability to directly sense the

strength of magnetic field. Mathematically hall voltage is given by 3.4.

Vh =(− IB

d)

ne(3.4)


I (current)

V = VH (Hall Voltage)

V =0

B (Magnetic Flux)

w

d

R

R

R

R

BV v

Magnetic

Resistance

B( Magnetic Flux)

Figure 3.5: Principle of Hall Effect and Magneto resistive Magnetometer

Where Vh, Hall voltage, I , biased current through the electric plate, n is

the charge density and e is charge of electron. The 2D orthogonal configu-

ration of single magnetic sensor can be used to measure the heading or the

angle from magnetic north. This configuration is also known as magnetic

compass. Heading measurement or angle from magnetic north is simply

obtained by the trigonometric relation between two axis magnetic strength

as given by 3.5.

θ = arctan

(

By

Bx

)

(3.5)

Where,θ- Heading angle. By - Magnetic Field strength at y. Bx- Magnetic

Field Strength at x.

The following terms are associated with the magnetic sensor. "Declination

(D)" is the angular difference between the heading of true north and mag-

netic north. "Inclination (I)" is the angle above or below the horizontal.

"Total strength (T)" or Magnitude of vector magnetic field that measure

of magnetic field strength nearby (nationalatlas, 2008).

Magnetometer is common for heading measurement using the Earth’s mag-

netic field, especially for navigation of ship, car and unmanned aircraft etc.

The same property of magnetometer has been utilized with accelerometer

for the 3D attitude estimation as presented in (Egziabher, 2008).


3.1.4 Bend Sensor

It is the unique sensor that is used to measure the bend angles. It utilizes

the material deformation properties that produce change in resistance, at

the time of sensor bends (Sensors, 2008). The typical resistance based

bend sensor is shown in figure 3.6. As shown that it gives the resistance

of 10Kohm in un-flexed state and in bend state the resistance increases

gradually. It can attains the max resistance in range of 30-40Kohm at the

90 deg.

Figure 3.6: Flexible bend Sensor (Sensors, 2008)

Typical use of such sensor can be seen in virtual gloves, where it uses to

measure the bend of human hand fingers. However, it requires additional

signal conditioning circuitry and calibration for measuring change in resis-

tant proportional to the bend angle.

3.2 Performance Evaluation of Angle Sensor 36

3.2 Performance Evaluation of Angle Sensor

The Performance measurement is critical and necessary step in sensor eval-

uation. It gives the idea of how to use these sensors properly and accurately

in our application. It also provides evident result based on which further

enhancement and modification can be possible.

3.2.1 Hardware Setup

The data acquisition hardware is constructed to measure the performance

of accelerometer, gyroscope, and magnetometer for the application of an

angle measurement. It is comprises on a single AT90CAN128 Atmel u

controller, power circuitry and several sensors connectors as shown in fig-

ure 3.7. In general all sensors are operated on SPI (Serial Peripheral Inter-

face) protocol, but each of them has its own sets of command and protocol

configuration through which it can be configured and calibrated. This con-

figuration makes them hard to program using single controller. Despite the

main controller only have a single SPI (Serial Peripheral interface) port,

several virtual SPI ports has been created programmatically. As presented

in [ref] on single SPI interface more than one device can be operated in

master slave configuration but for fast and reliable data acquisition each

sensor is programmed through dedicated its single SPI ports as shown in

figure 3.7.

3.2.2 Inclination Angle using IMU

The ADIS16350 Inertial Measurement unit (Inertial Measurement Unit)

that contains tri axes accelerometer, gyro and temperature in a single pack-

age were tested as shown in 3.8.


USART 0

USART 1

AT90CAN128

u Controller

SPI 2

SPI 1

ADC 1

ADIS16350

IMU

(3 Accel, 3 Gyro)

HM 55B

Magnetic sensor

Bend Sensor

Figure 3.7: Data Acquisition Hardware,Composed of single microcontroller

with multiple SPI and ADC port for Inertial, magnetic and bend sensor,

two USART for RX,TX data communication.

In performance tests the IMU is rotated (0deg - 90deg and 90deg - 0deg)about

X axis (roll). The Inclination angle formed by the X-Y plane from the Earth

surface has observed through the use of accelerometer and Gyroscope. In

case of accelerometer measurement, the change in earth gravity vector sense

by the tri axis accelerometer is used for inclination angle. Therefore, un-

wanted linear and angular acceleration has to be digitally low passed filtered

using either standard Butterworth low pass filter or the normal average fil-

ter. Before filtering the measurement data the bias factor must be obtained

using standard calibration. These steps must be followed for smooth and

accurate measurement. The measurement data has recorded by the sensor

are fully calibrated and filtered from unwanted acceleration component as

depicted in first graph of figure 3.9.

As shown in figure 3.9, the rotation about the x axis only affects the y-

z gravitational component as function of trigonometric sine. Thus, the

simple mathematical relation as indicated in 3.6 can be used to measure


Figure 3.8: Analog ADIS16350 Inertial Measurement Unit, Composed of

tri Axes accelerometer, Gyro and temperature sensor

the inclination angle.

θ = arcsin

(

YAccel + bias

9.8

)

(3.6)

Where, θ - Inclination angle roll. YAccel - Low pass filtered y component of

acceleration vector. Bias - Calibrated bias factor.

The corresponding inclination angle obtained by the measurement is de-

picted in second graph of figure 3.9. It has been verified by the use of

fourth order curve fitting polynomial as indicated as the green line on the

graph. It represents the ideal response that can be used as a reference for

performance measurement. As can be seen in figure 3.9, the fluctuation

due to additional linear acceleration was occurred during 130 -135 sample

which limit the use of single accelerometer for an accurate angle measure-

ment.

Under the same condition, the IMU gyroscope was tested. The calibrated

data acquired by the tri axes is recorded as shown in figure 3.10. As can

be clearly seen in figure 3.10, how much the noise and bias drift is domi-

nant on the real measurement. It can be compensate by obtaining proper


0 50 100 150 200 250 300−2

0

2

4

6

8

10Tri Axes Acceleration (m/s2)

T Sample

Acc

eler

atio

n (m

/sec

2 )

0 50 100 150 200 250 300−20

0

20

40

60

80

100Inclination Measurement (

T Sample

Ang

le (

deg)

Accel XAccel YAccel Z

Inclination angle Curve Fitting

Figure 3.9: Inclination Measurement using IMU Tri Axes Accelerometer,

First Graph indicates the vector component, Second Graph is the measured

of corresponding inclination angle

bias factor through calibration. In another way, it could be minimized at

start by enabling self-test function as described in ADIS1625 calibration

procedure (DEVICES, 2008).

However, this problem cannot be limit to zero. As the gyroscope give the

angular rate which can be integrated to obtain the angular displacement.

In same measurement condition, when IMU was rotated about x axis. The

integrated angular displacement along each axis is computed using discrete

integration as shown in figure 3.11. The left most graph represent the roll

angles, which indicates the rotation about the X axis (roll). It is also an

inclination angle in this case. While the other graphs shows the rotation

about y (pitch) and z (yaw). The result of continuous short time integra-

tion due to drift is also indicated as the increase in pitch and yaw angles.


0 50 100 150 200 250 300−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6Tri Axes Gyro (deg/s)

T Sample

Ang

ular

vel

ocity

deg

/sec

Gyro XGyro YGyro Z

Figure 3.10: Inclination Measurement using of IMU Tri Axes Gyros, Ob-

served angular rate of each axis of gyro vector

Another problem associated in orientation measurement using tri axis gyro

is the measure of angle without aiding. Therefore, initial reference must

be known whenever they require for the orientation measurement. One

advantage of using gyro is high operational bandwidth which is essentially

required for high speed motion sequences which cannot be obtained with

the use of accelerometer and magnetometer.

3.2.3 Heading Angle using Magnetometer

In magnetometer performance test the two HM55B Magnetic compass are

used to measure complete earth magnetic field vector. These two magnetic

compasses are orthogonally positioned to cover complete 3D magnetic field.

These sensors are subjected to measure the Earth’s magnetic field vector


0 100 200 300−20

0

20

40

60

80

100Roll Angle using X Gyro rate

T Sample

Rol

l Ang

le (

deg)

0 100 200 300−2

0

2

4

6

8

10

12

14

16

18Pitch Angle using X Gyro rate

T Sample

Pitc

h A

ngle

(de

g)

0 100 200 300−18

−16

−14

−12

−10

−8

−6

−4

−2

0

2Yaw Angle using Z Gyro rate

T Sample

Yaw

Ang

le (

deg)

Figure 3.11: Integrated tri axis Gyro rotations, Roll, Pitch and Yaw Angles

but it is also sensitive to the other magnetic disturbances nearby. These

disturbances are caused by the interference of additional magnetic fields.

They are mainly categories as hard iron and soft iron disturbances. The

ferromagnetic material in surrounding causes hard iron or static distur-

bances. It can be reduced through calibration. While the soft disturbances

or dynamic distortion is occurred by the presence of Electromagnetic wave

that cannot be easily compensated. Therefore, calibration must be the

necessary measure of an accurate measurement. 3.12.

