Speed and position control of autonomous mobile robot on variable

GUNES & BABA: MOBILE ROBOT FOR SPEED AND POSITION CONTROL ON VARIABLE TRAJECTORY 513Journal of Scientific & Industrial Research

Vol. 68, June 2009, pp. 513-521

*Author for correspondence

Tel: +90 536 4292945

E-mail: [email protected]

Speed and position control of autonomous mobile robot on variable trajectory

depending on its curvature

Mahit Gunes1 and A Fevzi Baba2*

1Department of Electrical-Electronics Engineering, Kahramanmaras Sutcu Imam University, K.Maras/Turkey2Electronics and Computer Department, Technical Education Faculty, Marmara University, Istanbul/Turkey

Received 07 July 2008; revised 13 March 2009; accepted 19 March 2009

This paper presents design of autonomous mobile robot MBR-01 for speed and position control on variable trajectory

depending on trajectory curvature. MBR-01 can communicate reciprocally with host computer using RF data transceiver. Road

data image is captured by CCD camera mounted on vehicle and transferred to host computer using RF data link unit. Applying

image processing on trajectory, reference speed has been produced depending on curvature of trajectory. Reference speed is

applied to fuzzy controller unit and output is sent to vehicle by wireless transmitter unit. Received control signal by vehicle is

transferred to DC motor drive system with Pulse Width Modulation techniques. Position control is realized by microprocessor-

based units mounted on vehicle. Equipped 7 optical sensors detect trajectory deviation and wheel angle of vehicle for track

detection and wheel angle detector unit.

Keywords: Autonomous mobile robot, Fuzzy edge detection, Position control, Speed control

Introduction

Autonomous mobile robots are characterized by

nonlinear and complex dynamics, such as turning and

static friction, and noisy and typical harsh outdoor

environments1. Robot navigation in out-door environment

requires a real-time image processing at variable natural

illumination, bright sunlight, clouds, etc2. Different

methods on mobile robot navigations include behavioral-

based of vehicle from combination of several behaviors

including trajectory tracking3,4, target tracking5, obstacle

avoidance6,7, landmark recognition systems8,9, soccer

robots navigation10,11. Model-based3,6,12,13 and feature-

based14,15 methods are used for trajectory-tracking and

navigation toward objects. Methods on fuzzy logic based

mobile robot navigation include numerous studies as

indoor laboratory environments3,8,10,11 while some of

them discuss outdoor navigation using fuzzy systems2,7,16.

Generally, indoor applications include corner detection,

door detection, wall-following, path planning, seeking the

goal position17. Outdoor applications consist of garage-

parking, parallel parking, stair climbing, scrambling over

rubble, human-like driving using GPS systems18-20.

Under unknown and dynamic real world conditions,

Internet allows users to connect to a website and remotely

control a robot manipulator or a mobile robot21,22. A

number of limitations and difficulties (restricted bandwidth,

image transmission delays, and packet lost or error),

influence performance of Internet-based robot navigation

systems23,24.

This paper proposes a vision-based speed and position

control on variable trajectory of developed autonomous

mobile robot (MBR-01), which can be remotely controlled

by host computer with wireless image transferring unit

mounted on MBR-01 for different applications.

Materials and MethodsA. System Structure

Developed vehicle (Fig. 1) weighs 11 kg and has

maximum speed of 4 m/s. It is equipped with 12VDC

4Ah battery power supply unit, to navigate for 6 h. Vehicle

has two electrical motors; steering motor provides

orientation of driving wheel, and second motor provides

driving torque. A wireless image transmitter unit (2.4 GHz

carrier frequency) captures trajectory information with a

CCD camera and transfers image data to computer. There

is a reciprocal RF data transmitter unit for controlling

signals between robot and computer (Fig. 2). At the mobile

514 J SCI IND RES VOL 68 JUNE 2009

Fig. I-Developed autonomous mobile robot (MBR-Ol)

r - - - - - - - -- ,r---------------------------,PC

2.4GHzAV Receiver

MOBilE ROBOT

2.4GHzAV Transmitter

~CCD

Fig. 2-Simplified control diagram of the system

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GUNES & BABA: MOBILE ROBOT FOR SPEED AND POSITION CONTROL ON VARIABLE TRAJECTORY 515

