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navigation task has been developed through the provision of a

set of sensors with which to estimate the robot position, thus

allowing autonomous inspection to be performed in specific

tank areas.

This paper is organized as follows. Section II explains the

mechanical design. Section III shows the architecture of the

inspection system. In this section, a sensorial system and a

data fusion strategy to estimate the robot position is proposed.Some experiments are carried out to test the prototype that is

discussed in Section IV. Conclusions and Future Works are

presented in the last sections.

II. MECHANICAL DESIGNThe aim is to develop a climbing robot which can take an

ultrasonic sensor to every part of the oil tank's surface and

will deal with the constraints of a real inspection work space.

A. Magnetic WheelsThe wheel structure consists of a cylindrical nylon structure

in the centre with 12 holes which surround Neodymium-Iron-

Boron magnets, all of which is enclosed in two steel plates inorder to conduct the magnetic flux through the surface it is

working on. Three rubber o-rings around the nylon structure

increase the friction between the wheel and the rolling surface

(see Figure 1). The use of small magnets in a nylon structure

permits an increase in the wheel radius with a short increase

of wheel weight. The wheel diameter is 112 mm and its

thickness is 20 mm. The final weight of a magnetic wheel in

this configuration is 1 kg. The wheel test is shown in [1].

B. Robot ConfigurationThe scheme of a tricycle has been conceived for this robot's

design. This is composed of three magnetic wheels, each of

which is triggered by a motor DC. The total mass of themechanistic structure is 12kg, to which it is necessary to add

the weight of the battery, the sensors and the electronic

components, increasing the weight to 23kg. The total size is

approximately 800x750x250 mm3. A central area is required

to install the inspection system that consists of a Cartesian

robot with a rolling ultrasonic sensor. This is composed of

three motorized linear guides (see Figure 2).

This system allows the robot to cover a high area, making

the inspection task faster and reducing its movements with

the associated reduction of the involved energy consumption.

On board power supplies are mounted above the inspection

system.

The turning mechanism in a robot is a difficult design

problem. A previous design of skid-steer type robots was

rejected because the prototype could not turn, as magnetic

wheels do not permit the necessary slip for this kind of

robots. The design proposed in this paper has solved thisproblem. The front wheel is the drive wheel, and the tricycle

configuration causes the robot follow the direction of the

drive wheel. When the robots work space is a fuel tank, it

would be very useful to be able turn around a point in order to

reach the inspection points more easily. The tricycle

configuration allows it to turn around an instantaneous center

of rotation (ICR).

C. Robot KinematicThe kinematic equation for a robot with the configuration

of tricycle is of the form shown in (1) can be found in [13].

(1)

where is the angle between the inertial system and the robot

frame, is the angle between the drive wheel and the robot

frame and l is the distance between the front and rear wheels.

Fig. 3. Kinematic Scheme for tricycle system

y

x

l

Fi . 2. CAD re resentat ion of robot desi n

=

tv

l

y

x

1

0

0

0

0

/)sin(

)cos()cos(

)cos()sin(

'

'

'

'

Fig. 1. Magnetic wheel structure

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The contol variables vt and are the linear velocity and theangular velocity of the robot respectively.

III. NAVEGATION SYSTEMA.ArchitectureThe robotic inspection system developed is based on

client/server architecture.

As Figure 3 shows, the client application is related to the

climbing robot tasks, while, the server application runs on a

remote PC. The two applications are communicated by a

wireless network (WLAN Wi-Fi IEEE 802.11g) using

TCP/IP protocol.

Fig. 4. Main modules in the Client/Server architecture for the proposed

climbing robot

Server Application

The server application is focused on configuring the

inspection task, and monitoring the robots status and

store/visualization of the thickness measures for a tank wall.

The tasks/goals of each module are:

Dataset: Store tank information such as: tank identification,

landmarks, tank plan and the thickness measures history.Mode: Set the application to work in autonomous or

teleoperation mode.

User Interface: Set up the tank details required by the

dataset (tank identification, plan, weld intersections to use as

landmarks), configuration of the robot trajectory and areas to

inspect in autonomous mode. Figure 5 shows the main panel

of the User Interface.

Fig. 5. Main panel of the User Interface where is the

robot trajectory to follow during the inspection, is the area to inspect

and each intersection between a vertical a horizontal welding( ) is n- used

as a landmark.

