ORIGINAL ARTICLE

Design of a quadruped robot for human–elephant conflictmitigation

S. J. Sugumar • R. Jayaparvathy

Received: 23 March 2013 / Accepted: 30 August 2013 / Published online: 7 November 2013

� ISAROB 2013

Abstract

Introduction Human–elephant conflict is a major problem

leading to crop damage, human death by elephants and

elephants being killed by people. The surveillance and

tracking of elephant herds are difficult due to their size and

nature of movement.

Materials In this article, we propose a four-wheeled

quadruped robot to mitigate human–elephant conflict. The

robot can detect movement of wild pachyderms in certain

pockets along the forest borders through which the ele-

phants enter into the human living areas from the forest.

The robot is so designed that it can navigate with wheels on

flat terrains and with legs on unfriendly rugged terrains

with the help of mounted cameras.

Methods The images of the wild elephant are captured

and transmitted to the base stations and an SMS is sent to

the forest officials indicating an elephant is found. We

obtain a suitable kinematic model for both legs and wheels

with control algorithm for the quadruped robot to move

along a predetermined path.

Conclusion The quadruped robot proposed is a solution

to detect elephant movement without affecting the eco-

logical conditions to overcome human–elephant conflict.

The unpredictability of time and location of elephant

arrival into the villages are considered the major issues that

are resolved in this work. The results of our work con-

tribute to elephant conservation issues and are suitable for

the detection of elephants in forest border areas.

Keywords Kinematic � Degree of freedom � Legged

robot � Quadruped � Leg-wheeled robot

1 Introduction

India is home to 60 % of the Asian elephant population.

According to Project Elephant, a government initiative for

elephant conservation, there were 28,000 elephants in the

country in 2011. Coimbatore forest is a habitat for elephants

in India. Elephant intrusion along the forest border areas is

the common problem in this division. Elephants intrude into

farm fields and villages and cause damage. Man–animal

conflict has been on the rise in the forest border areas with

herds of wild pachyderms straying into human habitation

[1]. Till recently, the Coimbatore forest department tried

using powerful searchlights, crackers, and drums to push the

wild elephant herds back into the jungle. The Kumki (Res-

cue) elephants were used more in hill areas to chase the herds

of wild elephants into the forests. Surveillance and tracking

of these herds are difficult due to their size and nature of

movement. The loss due to destruction in the farms is more.

The elephants are also subject to attack by humans resulting

in danger to the life of elephants [2]. Many approaches have

been used to detect the intrusion of the elephants. One such

approach is to use seismic geophones as underground sen-

sors to sense and produce an output voltage based on the

vibration produced when the elephants walk in the detection

range of the seismic geophones.

In this paper, we propose an enhancement to the

aforesaid elephant intrusion detection system. The wheeled

quadruped robot can be used for such intrusion detection

system. Despite its success on wheeled locomotion for

predetermined and plane surfaces, it is not suitable for

unknown and rough terrains. The tracked locomotion is

S. J. Sugumar (&)

Coimbatore Institute of Technology, Coimbatore, India

e-mail: sjs_cit@yahoo.co.in

R. Jayaparvathy

SSN College of Engineering, Chennai, India

e-mail: jayaparvathy14@gmail.com

123

Artif Life Robotics (2013) 18:204–211

DOI 10.1007/s10015-013-0120-2

developed to cope with this problem. However, tracked

locomotion is also not without problems, since it destroys

the terrain on which the vehicle is moving. As an alter-

native to both wheeled and tracked movements, legged

locomotion is developed by imitating the legged animals in

nature [3]. Wheeled and legged mobile robot path planning

has received great attention and several models and algo-

rithms have been developed for the detection and overcoming

of obstacles and in the development of skills that allow robots

to walk on irregular surfaces with obstacles [4].

In [5] Belter et al. discussed a multi-layered drive control

for walking robot. This was implemented by using six-leg-

ged robot with 18 degrees of freedom (DOF). Each joint used

one DC servomotor, and synchronization was done among

the legs using master slave concept. The authors also

addressed sensing system issues for walking robot using

closed control loops through these sensors. In [6] Amaral

et al. designed a robot that walked on straight, curved paths,

detecting and overcoming known obstacles. The robot used

only feet sensor information and managed standalone

movement sequences to position the robot and to overcome

obstacles. In [7] Szrek and Wojtowicz constructed the Leg-

Van wheel-legged robot with an autonomous leveling and

obstacle detection system. They presented the robot’s kine-

matic wheel suspension system, general operation strategy,

and control system. In [8] Raibert et al. developed a new

breed of rough-terrain robots called BigDog that captured the

mobility, autonomy, and speed of living creatures. Such

robots could travel in outdoor rough terrains which are dif-

ficult to travel for conventional robot vehicle platforms.