The heading angle has been observed by the test. During the test, tri axis

magnetic sensor has rotated clockwise and anti clockwise about z axis in

such a way that z axis remains aligned to the normal of the Earth surface.

In this case, heading angle could be obtained by the x, y component of

magnetic vector. The measurement data has recorded by the experiment

as shown in figure 3.12. The first graphs in figure 11 shows the behavior of


0 100 200 300 400 500 600

−50

0

50

Tri Axes Magnetometer (uT)

T Sample

Mag

netic

Fie

ld u

T

Magno XMagno YMagno Z

0 100 200 300 400 500 600−200

−100

0

100

200Heading (Magnetic North)

T sample

Ang

le (

deg)

Heading Angle

−20 −15 −10 −5 0 5 10 15 20−20

−10

0

10

20Magnetoic Loop (uT)

X Magnetic Field uT

Y M

agne

tic F

ield

uT

Magnotometer XY loop

Magnetic Disturbance

Ideal response

Figure 3.12: Heading Measurement using Tri Axes Magnetometer, Top

graph is the measurement of magnetic vector, middle graph is heading

angle output and bottom is the magnetic loop calibration

magnetic field vector. While, the corresponding heading measurements are

depicted in the second of figure 3.12. The third graph of figure 11 which is

mainly used for the calibration. It shows that how much the environment

is suitable for heading angle measurement. As we know the strength of

magnetic field is equally spread everywhere. In ideal case when there is no

influence of magnetic disturbance. The magnetic loop should be appeared

in the shape of circle whose center at origin. However, due to the presence

of magnetic disturbances, it appeared in the shape of ellipse whose center

is also shifted as shown in figure 3.12.

The Performance test of each sensor concludes that the single sensor could

not be enough for an accurate orientation measurement. Therefore, sensor


fusion based estimation algorithm is necessary to overcome the affect of

noises presented in each sensor, as discussed in following section.

Chapter 4

3D Orientation Measurement

Using Inertial And Magnetic

Sensor

All the advancement made in MEMS technologies enables micro machined

accelerometer and gyroscopes suitable for various application that includes

analysis of Human movement through recording (Eric Foxlin, 2004), Imag-

ing Platform stabilization control using counter rotation (Marcelo C. Al-

grain, 1993), Airborne Attitude control (Marmion, 2006), inertial naviga-

tion (David H. Titterton, 2004), mixed and augment reality (Eric R. Bach

and McGhee, 2003) and detecting levels of activity for rehabilitation of pa-

tients (REBELLO, 2004; Peter H. Veltink, 1996). In all these application,

they are mainly uses for orientation estimation. There are some limitations

still present due to substantial noise in sensors.

The orientation can be estimated by integrating the tri axis angular rate.

The MEMS Gyroscope can accurately be used to measure the angular rate

45

that can give the measure of orientation. However, a relatively small offset

error due to temperature effect, improper bias factor and additional mea-

surement noise may introduce large integration errors. Another problem of

using gyroscope is the measurement without aiding. Thus, initial position

must be known as aiding.

A tri axis linear accelerometer measures the sum of linear acceleration,

gravity and measurement noise. In absence of acceleration component as

in most case of human motion, the gravity vector can be used to measure

the inclination angle in Earth Fixed coordinate as presented in (Luinge and

Veltink, 2004). This property of accelerometer can be applied to compen-

sate the gyro drift for orientation measurement. Based on this feature the

gyroscope and accelerometer measurement has been fused to estimate ori-

entation of human segment using Kalman filter (Demoz Gebre-Egziabher

and Powell, 1998). The results indicate that they can only reduce the gyro

drift in 2DOF.

Since the accelerometer cannot be used to detect the rotation about ver-

tical axis (yaw). Thus, the yaw rotation must be included for complete

3d orientation. This problem has been resolved by the use of Magne-

tometer. It measures the heading angle from magnetic north. This prop-

erty of magnetometer has utilized together with the fusion of gyroscope

and accelerometer to measure complete 3D orientation (Eric R. Bach and

McGhee, 2003; Ashutosh Saxena and ebastien Ourselin, 2004; Eric R. Bach-

mann, 2001; Daniel Roetenberg and Veltink, 2007b). A gyro free orienta-

tion measurement method has also proposed (Demox Gebre-Egziabher and

W.Parkinson, 1998) to keep the system cost low but its performance indi-

cates that it can only be used to track slow motion but it is suitable for

navigational application. Still the affect of magnetic disturbances nearby

can affect the accuracy of overall orientation measurement, especially in

4.1 Sensor Fusion 46

heading angles ψ as discribed earlier. However, in control environment

such as in laboratory it can be characterized prior to the measurement,

which can be eliminated using calibration. Another method has also pro-

posed in which the GPS (Global positioning System) signals has been fused

instead of magnetometer to improve the accuracy of orientation estimation

as presented in (Demoz Gebre-Egziabher and Powell, 1998).The inherent

noise and error in GPS can affect the measurement but its performance

are considerable much better than magnetometer, especially in presence of

magnetic disturbance. However, it limits the use of orientation measure-

ment only in the Earth focused applications.

In this study, the various types of Kalman based fusion methods for ori-

entation measurement (Angelo M, 2006; Xiaoping Yun and B.McGhee,

2003; Kraft, 2003; Tatsuya Harada and Sato, 2003, 2007) have been stud-

ied. Based on the real-time performance the quaternion based standard

Extended Kalman filter technique is implemented for orientation measure-

ment. This chapter is focused on the design of quaternion based estimation

filter and its performance evaluation in the presence of static magnetic dis-

turbances and measurement noises.sd

4.1 Sensor Fusion

A Standard discrete non- linear Extended Kalman Filter was designed and

implemented to estimate an accurate orientation measurement. It com-

bines the complete three tri axis sensors information including tri axis ac-

celerometer, Gyroscope and magnetometer to estimate quaternion vector

of complete 3D orientation. The structure of estimation filter procedure

is shown in figure 4.1.In practical, the discrete Kalman Filter is used to

combine the feature of multi-sensors to estimate an accurate quantity of


interest from the noisy measurement. As in this case, the noise present in

gyro as in form of drift is compensated with the fusion of accelerometer

and magnetometer.

LPF

LPF

Accelerometer

Model

Gyroscope

Model

Magnetometer

Model

Quaternion Based

Extended Kalman Filter

Rk Qk

Accelerometer

Signal As

Magnetometer

Signal Ms

Gyroscope

Signal Gs

Gs

Mf

ZA

ZM

ZG

QM

QuaternionOutput

System

Cov

Measurement

Cov

QO

Figure 4.1: Structure of Sensor Fusion, Accelerometer As , Magnetometer

Ms and Gyroscope GS are combined to estimate quaternion based orien-

tation, ZA and ZM are the calibrated sensor outputs respectively, Qm is

measurement quaternion obtained by ZA and ZM , the estimated error in

Qm is corrected with the fusion of calibrated gyro ZG using Kalman filter

based on noise covariance Rk and Qk.

In the applied fusion technique, the gravitational component Gs is obtained

by the Accelerometer signal As using low pass filtered. It reduces the af-

fect of additional acceleration component on inclination measurement. In

similar way, the Magnetometer Ms signal is also filtered to minimized the

immediate effect of high frequency magnetic disturbance due to mobile

signals, wireless signals in surrounding. After applying pre signal filtering,

the output signal GS and Ms are further processed through their respective

measurement model. It measures the calibrated measurements ZA and ZM .

Further, the ZA and ZM are combined together using kinematic of the earth

fixed coordinate system to form the measurement quaternion vector Qm.

The measurement quaternion contains the information about an absolute

reference in Earth fixed coordinate system. This information of an absolute


reference is fused with gyro measurement by the use of quaternion system

state model as illustrated in preceding sections. This approach maintains

high dynamic response and increase accuracy of complete orientation mea-

surement. The Kalman filter combine all the sensors measurements and

estimate the correct measurement using system Rk and measurement noise

covariance Qk. These co variances are computed separately using sensor

calibration procedure. It uses noise covariance to predict the correct esti-

mate by reducing estimated state error. Hence, the noise free quaternion

vector output is estimated.

The sensors state model are the important factor to designed and implement

the estimation algorithm. Therefore, the following sensors measurement

models are discussed as follows.

4.1.1 Accelerometer Measurement Model

Accelerometer signal can be modeled as the sum of linear acceleration a,

magnitude of the gravity g and measurement white noise w as shown in

equation 4.1.

As(t) = a(t) − g(t) − w(t) (4.1)

In above equation, the amount of linear acceleration a(t) component can be

further modeled as the first ordered - low pass filtered white noise process

as expressed in equation 4.2.

a(t) = caa(t− 1) + v(t) (4.2)

Where, ca - low pass cut off frequency.

It can be experimentally determined for pre filtering process.


4.1.2 Gyroscope Measurement Model

Gyroscope signal Gs can be modeled as the sum of angular velocity ω, offset

b and white noise w as indicated in equation 4.3.

Gs(t) = ω(t) + b(t) + w(t) (4.3)

The fluctuation of offset bt due to variation in temperature and misalign-

ment in gyro measurement can be described as the Markov process, driven

by the Gaussian white noise as shown in 4.4 .

b(t) = b(t− 1) + w(t) (4.4)

This factor must be obtained experimentally and subtracted from the read

gyroscope signal, Otherwise it increases as the amount of unwanted inte-

gration offset.

