International Journal of Robotics and Automation vol 2 issue 1

International Journal of

Robotics & AutomationJan - Jun 2016 IJRA

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¬ International Journal of Thermal Energy andApplications

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and Design¬ International Journal of Manufacturing and

Materials Processing¬ International Journal of Mechanical Handling and

Automation

« International Journal of Radio Frequency Design« International Journal of VLSI Design and Technology« International Journal of Embedded Systems and Emerging

Technologies« International Journal of Digital Electronics« International Journal of Digital Communication and Analog

Signals

« International Journal of Housing and Human SettlementPlanning

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Applied Mechanics

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Computer Science and Engineering « International Journal of Wireless Network Security« International Journal of Algorithms Design and Analysis« International Journal of Mobile Computing Devices« International Journal of Software Computing and Testing« International Journal of Data Structures and Algorithms

Nanotechnology« International Journal of Applied Nanotechnology« International Journal of Nanomaterials and Nanostructures« International Journals of Nanobiotechnology

« International Journal of Solid State Materials« International Journal of Optical Sciences

Physics

« International Journal of Renewable Energy and itsCommercialization

« International Journal of Environmental Chemistry« International Journal of Agrochemistry« International Journal of Prevention and Control of Industrial

Pollution

Civil Engineering« International Journal of Water Resources Engineering« International Journal of Concrete Technology« International Journal of Structural Engineering and Analysis« International Journal of Construction Engineering and

Planning

Electrical Engineering« International Journal of Analog Integrated Circuits« International Journal of Automatic Control System« International Journal of Electrical Machines & Drives« International Journal of Electrical Communication

Engineering« International Journal of Integrated Electronics Systems and

Circuits

Material Sciences and Engineering « International Journal of Energetic Materials« International Journal of Bionics and Bio-Materials« International Journal of Ceramics and Ceramic Technology« International Journal of Bio-Materials and Biomedical

Engineering

Chemistry « International Journal of Photochemistry« International Journal of Analytical and Applied Chemistry« International Journal of Green Chemistry« International Journal of Chemical and Molecular

Engineering

« International Journal of Electro Mechanics andMechanical Behaviour

« International Journal of Machine Design andManufacturing

« International Journal of Mechanical Dynamicsand Analysis

« International Journal of Fracture and damageMechanics

« International Journal of Structural Mechanicsand Finite Elements

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Biotechnology « International Journal of Industrial Biotechnology and

Biomaterials« International Journal of Plant Biotechnology« International Journal of Molecular Biotechnology« International Journal of Biochemistry and Biomolecules« International Journal of Animal Biotechnology and

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Dr. Arindam Kumar ChandaDepartment of Mechanical & Automation Engineering, G. B. Pant Govt. Engineering

College, Okhla, New Delhi, India

Dr. P. Narendra MohanUniversity College of Engineering and

Technology, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur (Andhra Pradesh),

India

Dr. V. SugumaranSchool of Mechanical Engineering and Building

Sciences, VIT University, Chennai (Tamil Nadu), India

Dr. S RamabalanDepartment of Mechanical Engineering, E. G. S. Pillay Engineering College, Nagapattinum

(Tamil Nadu), India

Dr. Ajith RameshDepartment of Mechanical Engineering,

Amrita School of Engineering, Coimbatore (Tamil Nadu), India

Akhil MuralidharanVellore Institute of Technology, Chennai

(Tamil Nadu), India

From the Editor's Desk

Dear Readers,

We would like to present, with great pleasure, the inaugural volume of a new scholarly

journal, International Journal of Robotics and Automation. This journal is part of the

Engineering Sciences, and is devoted to the scope of present Robotics and Automation

issues, from theoretical aspects to application-dependent studies and the validation of

emerging technologies.

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an emerging and increasingly vital field, now widely recognized as an integral part of scientific and

technical investigations. Its mission is to become a voice of the Mechanical Engineers community,

addressing researchers and practitioners in this area.

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awareness and know-how for the profit of mankind ranging from the academic and professional research

societies to industry practitioners in a range of topics in Robotics and Automation in general. Journals Pub

acts as a pathfinder for the scientific community to published their papers at excellently, well-time &

successfully.