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----- ../ ../ :'

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ICAMERA IMAGEI [FUZZY EDGE IMAGEI

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INPUT FUNCTION 1- NEGl,~ur~~-POZo . ,-200 -100 0 100 20(}

Uiminance Difference

Fig. 3-Fuzzy edge detection rules

robot side, a fuzzy controller unit performs position controlby getting angle of steering wheel and deviationinformation. Road data image is received from videoreceiver mounted on computer, and then video interfaceunit captures this image from receiver output. Aftercompletion of this process, fuzzy edge detection and lineextraction algorithms are activated.

After line extraction process on trajectory, referencespeed is determined depending on curvature of trajectory.Speed error and its derivative values are applied to fuzzycontroller. Control signal is modulated at 433 MHz carrierradio frequencies and then transmitted back to vehicleside, where control signal is received by video receiverunit and applied to speed controller unit. Steering controlunit is processed on vehicle side by microprocessors basedon fuzzy algorithm unit. For this process, vehicle isequipped with 7 sensors to take its position deviationaccording to desired trajectory. Position error is appliedto fuzzy position controller.

B. Fuzzy Edge Detection

In robot navigation, edge detection methods (Sobel,Prewitt, Roberts and Gradient) are not robust to noiseand environment changes, since a robot requires imageprocessing in real-time. Fuzzy reasoning2 classifies a pixelin an image into a border region or a uniform region based

Defuzzification graph Q: 152

.,:~o " ..

001631455973881051251461601812072272481

Fig. 4-Member functions

on luminance differences between this pixel and itsneighboring pixels. Detecting edges by fuzzy inferenceclassifying every pixel in an image into white or black isbased on: i) If a pixel points on border region then makeit black, else make it white; and ii) If a pixel points to auniform region than make it white, else make it black. Todecide whether a pixel belongs to a border or a uniformregion, luminance differences between pixel and itsneighboring pixels have to be computed (Fig. 3). Inprocessed image, it is started to compute from upper leftcomer point (0, 0) to bottom right comer point (X, Y).Through entire line, every U pixel and its neighboringluminance differences are calculated individually and thendecided to whether pixel is on edge or not. It is possibleto define 8 different rules (Fig. 3). C++ software wasdeveloped to implement this process. If resolution is 640x 480 pixels then 307200 calculations will be requiredand it reduces overall speed of the system. This is a

disadvantage for a big resolution imag~. Each pixel onprocessed image is composed of RGB values and eachcolor has 8 bit resolution. Thus maximum value of eachcolor will be hexadecimal FF and maximum value of each

pixel will be hexadecimal FFFFFF (= .6777215). Usingblack/white CCD camera, there is no important differencebetween 8 bit gray image or 32 bit color image to makedecision in edge calculation.


So in this study 8-bit gray level is used to obtain

maximum image processing speed. Maximum value 255

corresponds to white, and minimum value 0 corresponds

to black. U value of 255 is black and value of -255 is

white. For control input, luminance difference membership

function is defined by triangular function (Fig. 4). Two

fuzzy sets are defined in input function. NEG corresponds

to negative, POZ corresponds to positive, BLK

corresponds to black and WHT corresponds to white.

As a result of defined rules, output membership function

of each U point will be white if point is a plane, or black

if it is an edge. Membership functions of outputs based

on gray level intensity of pixel Q are determined by

following 8 fuzzy rules.

1 If (f1 is neg) and (f2 is neg) and (f4 is poz) and (f5 is

poz) and (f6 is poz) and (f8 is neg) then (u is black).

2 If (f2 is neg) and (f3 is neg) and (f4 is neg) and (f6 is

poz) and (f7 is poz) and (f8 is poz) then (u is black).

3 If (f2 is poz) and (f3 is poz) and (f4 is poz) and (f6 is

neg) and (f7 is neg) and (f8 is neg) then (u is black).