4.1.3 Magnetometer Measurement Model

Magnetometer signal can be modeled as the sum of the Earth magnetic

field me, Magnetic disturbance md and the uncorrelated Gaussian white

noise w as shown in 4.5.

Ms(t) = me(t) +md(t) + w(t) (4.5)

The Magnetic Disturbances md can be modeled as shown in equation 4.6.

md(t) = cdmd(t− 1) + w(t) (4.6)

Where, cd - constant range (0-1). w(t) is Gaussian white noise with stan-

dard with standard deviation δd. The presence of magnetic disturbance


is detected by the magnitude of the Earth magnetic field vector.It can be

computed using 4.7.

MA =√

M2x +M2

y +M2z (4.7)

It indicates the existence of magnetic disturbances in surrounding. In case

of zero magnetic disturbances this magnitude of vector MA would be equal

to 1. Otherwise the measure of magnetic dip angle would be needed to

estimate the amount of magnetic disturbance in the Earth fixed coordinate.

The magnetic dip angle, also known as magnetic inclination.It is the mea-

sure of angle between magnetic field direction and surface of the Earth as

shown in figure 4.2. It gradually decreases from 90 deg at magnetic poles

to 0 deg at magnetic equator.

Figure 4.2: Magnetic inclination Angle,H is the projection of the F magnetic

field vector on XY Plane of Earth surface, I is dip angle by the surface.

Mathematically, it can be written as (3-8).

θ = arctan

(

Mz√

M2x +M2

y

)

(4.8)

4.2 Filter Structure 51

As shown in the formula the change in magnetic flux directly affect the

dip angle measurement. Thus, the amount of magnetic disturbance is ob-

tained by the comparison of measured dip angle with the ideal value of

dip angle at the location. Usually the disturbance is represented as the

vector of standard deviation ?d that is used with actual measurement for

the correction of heading angle measurement.

4.2 Filter Structure

The Standard Non Linear Extended Kalman Filter is implemented for sens-

ing complete 3D orientation. It combines the absolute reference information

as the observation quaternion Qm with high bandwidth angular rate of gy-

roscope ZG using quaternion based state model as shown in figure 4.3.

The inner data flow of quaternion based Extendend Kalman filter for ori-

entation estimation is recursively in nature as shown in figure 4.3 by the

feedback arrows. In subsequent iterations it reduces propagation of state

error present in posterior state quaternion by the inherent statistical pred-

ication computations and prior quaternion .Finally this approach reduced

the state estimation error.

4.2.1 Quaternion System State Model

The state space representation of quaternion based continues time non lin-

ear stochastic system can be written as.

q′(t) =1

2U(ωZG(t)) + v(t) (4.9)

Where, q′(t)-Quaternion state vector of dimension 1×4,[

q0 q1 q2 q3

]

.

U - Conversion matrix of dimension 4 × 3.


State Prediction Covariance

State Prediction Covariance

Residual Covariance

Residual Covariance

Filter Gain

1)

Filter Gain

1)

Filter Gain

Update State Covariance

Update State Covariance

P(k|k)

Q(k)

R(k)

w(k)

z(k)

v(k)

u(k)

q(k)

Measurement Model

)

Measurement Model

)

Quaternion State Model

)

Quaternion State Model

)

Evaluation of Jacobians

Evaluation of Jacobians

q(k)

Figure 4.3: Process Flow of Quaternion Based Extended Kalman Filter,

P (k|k) is state covariance, Q(k) is measurement Covariance, R(k) is Sys-

tem Covariance, w(k) represents Measurement Noise, z(k) represents obser-

vations equivalent to measurement quaternion Qm , v(k) represents addi-

tional System Noise, u(k) is gyro rate input equivalent to ZG and q(k) is the

quaternion state vector to be estimated by the predictive process of extended

Kalman filter, q, z are state and measurement prediction respectively and

v(k + 1) measurement residual.


ωZG(t)- Gyro Angular rate vector of dimension 1 × 3,[

ω0 ω1 ω2

]

.

v(t)- System noise or gyroscope noise model.

Equation 4.9 is also known as dynamic equation or the planet equation. In

case of orientation measurement, it is called quaternion state space model.

The term system is used for the orientation sensor module included tri axis

accelerometer, gyroscope and magnetometer.

Conversion Matrix which transform the gyroscope angular rate into the

equivalent quaternion state as shown in ??.

U =1

||q||

−q1 −q2 −q3

q0 −q3 −q2

q3 q0 −q1

−q2 q1 q0

, ||q|| =√

qq′ (4.10)

The discretization or integration of 4.9 gives the quaternion based Kalman

state equation as shown in 4.11 and 4.12.

q(k + 1) = f [k, q(k) + u(k)] + v(k)] (4.11)

q(k + 1) = qk +1

2U(T ∗ ωZG(t)) + v(t) (4.12)

4.12 is known as discrete quaternion state equation. It serves two purposes,

first convert the gyro angular rate into 3D orientation by integration and

second incorporate prior state to the posterior state. Where T in 4.12 is

the sampling period at which the data is being acquired by the orientation

sensor module.


The system noise v(k)in quaternion based model is noise present in gyro-

scope. It can be model as the sequence of zero mean white Gaussian noise

with covariance as indicated 4.39

E[v(k)v′(k)] = Q(k) (4.13)

4.2.2 Measurement Model

The output of the system or the observation quaternion obtained by the

accelerometer and magnetometer can be represent as

z(t) = q(t) + w(t) (4.14)

Where, z(t)- Measurement Quaternion Qm. q(t)- Quaternion state vector.

w(t)- Measurement Noise, Measurement quaternion noise model.

The discretization of 4.14 provides the measurement equation for discrete

extended Kalman filter as 4.15.

z(k + 1) = h[k, q(k + 1), w(k)] (4.15)

z(k) = q(k) + w(k) (4.16)

z(k) in 4.15is the measurement quaternion Qm as stated earlier. The

measurement quaternion Qm is obtained by converting sensor frame ac-

celerometer and magnetometer measurements into Earth Fixed coordinate

frame of reference.

In Theory, the Earth fixed Frame is known as World Frame as shown in

figure 4.4. It represent as the 3D right hand frame attached to the cen-

ter of the Earth Globe in such a way that Z points towards Geomagnetic


Z (Geographic North pole)

X (East)Y (Prime Meridian)

Magnetic North

Magnetic South

The Earth

Figure 4.4: Orientation in Earth Fixed Frame

north, Y points towards East and X towards prime meridian. It is de-

noted as superscript W. It will act as the reference frame for the complete

orientation of body frame sensor module. The body frame or the sensor

frame is the frame attached to the orientation sensor module as discussed

in experimental setup. It is denoted as superscript s.

The orientation of sensor frame S relative to Earth fixed frame W can be

calculated by fusing gravity based inclination using accelerometer gs and

magnetic field based heading using Magnetometer ms, according to world

frame. These measurements in sensor coordinate frame can be expressed

in Earth fixed coordinate frame with the use of Rotation matrix as shown

in 4.17 and 4.18.

gw = Rws g

s (4.17)

mw = Rwsm

s (4.18)

Where, gw- Gravity vector in Earth Fixed Frame. gs- Gravity vector in

Sensor Frame. mw- Magnetic field vector in Earth Fixed Frame. ms-


Magnetic field vector in Sensor Frame. Rws - Rotational Matrix, transform

Sensor frame relative to Earth Fixed Frame.

Rws Rotation matrix represents rotation transformation of sensor frame with

respect to Earth Fixed frame. It is represented by the Euler angular matrix,

roll(φ), pitch (θ) and yaw (ψ) as shown in 4.19.

Rws = RZY X =

cos θ cosψ − cosφ sinψ + sinψ sin θ cosψ sinφ sinψ + cosφ sin θ cosψ

cos θ sinψ cosφ cosψ + sinφ sin θ sinψ − sinφ cosψ + cosφ sin θ sinψ

− sin θ sinφ cos θ cosφ cos θ

(4.19)

Equations 4.17 and 4.18 are non linear equation which can be solved

simultaneously to obtain Euler angles. There are two ways for solving

these equation one is using Gauss Newton Method and other is by the

use of Geographical constrain, such as field of gravity vector and magnetic

north in the Earth fixed Coordinate as presented in (Tatsuya Harada

and Sato, 2003).According to the geographical constrain approache ,the

following gravity and magnetic field vector in Earth fixed frame can be

assumed as 4.20 and 4.21.

gw =[

0 0 −1]T

(4.20)

mw =[

a 0 b]T

(4.21)

Where, a and b are the normalized horizontal and vertical component of

Earth magnetic field respectively. Now, the rotation in sensor frame can

also be expressed as 4.22 and 4.23.

gs = Rswg

w (4.22)


ms = Rswm

w (4.23)

Where, Rsw = Rw

sT , superscript T indicates Transpose.