International Journal of Robotics and Automation focuses on original high-quality research in the realm of

Industrial automation, Factory automation, Building automation, Robotics, Hydraulics and pneumatics,

Manufacturing automation, System integration etc.

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Automation and solutions in the area.

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It is our hope that this fine collection of articles will be a valuable resource for Mechanical readers and will

stimulate further research into the vibrant area of Robotics and Automation.

Puneet Mehrotra

Managing Director

1. Simulation of the Inverse-Kinematics for JACO Manipulator Robot Arm Salih Rashid Majeed, Klause D-Kuhnert 1

2. Review on Improving Techniques for Path Planning Process in Robot Designing Himanshu Garg 7

3. Highlights on Trajectory Optimization of Robots

S. Sharmila 12

5. Passivity Analysis of Uncertain Stochastic Neural Networks with Discrete and Distributed Time-Varying DelaysM. Syed Ali, M. Esther Rani 19

4. A Brief Account on Dynamics of Robotic ArmPriyanka Garg 15

Contents

IJRA (2016) 1–6 © JournalsPub 2016. All Rights Reserved Page 1

International Journal of Robotics and Automation Vol. 2: Issue 1

www.journalspub.com

Simulation of the Inverse-Kinematics for JACO Manipulator

Robot Arm

Salih Rashid Majeed*, Klause D-Kuhnert Institute for Real Time Learning System, Siegen University, Germany

Abstract

The inverse kinematics is a very important issue in the automation of robotic manipulator.

This paper deals with inverse kinematics (IK) in the simulation process, using “JACO”

robotic arm and studies about the implementation of IK in a very simple manner, using

BLENDER software. In this program, we simulated IK based on the relationship between the

bones and the skin in natural life, making use of some options included in the program.

Keywords: inverse kinematics, BLENDER, JACO robot arm, Morse

INTRODUCTION

The significance of inverse kinematics

(KI) in the manipulator robotic arm due to

direct effect in the movement of the arm,

made the study of this relationship very

important in the robot field. The use of

robot arm in many applications persuades

the necessity of studying the robot arm

mechanism and discovers the easy

methods to attain soft and real movement

in the robot arm. Our algorithm subjects to

one of the effective and well-known

simulation programs for implementing this

design and mechanism instead of the

classical calculations applied before.

RELATED WORK

Monzani et al. represented a method to re

target a motion from one character to a

geometrically and topologically different

one, and demonstrate how IK can conserve

the motion-captured posture while

enforcing Cartesian constraints. Their tool

has been successfully applied to video-

game projects. Extensions could be used to

define more complex mapping functions,

rather than one-to-one correspondences, to

be able to handle coupled DOFs. [1]

Grochow et al. present an IK system based

on a learned model of human poses given

a set of constraints, our system can

produce the most likely pose satisfying

those constraints, in real time training, the

model on different input data leads to

different styles of IK. The model is

represented as a probability distribution

over the space of all possible poses which

means that our IK system can generate any

pose, but prefers to the poses that are most

similar to the space of poses in the training

data, they represent the probability with a

novel model called a Scaled Gaussian

Process Latent Variable Model and their

style-based IK can replace conventional

IK, wherever it is issued in computer

animation and computer vision. [2]

Der et al. stated that their IK algorithm

provides intuitive and direct control of

non-rigid shapes similar to a conventional

IK algorithm for jointed rigid skeletons. It

manages the geometric complexity of

detailed meshes by using a reduced

deformable model that parameterizes

shape deformation with a compact set of

control parameters. [3]



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Review on Techniques for Path Planning Process in Robot

Designing

Himanshu Garg*

H. P. University, Shimla, India

Abstract

Path-planning is one of the basic and quite interesting issues in mobile robotic design since

Mobile robot path planning has a few main properties according to type of environment,

algorithm and completeness. The properties are whether it is static or dynamic, local or

global and complete or heuristic. The static path planning refers to environment which

contains no moving objects or obstacles other than a navigating robot and dynamic path

planning refers to environment which contains dynamic moving and changing object such as

moving obstacle. Meanwhile the local and global path planning depend on algorithm where

the information about the environment is a priori or not to the algorithm. If the path planning

is a global, information about the environment already known based of map, cells, grid or etc

and if the path planning is a local, the robot has no information about the environment and

robot has to sense the environment before decides to move for obstacle avoidance and

generate trajectory planning toward target. The problems in path planning and its

optimization is thus a popular research topic for many researchers across the globe, efforts

are made in past to solve this problem and quite interesting results have been seen. In the

present paper, a brief idea about the process of path planning in mobile robots with a short

review of various contributions in improvising path planning process are presented.