Broker: The main goal of this module is to interact

(command/queries) with the robot. This module also requests

information from the robot about its status (position, batteries

state) and sends the robot the inspection mode, trajectory,

landmarks, etc.

Monitor: This provides the operator with a graphical

interface with the information about the robots status.Although several applications use the robot as a server, we

have decided to make the robot a client, thus making it

possible for the inspection system to work simultaneously

with more than one robot in the future.

The server application has been developed with Visual

Studio 2005 for .NET framework using C#. This powerful

programming environment allows us to develop complex

graphical interfaces, database access and WLAN

communication in relative short amounts of time.

Client Application

The main tasks to be performed by the client applicationrunning at the robot CPU are: to check the status of the

robots batteries and to capture the sensor measures, position

estimation, trajectory generation, actuator control and server

communication. These tasks are developed by means of the

modules overviewed below:

Batteries: To check the state of the batteries. In the case of

low charge an alarm is sent to the Brain module.

Sensors: To update the sensor measures.

Brain: To communicate with the server application

(queries/status report), to estimate the robots position and to

control the generation of trajectories.

Control: To execute the motor control modelActuators: Motors (hardware)

The client application has been develop in LabVIEW. The

LabVIEW real-time graphical programming environment

simplifies the programming of complex robotics applications

by providing a high level of abstraction, and communication

with a wide variety of sensors.

B.Localization ProblemA fundamental task for an autonomous mobile robot is that

of localization, i.e., determining its location in a known

environment. Absolute localization relies on landmarks,

maps, beacons, or satellite signals to determine the robots

global position and orientation.

Dead-reckoning (open-loop estimation) is commonly used

for the estimation of position during path execution. Dead-

reckoning is often used when wheel encoders are available

for drive wheel position measurement. However, errors in

kinematic model parameters, wheel slip, or an uneven surface

may cause poor position estimates to occur. A worse scenario

is one in which poor estimates would cause a collision, thus

clogging the robots operation. It is therefore important to

Dataset

Server

UserInterface

BROKER

MONITOR

MODE

Autonomous

Teleoperation

Client

BRAIN

BATTERIES

SENSORS

CONTROL

ACTUATORS

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minimize errors in estimated position during the path

execution phase.

Mobile robots use additional sensors to deal with the

localization problem. The localization strategies are also

based on the use of redundant information from the sensorial

system which would lead to data fusion methods. Data fusion

should give better results than a single sensor given data

error.

C.Sensorial SystemWe have also included another four sensors in the wheel

encoders:

1) Laser distance meter: Provides information about thedistance between the robot and the floor (robot height)

2) Inclinometer: Robot orientation (angle)3) Welding detector: Tank weld detection4) Ultrasonic sensors: To avoid obstaclesThe goal of the three first sensors is to estimate the robots

absolute position; ultrasonic sensors are meanwhile required

in most navigation systems to deal with obstacle avoidance.Figure 6 shows the sensors used by the robot.

In the case of oil tank navigation, several obstacles could

appear in the robot trajectory such as stairs, valves, etc. On

some occasions an obstacle could affect the sensor measures

confidence; for example, the laser distance sensor could

report the wrong distance between the robot and the floor if

an obstacle (tank piece) is in the way. It is for this reason that

redundant information is so important.

Fig 6. Sensors used by the sensorial system

In the sensorial system, we would like to highlight the

welding detector. At this moment we are using a temporal

solution to detect the tank welds. When the robot passes over

a weld, the surface variation (weld) causes it to press the

mouse wheel, thus storing a new event in the client system. In

the next version, the soldier detection sensor will be replaced

with a limit switch sensor.

D.Computing absolute robot positionIn comparison to other wall climbing robot inspection

environments, oil tanks show special features such as welding

joins that could be used as landmarks. When the robot passes

over a weld, the robot position could therefore be updated.

To compute the absolute robot position in the tank, we

assume that: The oil tank landmarks are known from thedataset

The initial robot position is set up by the operatorThe robot movements are considered in a vertical planar

space (x, y). The coordinate y therefore corresponds with the

robot height with regard to the floor, while the x coordinate is

the horizontal robot position with regard to the tank

coordinate origin.

The absolute robot position is computed by developing a

data fusion process. The data fusion process takes into

consideration the position computed by means of the

encoders (odometry) and inclinometer, laser distance meter

measures and the well known landmarks.Let be the absolute robot position at

the time step t, and be the absolute robotposition estimated by mean of the encoder sensor and the

inclinometer orientation measure ( (t))

(2)

(3)

where T is the sampling period, )](),([)( tvtvtv yxt = is

the robot velocity (is assumed to be 0) and)(R denotes the rotational transformation matrix from the

robot to the world coordinate.