In this work, a four-legged and wheeled robot, namely the

wheeled quadruped robot is developed specially to move in

any terrain i.e., both walk using legs and move on wheels.

The elephants in the forest always move along specific cor-

ridors in search of food and water. These corridors are

commonly called as elephant corridors. They enter the farm

lands by passing through certain pockets along the forest

borders. We propose a technique to use robots in detecting

elephants in forest border areas only, not inside the forest.

The robot we design can be laid in certain elephant pockets

through which the elephants normally come out of the forest

and enter human habitat region. This design is made in such a

way the quadruped robot is made to move in predetermined

path to capture the elephant image in and around these

pockets. This work is done as a solution to human–elephant

conflict, which is predominant in the Coimbatore forest

division in Tamil Nadu, India.

2 Quadruped architecture

In our previous work ‘‘An Early Warning System for Ele-

phant Intrusion Detection along the Forest Border Areas’’

[9], sensors namely seismic geophones are buried under the

ground. If the elephant walks in the sensing range of the

geophones, the vibrations are converted to an electrical

signal and the signal is fed to a controller which sends an

SMS to the forest officials for necessary action. In such

intrusion detection systems there is a possibility of insuffi-

cient vibrations sensed by the geophones as a consequence

of weather conditions like rain. Due to this it may miss the

event i.e., elephant movement detection in the forest border

areas. A wheeled quadruped robot (WQR) is proposed in

this work which can navigate with wheels on flat terrains

and with legs on rugged terrains. The robot is designed in

such a way that it can move on unfriendly terrains with the

help of cameras mounted on the mobile robots [10]. The

elephants come out of the forest through certain pockets to

enter into villages for getting food and water during dry

seasons. The proposed robots can be made to move in the

elephant pockets for detecting the entry of elephants into the

human living areas. The image captured by the robot is

processed and compared with the stored image of elephants.

When the image matches, SMS is sent to the forest officials

through the GSM transceiver connected with the controller.

The image capture unit is shown in Fig. 1.

The robot control system architecture is shown in Fig. 2.

It is a distributed control system with one main processor as

master and four slave processors. The main processor is

responsible for communication with slave processors, sensor

reading, communication with host computer via RF wireless

module, and user interface on the robot. There is one slave

processor for each leg. The slave processor is dedicated to

control three servomotors and a DC motor in each leg. The

joint angles are generated and sent from the host system for

required position and orientation of each leg and robot body

by using the kinematic model developed. To maintain the

center of gravity of the robot and to get the orientation of the

robot body, the acceleration and tilt angle of the robot body

have to be measured. Tri axis linear accelerometer is used in

this robot to measure acceleration and tilt angle. It can

measure the acceleration in all three directions from ±1.5 to

6 g of force generated due to robot motion. This sensor has

three outputs one for each axis. Output will be an analog

signal which varies according to the force experienced by the

sensor in each direction.

Each leg is equipped with ground contact sensor placed

on robot foot. The contact sensor [6] is basically a DPST

micro-switch with NC and NO contacts fixed at the foot of

each leg. This provides necessary information about the

CAMERAATMEGA

MCUGSM

Fig. 1 Image capture unit

Artif Life Robotics (2013) 18:204–211 205

123

contact between the robot foot and surface of the terrain for

the control system of the robot. An IR optical sensor is used

as the wheel position encoder. To model the position and

orientation of the robot in global frame when it navigates,

angular position and angular velocity for each wheel are

calculated. This encoder gives the wheel angular position

uj and angular velocity xj for the jth wheel. The analog

servomotors are used as the actuators in each leg joint. The

analog servomotors have an integrated potentiometer to

enable closing of the feedback-loop in the position control

mode. Each servomotor has to be manually calibrated to

avoid the errors in position control.