By applying the gravitation geographical constrain of the Earth Fixed co-

ordinate in 4.22, the gs can be obtained as 4.24

gs =[

sin θ − sinφ cos θ − cosφ cos θ]T

(4.24)

Using equation 4.22, the Euler roll φ, and pitch θ can be written as 4.25

and 4.26 respectively.

roll − φ = Atan2(

−gsysign(cos θ),−gszsign(cos θ))

(4.25)

pitch− θ = Atan2(

gsx,√

(gsy)2 + (gsz)

2)

(4.26)

Since, the gravity based geographical constrain can only give Euler roll

and pitch angles using accelerometer measurements. As stated earlier for

complete 3d orientation the yaw angle is required .This problem has been

resolved by the use of magnetometer measurement.

The rotation matrix Rws shown in eq:RotationMatrixZYX can be further

split in terms of roll-φ, pitch-θ and yaw-ψ rotation matrix as 4.27.

Rws = RZ(ψ)Ry(θ)Rx(φ) (4.27)

Using equation 4.27, the equation 4.18 can be simplified as 4.28.


mw = RZ(ψ)Ry(θ)Rx(φ)ms (4.28)

Equation 4.28 can be further simplified as 4.29.

RZ(ψ)Tmw = Ry(θ)Rx(φ)ms (4.29)

By applying geographical magnetic field constrains in equation 4.29, the

yaw angle can be derived as 4.30.

yaw − ψ = Atan2(r(2), r(1)) (4.30)

Where, r − vector = Ry(θ)Rx(φ)ms.

This approach is followed to obtain low frequency Euler rotations by the fu-

sion of accelerometer and magnetometer measurements. In order to obtain

the measurement quaternion Qm, [Qm0Qm1Qm2Qm3]T (z(k)) the follow-

ing equation 4.31− 4.32 are used. These equations convert the Euler angle

into the quaternion based vector. It is necessary to use these equations in-

stead of Euler angles vector that exhibits the properties of singularity which

can only be eliminated with the use of equations 4.31− 4.32.

Qm0 = cosφ

2cos

θ

2cos

ψ

2+ sin

φ

2sin

θ

2sin

ψ

2(4.31)

Qm1 = sinφ

2cos

θ

2cos

ψ

2+ cos

φ

2sin

θ

2sin

ψ

2(4.32)

Qm2 = cosφ

2sin

θ

2cos

ψ

2+ sin

φ

2cos

θ

2sin

ψ

2(4.33)


Qm3 = cosφ

2cos

θ

2sin

ψ

2+ sin

φ

2sin

θ

2cos

ψ

2(4.34)

The Measurement Noise w(k)in Quaternion is represented by the noise

present in Quaternion Measurement vector using accelerometer and mag-

netometer. It can be assumed as zero mean white Gaussian noise with

covariance as 4.40.

E[w(k)w′(k)] = R(k) (4.35)

4.2.3 Update State Covariance

In linear Kalman Filter the update state covariance was independent to the

state estimation. However in non linear Extended Kalman filter the state

and covariance update are simultaneously computed under the Kalman it-

erative process. It is occurred due to the linearization of non Linear Kalman

state equation which introduces the term evaluation of jacobian. n. The

evaluation of jacobian is the part of update covariance computation as

shown in figure 3 kalman flow diagram. It includes the mathematical com-

putation of jacobian of state and measurement equations. Mathematically,

it can be expressed as 4.36 and 4.37.

F (k) =∂q(k)

∂qq=q(k|k) (4.36)

H(k + 1) =∂q(k)

∂q q=q(k+1|k)

(4.37)

By applying the mathematical jacobian on 4.12 and 4.16 the F(k) and H(k)

can be evaluated respectively. It is the essential step in extended kalman fil-

ter for updating state covariance. The intermediate steps of updating state


covariance as shown in figure 4.3, such as state predication covariance and

residual covariance that incorporate the system Q(k) and measurement

R(k) noise covariance for estimating the filter gain. The weight of the filter

gain is mainly reduced the error in current state estimation. Consequently

the inherent Kalman structure able to filtered additional process and mea-

surement noises.

As can be seen in figure 4.3 the filter gain which reduces state estimation

error depends on the accurate knowledge of system Qk and measurement

Rknoise covariance input. Therefore, these covariances are also known as

error covariance. They utilized to predict the state predication and residual

error covariance. Finally, the filter gain is obtained that uses to estimate

state and covariance update. The updated state covariance that combine

filter gain, residual covariance and prior covariance for the next iteration

can be written as 4.38.

P (k + 1|k + 1) = P (k + 1|k) −W (k + 1)S(K + 1)W ′(k + 1) (4.38)

4.2.4 Error Covariance

As stated earlier section the knowledge of error covariance Qk and Rk

are the important factor to compensate the affect of external noises. In

quaternion based orientation included accelerometer, magnetometer and

gyroscope, they can be modeled as zero mean white Gaussian noise. The

system error covariance Q(k) that describes the process noise w(k) or the

noise present in gyroscope can be experimentally obtained by the measure

of error propagation in gyroscope. It can be represented as the diagonal

matrix shown in 4.39.

4.3 Experiement 61

Qk =

Qk0 0 0 0

0 Qk1 0 0

0 0 Qk2 0

0 0 0 Qk3

(4.39)

In similar way, the noise present in accelerometer and magnetometer mea-

surement as the function of v(k) can be described by the measurement error

covariance R(k). It represent as the 4×4 diagonal matrix as indicated 4.40

whose diagonal elements are experimentally computed by the measure of

error propagation in accelerometer and magnetometer.

Rk =

Rk0 0 0 0

0 Rk1 0 0

0 0 Rk2 0

0 0 0 Rk3

(4.40)

4.3 Experiement

The purpose of the experiment was to investigate the stability and perfor-

mance of developed sensor module using Quaternion Kalman Filter in pres-

ence of environment disturbances and noises. For the experiment in various

condition, the low cost 3D Orientation sensor module was constructed as

indicated in figure 4.5. As can be seen in Figure the 3D orientation sensor

is comprises on 2 layers of PCB that are stacked together in one package

called module. The top layer of PCB (Printed Circuit Board) contains

two HM55B Hitachi Magnetic Compasses that are orthogonally positioned

with the help of two small PCBs. Each small PCB holding HM55B sensor

is mounted orthogonally on the top layer PCB as shown in figure. This

configuration measure the complete 3d Magnetic Field Vector.

4.3 Experiement 62

Figure 4.5: 3D Orientation Sensor Module, Module is depicted in various

views with their corresponding sensor reference frame is indicated by the

x(Red) ,y(Black) and Z( Green) at the bottom corner

In similar way, the Analog Inertial Measurement Unit AD16350 included

tri axis accelerometer and gyroscope is routed on the bottom layer PCB as

square box indicated in figure 5. Both sensors are digital and produces their

outputs in SPI Protocol standard. They are connected with the acquisition

board containing ATMEAG128 low cost controller as stated earlier in last

chapter. The data acquisition controller was operating at the 14.756 MHZ

clock frequency. At this frequency controller provide baud error free se-

rial data stream to the computer. The controller performed two functions.

First, acquired the data by the sensors using conventional I/O ports and

bundle them into the form of packet before transmitting to the host com-

puter. It takes fractions of mille seconds. Second maintain the constant

throughput or the data samples for transmission that is essential in case

4.3 Experiement 63

of real-time discrete Kalman Filter implementation. Finally, the acquired

data packet was being transmitted to the computer at the sample rate of

0.136 sec. A simply formula can also be used to compute the sample rate

as shown in 4.41.

ts = ta + tt (4.41)

Where, ts - Packet sampling time. ta - Sensor data acquisition time. tt -

Data sampling time.

The complete performance measurement experiment is divided into the

following cases.

1. When the orientation sensor is at rest.

2. When the orientation sensor is influenced by an external magnetic

disturbance.

Before starting an experiment, the bias factor and gain multiplier of each

sensor has been computed by the calibration as presented in Appendix A.

In calibration, each sensors including accelerometer, gyroscope and magne-

tometer measurements is observed for 8 -10min in quasi static case.Finally

the observation is then utilize to obtain the element of error covariance for

discreet Kalman filtering.These valuse were not changed during the exper-

iment.

4.4 Results 64

Pitch

θ

Roll

Ф

Yaw

ψ

Orientation Sensor

Figure 4.6: Orientation sensor rotation about Roll φ , Pitch θ and Yaw ψ,

anticlockwise about an each axis is +ve deg and clockwise is -ve deg.

4.4 Results

4.4.1 Case 0: Influence of Environment Noises

In case one the developed orientation sensor module was at rest or static

in position about the period of 30 min. Approximately 13050 total no of

samples has been taken by the experiment as shown in figure4.7. As can be

seen in observed measurement graph still the impact of bias drift is more

dominate in gyro measurement as compared to the accelerometer and mag-

netometer measurements. Therefore the Quaternion based Kalman filter

was required. Finally the 3D orientation result obtained by the Kalman

estimation and without Kalman estimation is indicated in figure4.8.

In figure 4.8, it can be seen that by the use of Kalman estimator the impact

of noise in 3d orientation has been considerably reduced up to 0.3 deg. In

another way the accuracy can also be measured with the use statistical

quantity such as standard deviation that measure the dispersion of set of

values as listed in table 4.1. In comparison, less the standard deviation

more the system is accurate and stable. It proved that the accuracy of 3d

orientation measurement with the use of Kalman filter is much better than

without kalman filter.