(4)

where )(tver the is the calculated translational velocity ofthe right wheel and )(tvel is the calculated translationalvelocity of the left wheel by means of the encoders position

variations.

We shall denominate as )(t the distance measured by thelaser distance sensor and a pair of

coordinates for the k-esime landmark (1 ,Kk whereKis the number of tank landmarks)

The data fusion module computes the robot position in the

axis y ( )(tpy ) by using )( tp , )(t and the landmarks. Thelast item is used only when a weld has been detected. The

goal of the data fusion module is to fix the encoder errors

with the laser distance measure, but taking into consideration

wrong measures from the laser distance sensor caused by

obstacles. Thus, if the current laser distance differs by more

than a threshold 1 to the )( tp value, the robot positionassumes )( tp as being the best choice. Another choice is toupdate )(tpy when a horizontal weld is detected, if the

Sensorial

System

DLS-B30

Laser distance meter

Inclinometer

Encoder

Ultrasonic

SRF 08

Soldier detector

Microsoft Wireless

Notebook Optical MouseHEDL 5540

Seika NG360

=

)cos()sin(

)sin()cos()(

R

))()((2

1)( tvtvtv elery +=

TtvRtPtP t *)(*)()1()( +=

)](),([)( tptptP yx=)](),([)( tptptP yx=

)(tvx

))(),(( kykx

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difference between )( tp and the landmark closest to thecoordinate )'(ky is less than the threshold, 2 )(tpy isupdated with )'(ky . The robot position in the axis x

)(tpy is usually estimated with )( tpx . Only when a weld isdetected and corresponds to robot clamber is a vertical weld

updated by )'(kx .The follow pseudo code therefore shows how the robots

absolute position is computed by the data fusion strategy.Ifweld_detection == false

if

else

end

or else

'k =getClosestLandMarkIndex(P(t-1))If ( )'(ky == )(t ) or ( )

else

endweld_detection = false

end

E.TeleoperationThe teleoperation task was developed by using the Xbox

360 controller. The server application is able to monitor the

current robot position in the tank with a delay of 1 second.

This ability provides the operator with the possibility of

teleoperating the robot even in situations in which the area

visibility is low or inexistent. This feature is an advantage

over others teleoperated systems in which the operator has to

guide the robot by using visual contact with the interior of thetank. Our system is able to teleoperate the robot by means of

monitor information (robot current position over the tank

plan).

IV. EXPERIMENTS RESULTSOne of the principal problems that may appear when

constructing a climbing robot is the appearance of slide on the

vertical surface during navigation. In order to demonstrate the

good behavior of the prototype, some motion capture

measures have been made on the robots trajectory. Tracking

has been accomplished with Qualisys Optotrack system. The

kinematic model of the robot, presented in the equation in

Section II (1), is compared with the measures achieved by

motion capture system

The kinematics equations have been introduced in

Simulink and values to accomplish the desired trajectory have

been assigned to vt and . The results are shown in Figure 7.6 kg have been added to the robot in order to study its

behavior in a similar situation to that of the real world in

which the inspection equipment will add some weight to the

robot (see Figure 7).

Fig 7. Real prototype in service.

Values obtained with Optotrack have been included in

Figure 8. Note, therefore, that a trajectory has been obtained

from the robot without slides. The value of the simulation and

the measured data are very similar.

Fig 8. Optotrack Meausure vs kinematic model simulation

The trajectory generated in the simulation of the kinematic

model makes it move across the center of mass of the robot,

which is placed in the Instantaneous Center of Rotation (ICR)

(see Figure 9). The measurements of the trajectory have not

allowed an element to be placed to track in the ICR of the real

robot, and the measure that is shown with the motion capture

system therefore has a deviation that can be appraised in

Figure 9 when the robot turns.

)()( tptp yy =

1))()(( ttpabs)()( ttpy =

)()( tptp xx =

2)()'( tpky y)'()( kytpy =

)'()( kxtpx =

-100 0 100 200 300 400 500 600 700 800 900-200

0

200

400

600

800

1000

X position(mm)

Yposition

(mm)

Optotrack measurement vs kinematic model

Optotrack measurement

Kinematic model

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Fig 9. Differences in the trajectory for 2 points inside the robot. a) Robot

turning seen from measurement point, b) Robot turning seen from ICR

V. CONCLUSIONSThis paper presents an autonomous climbing robot

prototype for the non-destructive inspection of oil storage

tanks. The proposed prototype cofiguration has been

developed with the capability of climbing up steel walls and

navigating welding lines.