The mechanical frame of the robot is designed with alu-

minum metal sheet to minimize the weight. DC servomotors

with integrated nylon gear have been chosen to drive each

robot leg joint. This servomotor has a stall torque of

0.314 Nm. The knee of each leg was designed with option to

connect a DC motor with the wheel. These four wheels can

reach the terrain surface when all the four legs are folded

under the robot body as shown in Fig. 3. This option enables

it to navigate through wheels on flat terrains.

The DC motor has an integrated reduction gear box and

it has a torque of 0.49 Nm. Each part of the robot body is

designed separately by part designing tools in Solid Works

[11] and the parts are assembled to form the body structure

using the assembling tool.

3 Kinematic of the robot

Kinematic is the science of geometry in motion [12]. It is

restricted to a pure geometrical description of motion by

means of position, orientation, and their time derivatives.

The links and joints in a robotic system are modeled as

rigid bodies, and the properties of rigid body displacement

takes a central place in robotics. In this work, a kinematic

controlled four-legged and wheeled robot named the

wheeled quadruped robot is developed. We obtain a suit-

able kinematic model and control algorithm that can help

the quadruped robot to move along a predetermined path to

detect elephant movement along forest border areas.

3.1 Kinematic modeling of robot legs

A systematic method for kinematic modeling of four-leg-

ged robots for walking on rough terrain is developed in this

work. A kinematic model for characterizing the robot joints

and linkage parameters that capture the motion of quad-

ruped robot is developed. The equations of motions are set

up using this table for different frames starting from the

robot reference frame, going through individual legs and

finally reaching feet-terrain contact frames. The composite

equation of motion is formed from those of individual legs.

The formulation allows determining actuations to various

joints for achieving a desired robot motion, while opti-

mizing a performance index such as a stability measure.

A commonly used convention for selecting frames of

reference in robotic applications is the Denavit–Harten-

berg, or D–H convention [13]. The (D–H) coordinate sys-

tem for a single leg is shown in Fig. 4. In this convention,

each homogeneous transformation matrix i-1Ti to trans-

form coordinate frames (i) to (i-1) is represented as a

product of four basic transformations using the parameters

of link i and joint i.

i�1Ti ¼ Rx ai�1ð ÞDx ai�1ð ÞRz hið ÞDzðdiÞ ð1Þ

The four quantities hi, ai, di, ai are parameters associated

with link i and joint i. The four parameters ai, ai, di, and hi

as given in (1) are generally given the names link length,

Fig. 2 Robot control system block diagram Fig. 3 Robot with wheel and leg arrangement

206 Artif Life Robotics (2013) 18:204–211

123

link twist, joint distance, and joint angle, respectively [14].

The matrix Ti is a function of a single variable, it turns out

that two of the above four quantities are constant for a

given link, while the fourth parameter, hi for a revolute

joint and di for a prismatic joint, is the joint variable.

Considering aj,i-1: Link length, aj,i-1: Link twist, dj,i:

Joint distance, hj,i: Joint angle transformation between i–ith

frame and ith is given by

j;i�1Tj;i ¼

chj;i �shj;i 0 aj;i�1

shj:icaj;i�1 chj;icaj;i�1 �saj;i�1 �saj;i�1dj;i

shj;isaj;i�1 chj;isaj;i�1 caj;i�1 caj;i�1dj;i

0 0 0 1

2664

3775

ð2Þ

where chj,i stands for cos(hj,i), shj,i stands for sin(hj,i), c

aj,i-1 stands for cos(aj,i-1), s aj,i-1 stands for sin(aj,i-1) and

L stands for Link state.

j;0Tj;1 ¼

chj;1 �shj;1 0 Lj;0

shj;i chj;1 0 0

0 0 1 0

0 0 0 1

2664

3775 ð3Þ

j;1Tj;2 ¼

chj;2 �shj;2 0 Lj;1

0 0 �1 0

shj;2 chj;2 0 0

0 0 0 1

2664

3775 ð4Þ

j;2Tj;3 ¼

chj;3 �shj;3 0 Lj;2

shj;3 chj;3 0 0

0 0 1 0

0 0 0 1

2664

3775 ð5Þ

j;3Tj;4 ¼

1 0 0 Lj;3

0 1 0 0

0 0 1 0

0 0 0 1

2664

3775 ð6Þ

The total transformation between the body of the robot

and foot of the jth robot leg is

j;0Tj;4 ¼ j;0Tj;1 � j;1Tj;2 � j;2Tj;3 � j;3Tj;4 ð7Þ

from Eqs. (2) to (6)