4.4 Results 65

0 2000 4000 6000 8000 10000 12000 14000−5

0

5

10

15Tri Axes Acceleration (m/s2)

T Sample

Acc

eler

atio

n m

/sec

2

0 2000 4000 6000 8000 10000 12000 14000−0.04

−0.02

0

0.02

0.04Tri Axes Gyro (rad/s)

T Sample

Ang

ular

vel

ocity

rad

/sec

0 2000 4000 6000 8000 10000 12000 14000−100

−50

0

50Tri Axes Magneto (H)

T Sample

H u

Tes

la


Gyro XGyro YGyro Z


Figure 4.7: 30min Tri Axis Acceleration, Gryroscope and Magnetometer

observation.

Further this can also be confirmed with the help of statistical histogram

of Euler angle with and without kalman filter as shown in figure4.9. In

Figure4.9 the red line is indicated histogram fit Gaussian distribution of

the sets of Euler angles. It shows that if the set of data or measurements is

more converge to the mean value and width of Gaussian which is defined

by the twice of standard deviation is narrow than the system is more sta-

ble and accurate. It can be clearly seen that by the use of Kalman Filter

S.No Standard Deviation σ Without Kalman Filter With Kalman Filter

0 Roll φ 0.1595 0.0973

1 Pitch θ 0.1963 0.1462

2 Yaw ψ 2.6744 1.1482

Table 4.1: Standard deviation of Euler angles with and without Kalman

estimation.

4.4 Results 66

0 2000 4000 6000 8000 10000 12000 14000−2

−1

0

1Roll Angle

T Sample time

Deg

Without Kalman FilterWith Kalman Filter

0 2000 4000 6000 8000 10000 12000 14000−1

0

1

2Pitch Angle

T Sample time

Deg


0 2000 4000 6000 8000 10000 12000 1400080

90

100

110Yaw Angle

T Sample time

Deg


Figure 4.8: 3D Euler orientation measurement at rest.

the measurement has been more converge to their mean as compared to

without Kalman filter. Moreover, the outliers are also less compared to the

histogram without Kalman filtering in which the population of measure-

ment is not completely bounded within fitted Gaussian distribution curve.

It can also be noticed that the impact of white noise in orientation sensor

module is Gaussian in nature as stated earlier.

4.4.2 Case 1: Influence of the external magnetic dis-

turbance

In this case the performance of Kalman Filter is observed under the affect

of additional magnetic disturbance by the use of Ferromagnetic electric

iron. During the experiment the orientation sensor was placed on the table

4.4 Results 67

−1.5 −1 −0.5 0 0.5 10

1000

2000Histogram of Roll Angle Without Kalman Estimator

−0.6 −0.4 −0.2 0 0.2 0.40

200

400

600Hist of Roll Angle With Kalman Estimator

−1 −0.5 0 0.5 1 1.50

500

1000

1500Histogram of Pitch Angle Without Kalman Estimator

−1 −0.5 0 0.5 10

200

400

600Hist of Pitch Angle With Kalman Estimator

80 85 90 95 100 1050

200

400

600Histogram of Yaw Angle Without Kalman Estimator

88 90 92 94 96 980

200

400Hist of Yaw Angle With Kalman Estimator

Figure 4.9: Histogram comparison of 3D orientation with and without

Kalman estimator.

and the electric iron was moved near to the sensor from the x direction

to the z direction and after few seconds it has been moved away by the

sensor directly from the z direction. The duration of magnetic disturbance

is indicated by two arrow ended horizontal line shown in figure 4.10.It can

be seen that the two X and Z component of magnetic vector has been more

affected by the magnetic disturbance instead of Y component. Using same

measurement the kalman filter has been applied which finally reduced the

impact of disturbances comparable to the measurement without Kalman

filter as shown in figure 4.11. By the statistical view still the standard

deviation in case of kalman filtered measurement is less than the standard

deviation without kalman filter as indicated in table 4.2.

In figure 4.12 the statistical boxplot of yaw measurement with and without

kalman is shown. It clearly shows that the outliers in yaw measurement

4.4 Results 68

0 50 100 150 200 250−90

−80

−70

−60

−50

−40

−30

−20

−10

0Tri Axes Magneto (H)

T Sample

Nor

mal

ized

H


Magnetic Disturbance

Figure 4.10: Tri Axis Magnetic Vector disturbance due to ferromagnetic

electric iron.

0 50 100 150 200 250105

110

115

120

125

130

135

140Yaw Angle

T Sample time

Deg


Additional Magnetic Disturbance

Figure 4.11: Yaw angle measurement with and without Kalman filter under

magnetic disturbance.

4.4 Results 69

without Kalman estimation are much higher than the yaw measurement

with Kalman estimator. Further the median value in case of Kalman fil-

tered data is also less than the median value without Kalman estimation

which is slightly shifted toward the upper quartile. Finally, it proves that

the influence of magnetic disturbance can affect the accuracy of the angle

measurement with and without Kalman Filter but still the use of Kalman

filter is providing smooth and stable angle measurement.

S.No Standard Deviation σ Without Kalman Filter With Kalman Filter

0 Yaw ψ 9.3612 6.8292

Table 4.2: Standard Deviation of Yaw Angle with and without Kalman

Estimation.

Yaw Without Kalman Yaw With Kalman0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Val

ues

Column Number

Euler Yaw Box Plot

Figure 4.12: BoxPlot of Yaw measurement with and without Kalman.

4.5 Discussion 70

4.5 Discussion

The experimental results show that the use of kalman estimation is neces-

sary in inertial and magnetic measurement for complete and accurate 3d

orientation measurement. The various aspects of noise and magnetic dis-

turbance has been analyzed by the experiment which proves that the 3d

orientation measurement using Kalman estimation are accurate up to 0.2

deg along each orientation axis. It has also been noticed by the experi-

ment. The accuracy of 3d orientation measurement is depends on several

factor such as motion speed, sampling rate, dynamic magnetic disturbance

and environment temperature. However, the increase in motion speed and

amount of magnetic disturbance can more affect the accuracy comparable

to other aspects. Despite that the proposed estimation algorithm is also

tested in real-time that will be required for capturing human arm motion

sequences. It will be discussed further in next sections.

In case when the sensor is placed near to the ferromagnetic material that

can affect the accuracy of the 3D orientation measurement the proper cali-

bration would be necessary. It enhances the performance of the estimation

filter. It has also been observed that the increase in the ferromagnetic ma-

terial and magnetic disturbance can significantly impact on the yaw angle

measurement and also decrease the efficiency of estimation filter. Further

when the orientation module is placed in car for navigational application

then the amount of unnecessary acceleration must be take into account to

enhance the accuracy of 3d orientation measurement because the increase

in linear acceleration can result of wrong inclination measurement. By con-

sidering this effect of acceleration the low pass pre filter is implemented in

the algorithm.

4.5 Discussion 71

The proposed estimation algorithm can be applied to any combination of

tri axis accelerometer, gyroscope and magnetometer for complete 3D orien-

tation measurement. In practical, the specification of the sensors should be

known such as noise and bias drift. By the advancement in MEMS technol-

ogy it could be possible to achieve less drift Gyros in future. It means that

the more accuracy could be achieved by the same approach. Each sensor

is temperature dependent; therefore the affect of temperature drift can be

modeled further to improve the accuracy. Besides the accuracy can also

be enhance by the implementation of adaptive estimation filter. Although

it would require more computation but this approach can significantly re-

duced the effect of external noises and disturbance.

Finally the proposed filter is more efficient to obtain the 3d orientation for

the robotic application which mainly requires the accuracy of 1 deg. Its ca-

pability can be further enhanced by the fusion of other angle measurement

method.

Chapter 5

Kinematic Analysis & Modeling

of WorkPartner Manipulator

The recent development and advancement in the field of robotics are gener-

alizing the concept of robots in human world. The robot is "The intelligent

mechanical structure that supposes to function autonomously" ??. The

intelligent means the robot does not do its task in a repetitive way as the

machine currently being used in the Automation industry; instead it is

the machine that utilizes its computer (brain) and mechanical structure to

solve real world problems without recourse of human operator. In practical,

the concept of such robots is far from the present technology require more

advancement. Thus, the concept of semi autonomous robot is still popular.

Based on these facts the field service robot, called WorkPartner ?? is de-

veloped. It is the semi autonomous robot that can function autonomously

and non-autonomously. It depends on the nature of the task. It is designed

to help human in their routinely tasks in urban environment. The Mechan-

ical Structural of WorkPartner Robot is hybrid as shown in figure 5.1 . It

73

allows the robot to be more efficient for rigorous outdoor applications. As

can be seen in figure 5.1, the complete WP can be viewed into two parts,

front part called "body" which is human like and back part called "panel"

which is car like. The panel of the WP robot included four active wheels

(walking and rolling) and power sub-system (combustion engine and batter-

ies) increase the locomotion on uneven terrain and fulfill power requirement

during the task. While the body part included Perception system and two

human arms like manipulators, enable the robot to sense dynamic work

environment and utilize manipulator to accomplish various tasks such as

cutting trees, grabbing thing etc.