The remote inspection system has been illustrated. Asensorial system and data fusion strategy to estimate the

absolute robot position have been proposed. This allows the

robot to navigate autonomously.

The mechanical system has demonstrated the capabilities to

track an accurate trajectory which is given by the defined

kinematic equations.

VI. FUTURE WORKSAs future works, we suggest adding a vision sensor. This

sensor will be a passive sensor with the goal of providing the

operator with more information about the amount of wall tank

conditions to help in the visual inspection of tank parts suchas valves, etc. during the teleoperation.

Another goal to evaluate is the accuracy during the

estimation of the absolute robot position.

ACKNOWLEDGMENT

This paper was sponsored by REPSOL and by the Junta de

Comunidades de Castilla-La Mancha (Spain).

REFERENCES

[1] R. Fernandez, A. Gonzalez, V. Feliu, A proposed wall climbing robotfor oil tank inspection, in Int. Conference in Climbing and WalkingRobots (Coimbra, Portugal, 2008).

[2] W. Fischer, F. Tche, R. Siegwart, Magnetic Wall Climbing Robot for

Thin Surface with Specific Obtacle, in Int. Conference of Field andService Robot, (Chamonix, France, 2007).

[3] W. Fischer, F. Tche, R. Siegwart, Inspection System for Very Thinand Fragile Surfaces, Based on a Pair of Wall Climbing Robots withMagnetic Wheels, in Int. Conference on Intelligent Robots andSystems, (San Diego, USA, 2007).

[4] W. Shen, J. Gu, Y. Shen, Permanent Magnetic System Desing for wallclimbing robot, in Int. Conference on Mechatronics Automation,(Niagara Falls, Canada, 2005).

[5] C. Balaguer, G. Virk, M. Armada, Robot Applications AgainstGravity, in IEEE Robotics Automation Magazine, Vol. 13, Issue 1,March 2006, pp. 5-6.

[6] D. Longo, G. Muscato, Alicia Climbing Robot: a Three-module Robotfor Automatic Wall Inspection, in IEEE Robotics AutomationMagazine, Vol. 13, Issue 1, March 2006, pp. 42-50.

[7] H. Slocum, S. Awtar, J. Hart, Magnebot: Magnetic Wheels BasedOverhead Tranportation Concept, in Int. Federation of AutomaticControl, (Barcelona, Spain, 2002).

[8] B. Luk, D. Cooke, S. Galt Intelligent legged climbing service robotfor remotemaintenance applications in hazardous environments, inRobotics and Autonomous Systems, June 2006, pp. 141-152.

[9] G. La Rosa, M. Mesina, G. Muscato, R. Sinatra, A low-costlightweight climbingrobot for the inspection of vertical surfaces, inMechatronics, 2002, pp. 71-96.

[10] Y. Zhang, A. Nishi, Low-presure air motor for wall-climbing robot

actuation, inMechatronics, 2003, pp. 377-392.[11] L. Briones, P. Bustamante, M. Serna, ROBICEN: A wall-climbing

robot for inspection in nuclear power plants, in Robotic Computer-Integrated Manufacturing,Vol. 11, No. 4, 1994, pp. 287-292.

[12] Jireh Industries LTD, Tripod - a Revolution in Remote Tranportation;Industrial Product http://www.jireh-industries.com.

[13] Anibal Ollero Robtica: manipuladores y robots mviles Marcombo2001

[14] Weimin Shen and Jason Gu Yanjun Shen. Proposed Wall ClimbingRobot with Permanent Magnetic Tracks for Inspecting Oil Tanks.Proceedings of the IEEE International Conference on Mechatronics &Automation, Vol.4, July 2005 pp. 2072-2077.

[15] W Yan, L Shuliang, X Dianguo, Z Yanzheng, S Hao, G. Developmentand application of wall-climbing robots IEEE InternationalConference in Robotics and Automation, Vol.2, 10-15 May 1999,pp.1207-1212.

[16] Love P. Kalra, Jason Gu, Max Meng, "A Wall Climbing Robot for OilTank Inspection," Robotics and Biomimetics, IEEE InternationalConference on, pp. 1523-1528, 2006

b)a)

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