j;0Tj;4 ¼

t11 t12 t13 t14

t21 t22 t23 t24

t31 t32 t33 t34

0 0 0 1

2664

3775 ð8Þ

where

t11 ¼ chj;1chj;2chj;3 � chj;1shj;2shj;3

t12 ¼ �chj;1chj;2shj;3 � chj;1shj;2chj;3; t13

t14 ¼ chj;1chj;2ðLj;3chj;3 þ Lj;2Þ � chj;1shj;2shj;3Lj;3

þ Lj;1chj;1 þ Lj;0

t21 ¼ shj;1chj;2chj;3 � shj;1shj;2shj;3

t22 ¼ �shj;1chj;2shj;3 � shj;1shj;2chj;3; t23

t24 ¼ shj;1chj;2ðLj;3chj;3 þ Lj;2Þ � shj;1shj;2shj;3Lj;3 þ Lj;1shj;1

t31 ¼ shj;2chj;3 þ chj;2shj;3; t32 ¼ �shj;2shj;3 þ chj;2chj;3

t33 ¼ 0; t34 ¼ shj;2ðLj;3chj;3 þ Lj;2Þ þ chj;2shj;3Lj;3

From Eq. (8), the position and orientation of the jth leg’s

foot are calculated. The WQR has 6 DOF with respect to

the global axis (XG, YG, ZG), the 3 linear motions in all

three axes and 3 rotations about each three axis as shown in

Fig. 5. The choice of zi is arbitrary; we see that by choosing

ai and hi appropriately, we can obtain any arbitrary

direction for zi. Thus we assign the axes z0… zn-1.

Specifically, we assign zi to be the axis of actuation for

joint i?1. Thus, z0 is the axis of actuation for joint 1, z1 is

the axis of actuation for joint 2, etc.

There are two cases to consider: (1) if joint i?1 is revolute,

zi is the axis of revolution of joint i?1; (2) if joint i?1 isFig. 5 Coordinate system of robot body in global frame

Fig. 4 (D–H) Coordinate system for a single leg

Artif Life Robotics (2013) 18:204–211 207

123

prismatic, zi is the axis of translation of joint i?1. The joint i is

fixed with respect to frame i, and when joint i is actuated, link

i and its attached frame, oi xi yi zi, experience a resulting

motion [15]. Once the z-axes for the links have been estab-

lished, the base frame is constructed. The choice of a base

frame is nearly arbitrary. We may choose the origin O0 of the

base frame to be any point on z0. We then choose x0, y0 in any

convenient manner so long as the resulting frame is right-

handed. This sets up frame 0. Once frame 0 has been estab-

lished, we begin an iterative process in which we define frame

i using frame i-1, beginning with frame 1. The D–H

parameter of a robot leg is given in Table 1.

The vector Q contains the orientation and position

vectors of the WQR.

Q ¼

x

y

z

abc

26666664

37777775

ð9Þ

The absolute linear position of the WQR is defined in

the global frame XG, YG, ZG-axes as n.

n ¼x

y

z

2435 ð10Þ

The absolute angular position of the WQR is defined in

the global frame XG, YG, ZG-axes as g.

g ¼abc

2435 ð11Þ

where, a is the roll angle that determines the rotation about

X0 axis, b is the pitch angle that determines the rotation

about Y0 axis, c is the yaw angle, that determines the

rotation about Z0 axis. The rotation matrix from the body

frame to the global frame is

RG ¼cccb casbsa� scca ccsbcaþ scsasccb scsbsaþ ccca scsbca� ccsa�sb cbsa cbca

24

35 ð12Þ

where, cx stands for cos(x) and sx stands for sin(x).

Let ni-1 = [xi-1 yi-1 zi-1]T and ni = [xi yi zi]T denote the

position of the current and next frames of the robot,

respectively [16]. Similarly, let gi-1 = [ai-1 bi-1 ci-1]T and

gi = [ai bi ci]T be the orientation of the previous and current

frames of the robot, respectively, where ai bi ci are the

rotation around x, y and z axis, or roll, pitch, and yaw,

respectively derived from (10) and (11). Using the D–H

notation, the transformation from the frame Fi-1 to the frame

of the robot Fi is obtained by rotation hi about z-axis, trans-

lation di along z-axis and rotation2i about x-axis. The results

of the transformation are provided in the result section.