According to the context of the thesis only the WorkPartner manipulator

is involved. Thus, this chapter is focused on the kinematic analysis of WP

Manipulator followed by its mechanical specification. Finally, the motion

has been analyzed and discussed using developed 3d kinematic simulator

of the WorkPartner Robot.

Perception System

Manipulator

Power System

Active Wheel Joint

Figure 5.1: 3D CAD model of WorkPartner Robot

5.1 Specification of WorkPartner Manipulator 74

5.1 Specification of WorkPartner Manipula-

tor

Any Robot which has human like manipulator comprising motion in 7DOF,

can do the task that human arm can do. Considering this fact the human-

like manipulator of the WorkPartner Robot has been designed. However

its motion is limited in 5DOF. Despite that it is functioned efficiently in

the work environment. The motion of WorkPartner manipulator in 5DOF

is achieved by the combination of 3 revolute joint. As can be seen in figure

5.2 the 5Dof WorkPartner manipulator includes 2 DOF shoulder joint, 1

DOF Elbow and 2 DOF Wrist. . While the end effector is not completely

as the human hand comprises five fingers but it can be gripper or cutter

etc. Its choice is mainly depends up on the application.

Shoulder

Inclination (pitch)Shoulder

Tilt (Yaw)

Elbow

Inclination (pitch)

Wrist

Inclination (pitch)

Wrist

Rotation (Roll)

2DOF

1DOF

2DOF

Figure 5.2: Joint Specification of WorkPartner Manipulator

Each 1DOF joint motion of the manipulator is controlled by DC servo

motor with tailor made planetary gears ??.It enables the manipulator to

attain position in 5DOF work envelope with the accuracy of 1deg. The

brake system is also installed in joint which can be used to manually repo-

5.2 Kinematic Modeling of WP Manipulator 75

sition the manipulator during uncontrolled state.

5.2 Kinematic Modeling of WP Manipulator

Denavit Hartenberg notation is simplest way of modeling mechanical ma-

nipulator for kinematic analysis. It is mainly use to define the mechanics

of rigid body or chain of rigid body in terms of 1 joint variable and 3 fixed

link parameters. For the revolute or rotary joint the θ is the joint variable

that defines movement of attached segment in work space with additional

three fixed link parameter known link length ai, link twist αi and link offset

di. In case of prismatic joint the link offset di is the joint variable and other

three are fixed. Further detail can be found in ??. As the single sided WP

manipulator is only comprised on three revolute joints. Therefore, it can

be modeled using DH notation as shown in table 5.1.

S.NO Link(i) αi ai di θi Angle range

1 Shoulder(Tilt) L1 00 1 0 θ1 −450 − 450

2 Shoulder(Inc) L2 −900 0 0 θ2 −900 − 900

3 Elbow(Inc) L3 00 4 0 θ3 00 − 1400

4 Wrist(Inc) L4 00 4 0 θ4 −900 − 900

5 Wrist(Rot) L5 −900 0 0 θ5 −900 − 900

6 End Effector L6 00 1 0 θ6 −

Table 5.1: Denavit Hartenberg representation of WP Manipulator

The Table 5.1 has been used to generate the Matlab compatible simulation

model using Robotic Toolbox as shown in figure 5.3. It has been used to

analyze the kinematic motion of WP in Workspace. Although, it is an

ideal kinematic model of WP Manipulator that cover complete workspace

as the sphere whose radius is equivalent to the length of the Manipulator.


In Practical, the joint motion of the WP Manipulator is not completely

cover 360 deg as indicated in angle range column of DH table. Therefore,

the workenvelop of the WorkParnter can be anaylzed using this simulation

model by limiting the motion of each joint according to its angle range

aspects.

−4−3

−2−1

01

2

−4

−2

0

2−4

−3

−2

−1

0

1

2

XY

Z

XY

Z

Figure 5.3: DH Kinematic Model of WP Manipulator

5.2.1 WorkEnvelop

The Work envelope of any manipulator defines the area in which it can

be moved easily. Further, it shows the reach ability of the manipulator.

In case of 5 DOF WorkPartner Manipulator, where each revolute joint

motion is not completely covered 360 deg. Thus, the Work enevelope has

been analyzed using DH kinematic simulation model in matlab. Based

on that the work envelop is showed in 3d kinematic model of the WP in

figure below. This 3d model is implemented for real-time emulation and

translation of human motion into the robot manipulator equivalent motion


as explained further in next chapter.

S_inc max 90o

S_inc min -90o

S_inc 0o

E_inc min 0o

E_inc max 140o

w_

inc

0o

w_

inc

ma

x 9

0o

S_tilt max 45o

S_tilt min -45o

S_tilt 0o

Figure 5.4: WorkEnvelop of Single WP Manipulator,a)Left Side view, b)

Top viewShoulder motion range is -45 deg-45 deg in tilt and -90 deg-90

deg in inclination, Elbow motion is in 0 deg-140 deg inclination followed

by shoulder motion, Wrist motion is -90 deg-90 deg in both inclination and

rotation followed by the shoulder and elbow motion.

As can be seen in figure 5.4 the 2DOF shoulder motion is indicated as

the purple quarter sphere, whose radius is equivalent to the length of the

manipulator. It signifies that the complete motion of WP robotic arm can

be moved within it. While the light green pie in left side of figure indicates

that when the shoulder joint will have reached its maximum inclination

then the Elbow motion follows the green pie. In similar way, when the

elbow motion has reached its maximum inclination then the wrist motion

follow the small reddish pie. Further, the span of each joint motion is also

indicated as line with values.

References

Aarne Halme, Ilkka Leppänen, J.S.S.Y. and Kettunen, I.

(2003). WorkPartner: Interactive Human-Like Service Robot for Out-

door Applications. In The International Journal of Robotics Research.

Helsinki University of Technology, Automation Technology Labora-

tory.

Angelo M, S. (2006). Quaternion-Based Extended Kalman Filter

for Determining Orientation by inertial and Magnetic Sensing. In

Transactions on Biomedical Engineering. IEEE.

Ashutosh Saxena, Gaurav Gupta, V.G. and ebastien

Ourselin (2004). In Use Parameter Estimation of Inertial Sensors

by Detecting Multilevel Quasi-static States. Department of Electrical

Engineering, Stanford University, USA.

ASIMO, H. (2008). BibTeX — Honda, The ASIMO Humanaiod

Robot. [Online; accessed 5th-June-2008].

URL: http: // asimo. honda. com

Daniel Roetenberg, C.T.B. and Veltink, P.H. (2007a). Es-

timating body segment orientation by applying inertial and magnetic

sensing near ferromagnetic materials. In IEEE TRANSACTIONS OF

NEURAL SYSTEMS AND REHABILITATION ENGINEERING.

REFERENCES 79

Daniel Roetenberg, P.J.S. and Veltink, P.H. (2007b). Ambu-

latory Position and Orientation Tracking Fusing Magnetic and Inertial

Sensing. In TRANSACTIONS ON BIOMEDICAL ENGINEERING.

IEEE.

David H. Titterton, J.L.W. (2004). Inertial Navigation. In Strap-

down Inertial Navigation Technology - 2nd Edition. The institute of

Electrical Engineers.

Demox Gebre-Egziabher, Gabriel H. Elkaim, J.P. and

W.Parkinson, B. (1998). A Gyro-Free Quaternion-Based Attitude

Determination System Sitable for Implementation using Low Cose

Sensors. Department of Aeronautics and Astronautics, Stanford Uni-

versity.

Demoz Gebre-Egziabher, R.C.H. and Powell, J.D. (1998). A

Low-Cost GPSfinertial Attitude Heading Reference System (AHRS)

for General Aviation Applications. Department of Aeronautics and

Astronautics, Stanford University.

DEVICES, A. (2008). BibTeX — ANALOG, Datasheet of

ADIS16530. [Online; accessed 15th-Feburary-2008].

URL: http: // www. analog. com/ UploadedFiles/ Data\ _Sheets/

ADIS16350\ _16355. pdf

Egziabher, Demoz Gebre; Elkaim, G.H.P.J.D.P.B.W. (2008).

A Gyro-Free Quaternion-Based Attitude Determination System Suit-

able for Implementation Using Low Cost Sensors. Department of Aero-

nautics and Astronautics, Stanford University.

Eric Foxlin, Michael Harrington, Y.A. (2004). Miniature 6-

DOF inertial system for tracking HMDs. In Helmet and Head-Mounted

Displays III, pages 1–15. InterSense, Inc, 73 Second Ave., Burlington,

MA 01803.

REFERENCES 80

Eric R. Bach, X.Y. and McGhee, R.B. (2003). Sourceless Track-

ing of Human Posture Using Small Inertia & Magnetic Sensors. In

Computational Intelligence in Robotics and Automation. The MOVES

Institute Naval Postgraduate School, Monterey CA.

Eric R. Bachmann, Robert B, M.X.Y.M.J. (2001). Inertial and

Magnetic Posture Tracking for Inserting Humans Into Networked Vir-

tual Environments. MOVES Institute, Monterey CA.