Keywords: path planning, optimization, algorithm, mobile robot, industrial robot,

probabilistic roadmap (PRM), genetic algorithm (GA), degree of freedom (DOF).

INTRODUCTION

Nowadays, no one can deny the

importance of robotics in our daily life. It

is a continuously expanding field because

of its wide applications in agriculture,

industry, home, surveillance, hospitals, etc.

Robots can be roughly divided into two

types based on their structure and usage,

namely mobile robots and industrial

robots. Mobile robots have the capability

to move around in their environment and

are not fixed to one physical location. In

contrast, industrial robots usually consist

of a jointed arm (multi-linked manipulator)

and gripper assembly (or end effector) that

is attached to a fixed surface. Mobile

robots have a very diversified and broad

area of application. [1]

These robots are

used in navigation, surveillance,

agriculture, cooperation, and in many other

fields as it has a larger workspace

compared to industrial robot. Coordinating

the motion of multiple mobile robots is

one of the fundamental problems in

robotics. The predominant algorithms for

coordinating teams of robots are decoupled

and prioritized, thereby avoiding

combinatorial hard planning problems

typically faced by centralized approaches.

While these methods are very efficient,

they have two major drawbacks. First, they

are incomplete, i.e. they sometimes fail to



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Highlights on Trajectory Optimization of Robots

S. Sharmila University of Madras, Tamil Nadu, India

INTRODUCTION

Industrial robots are used as a primary

means of contemporary automation due to

their potential of productivity increase and

product quality improvement. Industrial

robots are widely used to achieve the

demand of high precision and high speed

positioning. Typical examples are welding,

cutting, pick and place, and gluing. The

productivity by industrial robots can

further be maximized by path

optimization. [1]

Optimal motion can help in reducing the

machine cycle and increasing the system

utilization. In optimization, one determines

the control signals that will derive the

manipulator from starting point to ending

point while minimizing the specified

objective function, subject to the

constraints on controlling signals and the

position of the manipulator. Normally, the

optimal control problem for robotic

manipulators is complex because of the

non-linearity and complex dynamics. [2]

Trajectory optimization is a key problem

in robotics, in particular when the time

needed for optimization is critical. For

instance, in interactive scenarios with a

human, robots need to react on dynamic

changes of the environment and the

optimization time should be no more than

the temporal duration of the movement

itself so that an online sequencing is in

principle possible. Marc Toussaint [2]

proposed a probabilistic model for which

the maximum likelihood (ML) trajectory

coincides with the optimal trajectory and

which, in the LQG case, reproduces the

classical SOC solution. The algorithm then

utilizes approximate inference methods

(similar to expectation propagation) that

efficiently generalize to non-LQG systems.

We demonstrate the algorithm on a

simulated 39-DoF humanoid robot. [3]

TRAJECTORY OPTIMIZATION

Trajectory optimization is the second step

of path optimization in which a predefined

geometrical path, obtained by path

planning step, is optimized for a desired

objective. The importance of trajectory

optimization is clearly obvious from the

relationship among task execution,

productivity and energy consumption.

Research work on trajectory optimization

started in late 1960s when Stepanenko

discussed the minimum energy control

problem for manipulators. [1]

Kahn and Roth proposed the near time

optimal control for open-loop articulated

kinematic chains. The suboptimal solution

was expressed in terms of switching

curves for each of the system controls. The

constraints for the optimization problem

were the constant maximum/minimum

forces. As this method finds the

suboptimal solution, therefore, it was

computationally quite efficient compare to

the optimal control techniques. Luh et al.

proposed the optimum path planning

method using the “Method of Approximate

Programming (MAP)” in which

constraints are applied on Cartesian

velocities and accelerations. [4]

The Cartesian velocities and accelerations

bounds were identified experimentally.