3.2 Kinematic of robot wheel

Let xi and vi, s = 1… 4 denote the wheel angular and

center linear velocities, respectively. From the aforemen-

tioned assumption, we have the angular velocity as

xL: = x1 = x2, xR: = x3 = x4 and the linear velocities

vL : = v1 = v2, vR : = v3 = v4. The linear velocity corre-

sponds to the left wheel and right wheel velocities [17].

The front left and front right wheels provide the direction

of orientation of the quad robot as shown in Fig. 6. The

back right and left wheels provide the speed the robot. We

consider the quadruped robot moving on two dimensional

planes with inertial coordinate frame (Xg, Yg). To describe

motion of the robot, we define a local frame attached to it

with origin in its robot mass center (RMC). Assume that q

, [X Y h]T [R3 denotes generalized coordinates, where X,

Y determine RMC position and h is an orientation of the

local frame with respect to the inertial frame, respectively.

The change of angle from the inertial frame is the heading

angle. Let v , [vx vy]T [ R

3 be a velocity vector of RMC

expressed in the local frame with vx and vy determining lon-

gitudinal and lateral velocity of the quad robot. The longitu-

dinal velocity determines the direction of rotation and the

lateral velocity determines speed of the robot, which we

Fig. 6 Image of quadruped robot with four wheel and leg

Table 1 The D–H parameter of the robot leg

Leg

(j)

Joint

(i)

aj,i-1

(mm)

aj,i-1

(deg)

dj,i

(mm)

hj,i

(deg)

Joint angle

range (hj,i)

1 1 L10 0 0 h11 0� * 90�1 2 L11 90� 0 h12 -90� * 90�1 3 L12 0 0 h13 0� * -180�1 4 L13 0 0 0 –

208 Artif Life Robotics (2013) 18:204–211

123

consider to be constant. The kinematic equation of motion

using rotation matrix [18] is given as follows

_q ¼_X_Y_h

24

35 �

cosh �sinh 0

sinh cosh 0

0 0 1

24

35 �

vx

vy

x

24

35 ð13Þ

where _q [R3 is the generalized velocity vector and x denotes

angular velocity of the vehicle. xL and xR which denote

angular velocities of left and right wheels, respectively, can be

regarded as control inputs at kinematic level and can be used to

control longitudinal and lateral velocity. The longitudinal

velocity is given by

vx ¼ rxR þ xL

2; ð14Þ

Lateral velocity vy which determines velocity of lateral

slip of the vehicle can be obtained as follows

vy ¼ xCORx ð15Þ

where xCOR is a coordinate of instantaneous center of

rotation (COR) expressed along x-axis. We assume that

xCOR = const. It can be written as

vy ¼ x0x ð16Þ

where x0 is the geometrical parameter and the angular

velocity is obtained from the following equation

x ¼ rxR � xL

2cð17Þ

where r is so called effective radius of wheels and 2c is a

spacing between left and right wheel. The orientations to

move to the destination are provided to the controller. The

controller calculates the new heading angle [19] by which

the quad robot should rotate for movement. The angular

difference between the robot heading and a fixed point on

the surface is provided by the heading angle and is

calculated as follows

hHA ¼ tan�1 ydestn � ysource

xdestn � xsource

� �� �ð18Þ

The controller adjusts xL and xR to maintain a fixed

distance from the x-axis, and to maintain hHA = 0 for a

straight travel from source to destination. The results are

plotted in the next section.

4 Simulation and testing

The Kinematic model has been developed in this work for

the robot legged motion and is simulated using MATLAB

[20]. The position and orientation parameters of the robot

are generated and the parameters are plotted in 3D graph to

visualize the posture of the robot.

The computed joint parameters are sent to the robot via

RF module. The robot hardware setup takes the posture that

is generated by the kinematic model from the received joint

parameter data. Figure 7 shows the 3D graph of the posture

of robot. The same parameter was used for the posture of

leg in hardware.

Once the joint angle data are received from host system,

then the robot’s master controller sends joint angle data to the

corresponding slaves via SPI. The received tilt angle infor-

mation from accelerometer is used to find the orientation of

robot body. The calculated orientation is visualized in 3D plot.