Everett, H. (1995). Sensors For Mobile Robots Theory & Applica-

tion. Massachusetts : A K Peters, Ltd.

Gypsy6, A. (2008). BibTeX — Inition, Gypsy6 Torso. [Online;

accessed 5th-June-2008].

URL: http: // www. inition. com/ inition/ product. php? URL\

_= product_ mocaptrack_ animazoo_ gypsy&SubCatID\ _= 0

James, D. (2007). Accelerometer Design & Applications. Analog

Devices.

URL: http://www.analog.com/Analog_Root/static/library/techArticles/mems/sensor971

James F, O.Brien Robert, E.B.J.G.J.B.J. and Hodgins, K.

(2000). Automatic Joint Parameter Estimation from Magnetic Motion

Capture Data.

John Geen, D.K. (2008). New iMEMS R© Angular-Rate-Sensing Gy-

roscope. ADI Micromachined Products Division.

Johnston, O. Thomas, F. (1981). The Illusion of Life: Disney

Animation, Disney Editions,. Disney.

Karim Abdel-Malek, Jingzhou Yang, W.Y. (2003). Human

Performance Measures: Mathematics.

REFERENCES 81

Kraft, E. (2003). A Quaternion-based Unscented Kalman Filter

for Orientation Tracking. ISF, Physikalisches Institute, University of

Bonn.

L. Herda, P. Fua, R.P.R.B. and Thalmann, D. (2000). Skeleton-

Based Motion Capture for Robust Reconstruction of Human Motion.

Computer Graphics Lab (LIG) EPFL CH-1015 Lausanne, Switzerland.

leonardo-da-vinci biography (2008). BibTeX — LDVB, Vitru-

vian man and Da Vinci surgical anatomy drawings. [Online; accessed

5th-June-2008].

URL: http: // www. leonardo-da-vinci-biography. com/

vitruvian-man-anatomy. html

Luinge, H.J. and Veltink, P.H. (2004). Inclination Measurement

of Human Movement Using a 3-D Accelerometer With Autocalibration.

In TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITA-

TION ENGINEERING. IEEE.

Magnet.fsu (2008). BibTeX — magnet.fsu, Interactive Hall Effect.

[Online; accessed 15th-Feburary-2008].

URL: http: // www. magnet. fsu. edu/ education/ tutorials/

java/ halleffect/ index. html

Marcelo C. Algrain, J.Q. (1993). Accelerometer Based Line-

of-Sight Stabilization Approach for Pointing and Tracking Systems.

In Conference on Control Applications. InterSense, Inc, University of

Nebraska-Lincoln.

Marjan A. Admiraal, M.J.K. and Gielen, S.C. (2004). Modeling

Kinematics and Dynamics of Human Arm Movements. Motor Control,

8:312–338.

Marmion, M. (2006). Airborne attitude estimation using a Kalman

REFERENCES 82

filter. The University Centre of Svalbard, University of Nebraska-

Lincoln.

Martin, R.B. (1999). A Genealogy of Biomechanics. Presidential

Lecture presented at the 23rd Annual Conference of the American

Society of Biomechanics.

URL: http://www.asbweb.org/html/history_mission/genealogy/genealogy.htm

Miller, N., J.O.K.M.M.M. (2004). Motion capture from inertial

sensing for untethered humanoid teleoperation. Humanoid Robots,

IEEE/RAS International Conference, 2:547– 565.

Moreno, R.A. (2007). Cognitive Robotics.

nationalatlas (2008). BibTeX — nationalatlas.gov, Geomagnetism:

The Magnetic Field of the Earth. [Online; accessed 15th-Feburary-

2008].

URL: http: // nationalatlas. gov/ articles/ geology/ a\

_geomag. html

Peter H. Veltink, Hans B.J. Bussmann,

W.d.V.W.L.J.M.R.C.V.L. (1996). Detection of Static and Dynamic

Activities Using Uniaxial Accelerometers. In TRANSACTIONS ON

REHABILITATION ENGINEERING, volume 4. IEEE.

REBELLO, K.J. (2004). Applications of MEMS in Surgery. In Pro-

ceedings of the IEEE, volume 92. IEEE.

Rong Zhu, Z.Z. (2004). A Real-Time Articulated Human Motion

Tracking Using Tri-Axis Inertial/Magnetic Sensors Package. IEEE

TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITA-

TION ENGINEERING, 12:295–301.

Satoshi KURATA, Masaaki MAKZKAWA, H.K.A.T.R.T.

(1999). JOINT MOTION MONITORING BY ACCELEROMETERS

REFERENCES 83

SET AT BOTH NEAR SIDES AROUND THE JOINT. In Proceedings

of the 20th Annual Znternational Conference of the ZEEE Engineering

in Medicine and Biology Society.

Schneck, D.J. (2000). The Biomedical Engineering Handbook. vol-

ume 1. CRC Press & IEEE Press.

Sensors, F.B. (2008). BibTeX — Images SI Inc, Sensors Manufac-

turer. [Online; accessed 15th-Feburary-2008].

URL: http: // www. imagesco. com/ sensors/ flex-sensor. html

Stefan Hübschmann, M.S. (1996). Magnetoresistive Sensors Prin-

ciples of Operation and Applications. ZETEX.

URL: http://www.zetex.com/3.0/appnotes/apps/an20.pdf

Stilson, T. (1996). Accelerometer (Analog Devices ADXL50).

URL: http://www.cs.princeton.edu/courses/archive/fall07/cos436/sensors/node9.html

Suomela, J. Halme, A. (2004). Human robot interaction - case

WorkPartner. Intelligent Robots and Systems, 2004. (IROS 2004).

Proceedings, 4:3327– 3332.

Tatsuya Harada, Hiroto Uchnio, T.M. and Sato, T. (2003).

Portable Orientation Estimation Device Based on Accelerometers,

Magnetometers and Gyroscope Sensors for Sensor Network. IEEE,

University of Tokoyo.

Tatsuya Harada, Hiroto Uchnio, T.M. and Sato, T. (2007).

Development of Wireless Networked Tiny Orientation Device for

Wearable Motion Capture and Measurement of Walking Around, Walk-

ing Up and Down, and Jumping Tasks. IEEE, University of Tokoyo.

TKK (2008). BibTeX — TKK, The Automation and System Tech-

nology Dept. [Online; accessed 5th-June-2008].

REFERENCES 84

URL: http: // automation. tkk. fi/ WorkPartner:

WorkPartnerRobot. jpg

Toulmin, S. (2001). History of the Study of Locomotion.

URL: http://www.univie.ac.at/cga/history/renaissance.html

W. Maurel, D. Thalmann, P.H.P.B.P.G.P.K.N.M.T. (2004). A

Biomechanical Musculoskeletal Model of Human Upper Limb for Dy-

namic Simulation. Computer Graphics Laboratory, Swiss Federal In-

stitute of Technolog, EPFL, CH-1015 Lausanne, Switzerland.

Wes Trager, G.S.O. (1999). A Practical Approach to Motion

Capture: Acclaim’s optical motion capture system. [Online; accessed

15th-Feburary-2008].

URL: ’http: // www. siggraph. org/ education/ materials/

HyperGraph/ animation/ character\ %_ animation/ motion\

_capture/ motion\ _optical. htm

Xiaoping Yun, Mariano Lizarraga, E.R.B. and B.McGhee,

R. (2003). An Improved Quaternion-Based Kalman Filter for Real-

Time Tracking of Rigid Body Orientation. In Conference on Intelligent

Robots and Systems. IEEE, Naval Postgraduate School, Monterey,CA

93943.

Xsens (2008). BibTeX — Xsens, Inertial & Magnetic Based Motion

Sensor. [Online; accessed 5th-June-2008].

URL: http: // www. moven. com/ en/ home_ moven. php

Yasuyoshi YOKOKOHJI, Yuki KITAOKA, T.Y. (2002). Motion

Capture from Demonstrator Viewpoint and Its Application to Robot

Teaching. In Proceedings of the 2002 IEEE lntemational Conference

on Robotics 8 Automation.

Yu, T. (2007). Motion Capture (MoCAP) and Motion Data Related

Technologies. UNC Department of Computer Science.

Appendix A

Calibration of Orientation Sensor

Module

The calibration is the process of eliminating or reducing the effect of bias

offset in sensors measurements over the range of all continuous values. In

case of 3d orientation sensor module included tri axis accelerometer, gy-

roscope and magnetometer, it is required for three purposes. First is to

reduce the amount of unnecessary fluctuation (Bias offset) from sensors

measurement. Second is to measure the amount of white noise present in

each sensor as the element of noise covariance matrices for Kalman estima-

tor. Third is to convert the raw measurement in to the corresponding SI

unit standards such as m/s2 for acceleration,rad/sec for gyro measurement

and utesla for magnetometer.

The two Orientation Sensors Modules have been used for the calibration.