This method was computationally

expensive because the transformation from



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A Brief Account on Dynamics of Robotic Arm

Priyanka Garg University of Madras, Tamil Nadu, India

INTRODUCTION

The purpose of control is to keep fixed or

to alter the dynamical behavior of a

physical system according to the wishes of

man that can be formulated in words of

performances and goals. The kinematic

models of robots describe how the motion

of the joints of a robot is related to the

motion of the rigid bodies that make up the

robot. [1]

We implicitly assumed that we could

command arbitrary joint level trajectories

and that these trajectories would be

faithfully executed by the real-world robot.

Robotic dynamics zeros in on relationship

between the forces acting on a robot

mechanism and the accelerations they

produce.

Robotic Arm

Taking a closer look at how to execute a

given joint trajectory on a robot

manipulator, we observe that most robot

manipulators are driven by electric,

hydraulic, or pneumatic actuators, which

apply torques (or forces, in the case of

linear actuators) at the joints of the robot. [3]

The dynamics of a robot manipulator

describes how the robot moves in response

to these actuator forces. For simplicity, we

would need to assume that the actuators do

not have dynamics of their own and,

hence, we can command arbitrary torques

at the joints of the robot. This allows us to

study the inherent mechanics of robot

manipulators without worrying about the

details of how the joints are actuated on a

particular robot. Other problems in robot

dynamics include:

Calculating the coefficients of the

equation of motion.

Inertia parameter identification -

estimating the inertia parameters of a

robot mechanism from measurements

of its dynamic behavior.

Ten inertia parameters are required to

define the inertia of a single rigid body

(mass, location of center of mass, and six

rotational inertia parameters); so a

dynamic model will normally contain 10

inertia parameters per body. However,

when bodies are connected together to

form a mechanism, they lose some of their

degrees of motion freedom. [5]

As a result, some of their inertia

parameters may have no effect on the

dynamic behavior of the system, or may be

indistinguishable from algebraic

combinations of other inertia parameters.

Also, if the task is to obtain a dynamic

model of a robot mechanism from a given

kinematic model and a set of

measurements of its dynamic behavior,

then the procedure is to identify the values

of a set of base inertial parameters, which

are the set of observable inertia parameters

of the system. [5]

LITERATURE SURVEY

There is a considerable range of literature

on robotics research which exploits natural

dynamics. This review divides the work

into four categories: (i) research which

exploits the robot kinematics and (i)

statics, (iii) the dynamics of the robot



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Passivity Analysis of Uncertain Stochastic Neural Networks with

Discrete and Distributed Time-Varying Delays

M. Syed Ali*, M. Esther Rani

Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu, India

Abstract This paper investigates the problem of robust passivity of uncertain stochastic neural

networks with discrete and distributed time-varying delays. To reflect the most dynamical

behaviors of the system, both parameter uncertainties and stochastic disturbance are

considered, where parameter uncertainties enter into all the system matrices, stochastic

disturbances are given in the form of a Brownian motion. By utilizing the Lyapunov

functional method, the Itô differential rule and matrix analysis techniques, we establish

sufficient criterion such that, for all admissible parameter uncertainties and stochastic

disturbances, the stochastic neural networks is robustly passive in the sense of expectation.

The delay - dependent stability condition is formulated, in which the restriction of the

derivative of the time-varying delay should be less than 1 is removed. The derived criteria are

expressed in terms of linear matrix inequalities (LMIs) that can be easily checked by using

the standard numerical software. Illustrative examples are presented to demonstrate the

effectiveness and usefulness of the proposed results.