In Fig. 8, the green square represents the robot body and red

square represents the plane perpendicular to the direction of

gravity. The orientation of the body when the roll angle

a = 35� and the pitch angle is b = 25�, and for different roll

and pitch angles of the robot body is generated and verified

with the corresponding robot movement. To maintain the

center of gravity of the robot, all three angular displacements

should be nearer to 0� as possible. If the three angular dis-

placements are not nearer to zero then the joint angles are

recalculated by the kinematic model for the same foot position

to maintain the center of gravity i.e., making the roll, pitch, and

yaw nearer to zero without change in foot position.

The joint angle information will be generated using the

kinematic model of the WQR. The joint angle information

is sent to the robot to control the servomotor.

This test was carried out to verify whether the generated

pulse width modulation (PWM) signal to control the servo-

motor shaft angle position is appropriate to the received joint

angle information. The PWM signal is viewed for different

joint angles, and the PWM signal generated for joints J22, J23,

J41, J31 is shown in Fig. 9. The calibrated PWM ON time

value for joint servomotors is provided in Table 2.

Fig. 7 The 3D graph of the leg posture

Artif Life Robotics (2013) 18:204–211 209

123

The simulation is performed in Matlab environment to

verify behavior of the quadruped robot wheel. Permissible

torque signal is max = 0.25 Nm and angular velocities of

wheels = 56[rad/s]. From Fig. 10, the response of velocity

we can estimate the time constant of the robot system and

adjust the control gains accordingly. The velocity response

generated by the robot wheel which after a 150 ms the robot

control system reaches the desired linear velocity in which the

speed of the robot is constant during the motion of path from

the source point to destination point.

Figure 11 shows the robot heading angle of the quad robot.

The robot starts with a zero heading angle and takes a small

deviation and adjusts the position to the center of path and

reaches the zero heading angles again after 180 ms. Once the

zero heading angles are reached the robot moves in a straight

direction and follows the path.

4.1 Testing of the wheeled quadruped robot

The Robot Prototype directly linked with Host computer

via serial port to test the controlling options through

MATLAB GUI [21]. The joint angle information is sent to

the robot master control unit. The received joint angle

information from host then processed by the master control

unit and sent to slave control units. The robot does the

motion generated according to the kinematic model. Fig-

ure 12 shows that the robot makes the same posture plotted

in GUI. This test proves that the communication between

host and robot master control unit and communication

between master and slaves are working properly. Any

sequence of motion can be generated by host system and

sent to the robot for navigation on rough surface.

Fig. 8 Orientation of robot body when its roll is 35�

Fig. 9 PWM for J22, J23, J41, J31

Fig. 10 Linear velocity generated by the WQR

Table 2 Calibrated PWM on time value for joint servomotors

0� 45� 90� 135� 180� Angle

Motor

A 2450 2050 1625 1210 800 Servomotor PWM signal

on time values (in ls)

pulse period 20 msB 650 1040 1425 1810 2200

C 575 930 1300 1700 2100

D 550 925 1300 1700 2100

E 675 975 1375 1750 2150

F 565 910 1325 1725 2075

G 650 1070 1500 1970 2450

H 550 1000 1450 1900 2350

I 525 945 1365 1830 2300

J 500 925 1350 1800 2250

K 540 970 1400 1865 2325

L 525 930 1370 1820 2300

M 550 980 1425 1900 2400

210 Artif Life Robotics (2013) 18:204–211

123

5 Conclusion

The wheeled quadruped robot proposed in this work is used

to monitor and detect the elephant in the forest border areas

as a solution to human–elephant conflict. The kinematic

model for the legged motion has been developed in such a

way to enable movement in any terrain. The posture of WQR

body has been plotted using 3D plot in graphical user inter-

face environment and the same configuration of robot pos-

ture is verified using the designed WQR hardware. The

orientation of the robot body with respect to the plane per-

pendicular to direction of gravitational force is monitored in

3D plot. The walking motion of robot has been tested in

different terrains. The auto navigation of robot for walking in

all terrains can be done using this kinematic model. Kine-

matic model of wheeled locomotion is developed and tested.

The proposed method can be used as an effective scheme to

detect elephants in the forest border areas.

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Fig. 11 Heading angle of the WQR

Fig. 12 Testing of robot controlling through GUI

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