One is developed by Xsens technologies and other is self made low cost as

shown in figure A.1. The Xsens orientation sensor module is comprised

on embedded controller and tri axis accelerometer, gyroscope and magne-


tometer in small package. It directly provides serial raw data stream (not

calibrated) of each sensor in the form of packet on the serial port. Its

sampling rate and the serial baud can be adjusted using sensor configura-

tion command and testing software. While the self developed orientation

module is built by the low cost IMU ADIS16350 and two orthogonally

positioned HM55B Magnetic compass. It requires an additional data ac-

quisition hardware that obtains the raw data stream by the sensors and

bundle them into the form of packet before serial transmission. Both ori-

entation module are using tri axis inertial (accelerometer & gyroscope) and

Magnetometer which are subjected of various inherent and environmental

noise.

Figure A.1: Orientation sensor Modules,Left one module is developed by

Xsens Technologies, Right one is Self developed using ADIS16350 (Analog

Devices) and HM55B (Hitachi).

As observed by the experiment, the fluctuation in MEMS sensor (Ac-

celerometer and Gyroscope) is caused by the change in operating temper-

ature, misalignment of micro electro mechanical mass (drift) and environ-

ment noises. Thus, these sensors must be calibrated before using in actual

system. The following experiments are performed to obtain the amount of

bias offset, which must be reduced or mathematically subtracted later from

the real measurements.


A.1 Experiment 1: Determination of Qk and

Rk

In this experiment self developed orientation sensor are tested in static case.

That means sensor is not in motion and placed on the work table. Thus,

the measured data stream should be ideally constant. This experiment is

carried out for the period of 30 min because statistically it is enough to

analysis the properties of all sensors. The total no of 13052 data samples

has been recorded by the self made orientation sensor module at the sample

rate of 0.137909 sec, which is equivalent to approximately 7 samples/sec

.By the use of this experiment the influence of environment noise and drift

is computed as the element of error covariance Rk and Qk which can be

used in Quaternion based Extended Kalman filter to filter the impact of

environmental noises.

The experiment was conducted on Date: 15 June 2008 and Time: 5:00

- 5:30 at average vector temperature [9.2194 (X), 7.0413(Y), 6.8282(Z)]

Celsius. The data was being acquired at the rate of 7 sample/ sec. The

observed measurement during 30 min of experiment is shown in figure A.2.

As the covariance is the statistical measure. Therefore, using 30min ob-

servation the histogram of each sensor is plotted as shown in figure 2. It

clearly confirms that the noise is in Gaussian in nature whose mean is at

the peak of Gaussian fitted curve and variance defines the spread of mea-

surement about mean. The variance is actually measure to analyze the

accuracy of each sensor. If any sensor whose variance is near to zero or

in other word the spread of measurement as indicated in histogram is less,

then the sensor is good and accurate comparable to the one whose variance

is greater. Mathematically the mean and the variance can be expressed as

A.1, A.2 respectively.


0 2000 4000 6000 8000 10000 12000 14000−10

0

10

20Tri Axes Acceleration (g)

T Sample

Acc

eler

atio

n m

/sec

2


0 2000 4000 6000 8000 10000 12000 14000−0.05

0

0.05Tri Axes Gyro (rad/s)

T SampleAng

ular

vel

ocity

rad

/sec

Gyro XGyro YGyro Z

0 2000 4000 6000 8000 10000 12000 14000−1

−0.5

0

0.5Tri Axes Magneto (uT)

T Sample

Nor

mal

ized

uT

esla


Figure A.2: Tri Axis Accelerometer,Gyroscope and Magnetometer mea-

surement

x = E[x] =

∑N

n=1xnN

(A.1)

Where, x = E[x] , Mean value of x.

xn, set of x measurement data.

N , Total no of samples in x.

varx = E[x− x] =∑N

n=1(xn − x) (A.2)

Using equation A.1, A.2 the mean and variance of each sensor is computed

as shown in table 1:

As observed that each sensor values is not constant as it should be, There-

fore the mean value is used as the bias offset which reduce the impact of bias


−1 −0.5 00

1000

2000

3000Histogram of Acceleration X

Acceleration m/sec2

N S

ampl

es

−1 −0.5 0 0.50

1000

2000

3000Histogram of Acceleration Y

Acceleration m/sec2

N S

ampl

es

9 9.5 10 10.50

1000

2000

3000Histogram of Acceleration Z

Acceleration m/sec2

N S

ampl

es

−0.05 0 0.050

500

1000Histogram of Gyroscope X

Angular rate rad/sec

N S

ampl

es

−0.05 0 0.050

500

1000Histogram of Gyroscope Y


N S

ampl

es

−0.05 0 0.050

500

1000Histogram of Gyroscope Z


N S

ampl

es

−0.1 0 0.10

2000

4000

6000Histogram of Magnetometer X

Magnetic Field uT

N S

ampl

es

−0.4 −0.3 −0.20

2000

4000Histogram of Magnetometer Y

Magnetic Field uT

N S

ampl

es

−1 −0.95 −0.90

1000

2000Histogram of Magnetometer Z

Magnetic Field uT

N S

ampl

es

Figure A.3: Histogram fit of Accelerometer,Gyroscope and Magnetometer.

Measurement Type Accel X Accel Y Accel Z Gyro X Gyro Y Gyro Z Magno X Magno Y Magno Z

Mean -0.2372 -0.1337 9.7872 -0.0002 -0.0068 0.0059 -0.0133 -0.2906 -0.9566

Variance 0.0025 0.0021 0.0025 0.0001 0.0001 0.0001 0.0002 0.0001 0.0000

Table A.1: Statistical Observation (Mean and Variance)


drift present in each sensor. As explained in Chapter 5, the accelerometer

and magnetometer are being used to obtain the measurement quaternion.

Using the same sets of equations the measurement quaternion is computed

by the fusion of 30min accelerometer and magnetometer measurement. The

result is plotted as the histogram of quaternion vector components shown

in figure A.4 .

0.4 0.6 0.80

100

200

300

400

500

600Histogram of q1

−0.04 −0.02 00

200

400

600

800

1000

1200Histogram of q2

−0.02 0 0.020

100

200

300

400

500

600

700

800Histogram of q3

0.7 0.8 0.9 10

100

200

300

400

500

600Histogram of q4

Figure A.4: Histogram fit of Quaternion Measurement Vector Components

Using the Variance equation the variance in quaternion measurement is

estimated as shown in table. These variances will be used as the element

of measurement covariance RK as indicated in equation.

Measurement Type Quat q0 Quat q1 Quat q2 Quat q3

Variance 2.8654e-004 7.1625e-006 4.9397e-006 2.2529e-004



Rk =

2.8654e− 004 0 0 0

0 7.1625e− 006 0 0

0 0 4.9397e− 006 0

0 0 0 2.2529e− 004

(A.3)

It is assumed that this matrix will remains constant throughout the entire

test using Quaternion based Kalman estimation.

In similar way the element of system noise covariance Qk using gyros mea-

surement is computed. First the mean of gyro is used as the bias factor to

reduce the effect of bias offset from gyros measurement, which is also known

as calibrated gyros measurement. Second by the use of discrete integration

algorithm as indicated in figure A.5, resulting quaternion observation is

measured.

Gyroscope

Sensor

Bias Correction

Gs

Bs

Gc

T

(Sampling Time)

Discrete Integration Quaternion Vector

Output

T

Figure A.5: Quaternion Observation using Discrete Integration Algorithm

Finally the result is statistical analyze as the histogram fit of Quaternion

vector component shown figure A.6. Using variance equation the spread

of quaternion measurement is estimated as listed in table. These variances

will be used as the element of System Covariance Qk.It can be noticed that

the element of system covariance is small. Thus, the Quaternion Kalman

Filter requires less time to estimate an accurate 3d orientation.


0.8 1 1.20

200

400

600

800

1000

1200Histogram of q1

−0.5 0 0.50

50

100

150

200

250

300

350

400

450Histogram of q2

−0.2 0 0.20

100

200

300

400

500

600

700Histogram of q3

−0.5 0 0.50

50

100

150

200

250

300

350

400

450

500Histogram of q4

Figure A.6: Histogram fit of Quaternion Vector Components using Gyro

Angular rate.

Measurement Type Quat q0 Quat q1 Quat q2 Quat q3

Variance 5.0878e-004 0.0084 0.0015 0.0026



Rk =

5.0878e− 004 0 0 0

0 0.0084 0 0

0 0 0.0015 0

0 0 0 0.0026

(A.4)

Note: All the measurement results are taken at the average vector temper-

ature [9.2194 (X) , 7.0413(Y), 6.8282(Z)] Celsius. The variation in tem-

perature during an experiment is also observed in each axis as shown in

figure A.7.

0 2000 4000 6000 8000 10000 12000 140003

4

5

6

7

8

9

10

11Tri Axes Temperature (C)

T Sample

C

Temperature XTemperature YTemperature ZMean Temperature XMean Temperature YMean Temperature Z

Figure A.7: Tri Axis Temperature Observation (30min)

Appendix B

Name of the 2nd appendix

This is the second appendix.

Farrukh Iqbal Sheikh - Aaltoautsys.aalto.fi/en/attach/SpaceMaster-SecondRound-07-08/Draft... ·...

Documents

Transcript of Farrukh Iqbal Sheikh - Aaltoautsys.aalto.fi/en/attach/SpaceMaster-SecondRound-07-08/Draft... ·...