Keywords: delayed neural networks, linear matrix inequality, lyapunov functional, parameter

uncertainty, passivity

INTRODUCTION

In recent years, neural networks have

received considerable attention due to their

extensive applications in solving some

optimization problem, associative

memory, classification of patterns, and

other areas. Time delays are unavoidably

present due to the finite switching speeds

of the amplifiers and the inherent

communication time of neurons, and its

existence will affect the stability of a

network by creating oscillatory and

instability characteristics. [13, 14]

Therefore,

the stability analysis of neural networks

with delays has recently received much

attention. [1, 10, 19, 27, 20, 23, 25, 26, 28, 32, 40]

When one models real nervous systems,

stochastic disturbance and parameter

uncertainties are unavoidable to be

considered. Because in real nervous

system, synaptic transmission is a noisy

process brought on by random fluctuation

from the release of neurotransmitters, [17]

and the connection weights of the neuron

depend on certain resistance and

capacitance values that include

uncertainties. Therefore, it is of practical

importance to study the stochastic effects

on the stability of neural networks with

parameter uncertainties, some results

related to this problem have been

published in the papers mentioned in the

references. [2, 7, 16, 18, 36–39]

In addition, parameter uncertainties can be

often encountered in real systems as well

as neural networks, due to the modelling

inaccuracies and/or changes in the


Robotics & AutomationJan - Jun 2016 IJRA

www.journalspub.com

Mechanical Engineering

Electronics and Telecommunication Chemical Engineering

Architecture

Office No-4, 1 Floor, CSC, Pocket-E,Mayur Vihar, Phase-2, New Delhi-110091, India

E-mail: [email protected]

¬ International Journal of Thermal Energy andApplications

¬ International Journal of Production Engineering¬ International Journal of Industrial Engineering

and Design¬ International Journal of Manufacturing and

Materials Processing¬ International Journal of Mechanical Handling and

Automation

« International Journal of Radio Frequency Design« International Journal of VLSI Design and Technology« International Journal of Embedded Systems and Emerging

Technologies« International Journal of Digital Electronics« International Journal of Digital Communication and Analog

Signals

« International Journal of Housing and Human SettlementPlanning

« International Journal of Architecture and InfrastructurePlanning

« International Journal of Rural and Regional PlanningDevelopment

« International Journal of Town Planning and Management

Applied Mechanics

5 more...

1 more...

2 more...2 more...

5 more...

Computer Science and Engineering « International Journal of Wireless Network Security« International Journal of Algorithms Design and Analysis« International Journal of Mobile Computing Devices« International Journal of Software Computing and Testing« International Journal of Data Structures and Algorithms

Nanotechnology« International Journal of Applied Nanotechnology« International Journal of Nanomaterials and Nanostructures« International Journals of Nanobiotechnology

« International Journal of Solid State Materials« International Journal of Optical Sciences

Physics

« International Journal of Renewable Energy and itsCommercialization

« International Journal of Environmental Chemistry« International Journal of Agrochemistry« International Journal of Prevention and Control of Industrial

Pollution

Civil Engineering« International Journal of Water Resources Engineering« International Journal of Concrete Technology« International Journal of Structural Engineering and Analysis« International Journal of Construction Engineering and

Planning

Electrical Engineering« International Journal of Analog Integrated Circuits« International Journal of Automatic Control System« International Journal of Electrical Machines & Drives« International Journal of Electrical Communication

Engineering« International Journal of Integrated Electronics Systems and

Circuits

Material Sciences and Engineering « International Journal of Energetic Materials« International Journal of Bionics and Bio-Materials« International Journal of Ceramics and Ceramic Technology« International Journal of Bio-Materials and Biomedical

Engineering

Chemistry « International Journal of Photochemistry« International Journal of Analytical and Applied Chemistry« International Journal of Green Chemistry« International Journal of Chemical and Molecular

Engineering

« International Journal of Electro Mechanics andMechanical Behaviour

« International Journal of Machine Design andManufacturing

« International Journal of Mechanical Dynamicsand Analysis

« International Journal of Fracture and damageMechanics

« International Journal of Structural Mechanicsand Finite Elements

5 more...4 more...

3 more...

Biotechnology « International Journal of Industrial Biotechnology and

Biomaterials« International Journal of Plant Biotechnology« International Journal of Molecular Biotechnology« International Journal of Biochemistry and Biomolecules« International Journal of Animal Biotechnology and

Applications3 more...

Nursing « International Journal of Immunological Nursing« International Journal of Cardiovascular Nursing« International Journal of Neurological Nursing« International Journal of Orthopedic Nursing« International Journal of Oncological Nursing

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International Journal of Robotics and Automation vol 2 issue 1

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Transcript of International Journal of Robotics and Automation vol 2 issue 1