4 If (f1 is neg) and (f3 is poz) and (f4 is poz) and (f5 is

poz) and (f7 is neg) and (f8 is neg) then (u is black).

5 If (f1 is poz) and (f2 is poz) and (f4 is neg) and (f5 is

neg) and (f6 is neg) and (f8 is poz) then (u is black).

6 If (f1 is neg) and (f2 is neg) and (f3 is neg) and (f5 is

poz) and (f6 is poz) and (f7 is poz) then (u is black).

7 If (f1 is poz) and (f3 is neg) and (f4 is neg) and (f5 is

neg) and (f7 is poz) and (f8 is poz) then (u is black).

8 If (f1 is poz) and (f2 is poz) and (f3 is poz) and (f5 is

neg) and (f6 is neg) and (f7 is neg) then (u is black).

Luminance of each pixel is computed by Centroid

Defuzzification Method as defined in Eq. (1).

∑=

∑=

= n

iiuout

n

iiuoutiu

Q

1)(

1)(

µ

µ

…(1)

In this study, it is evident that determining limit value

of input membership functions is very important. Changing

limit values affects output defuzzification process, as

indicated in original processed image (Fig. 5) and different

membership functions (Fig. 6). Membership functions

were defined with changeable limit values, which were

determined by average luminance difference value of

processed image.

C. Fuzzy Speed Control of MBR-01

In speed control process of mobile robot, reference

speed has been determined according to curvature of

trajectory. To get this process, firstly fuzzy edge detection

algorithm was applied on image and then developed line

extraction algorithms from image determined trajectory.

According to specified trajectory curvature, reference

speed was determined by software defined by slope-speed

transfer curve. After determining reference speed, speed

error and error change were applied to fuzzy controller

unit. Speed error and error deviation are input variables

of fuzzy controller, which has two inputs in the range of

-10 to +10 pulse per second. This pulse was produced

from encoders equipped with rear wheels of the vehicle.

Fuzzy output was applied to pulse width modulation

(PWM) unit. Membership functions were defined

(Fig. 7). According to these descriptions, rules of the fuzzy

controller are stated as

1 If (e N) and (de NE) then (u Y)

2 If (e Z) and (de NE) then (u H)

3 If (e P) and (de NE) then (u H)

4 If (e N) and (de ZE) then (u Y)

5 If (e Z) and (de ZE) then (u NR)

6 If (e P) and (de ZE) then (u H)

7 If (e N) and (de PE) then (u Y)

8 If (e Z) and (de PE) then (u Y)

9 If (e P) and (de PE) then (u H)

D. Position Controller

Position controller has been implemented on mobile

robot by microcontroller units. Actual steering wheel angle

of mobile robot was measured by a mechanism. Deviation

information from trajectory was detected by 7 optical

sensors. If vehicle is in middle of trajectory, 4th sensor

Fig. 5—Original image


Fig. 6—Fuzzy edge outputs for different membership functions

Fig. 7—Membership functions


detects white stripe. When vehicle moves to right side,

white strip is detected by 3rd, 2nd and 1st sensors. Similarly,

when vehicle moves to left side, white strip is detected

by 5th, 6th and 7th number of sensors, respectively. If

vehicle passes through all sensors, robot keeps last sensor

information so vehicle moves to opposite direction.

Microcontroller board on vehicle scans white strip

detectors continuously and saves its status in memory to

learn vehicle position. This information gives vehicle

deviation from trajectory. If vehicle loses trajectory, last

sensor information in memory is taken into consideration.

Reference designator produces a reference steering angle

according to received position errors. Reference angle is

determined according to information of sensors.

• If (Position=1) Then (Angle 45°)

• If (Position=2) Then (Angle 22.5°)

• If (Position=3) Then (Angle 12.5°)

• If (Position=4) Then (Angle 0°)

• If (Position=5) Then (Angle -12.5°)

• If (Position=6) Then (Angle -22.5°)

• If (Position=7) Then (Angle -45°)

Difference between reference angle and feedback

angle was applied to fuzzy controller as error input. Input

and output membership functions were defined (Fig. 8).

In position controller, basic rules are defined as

1 If (e N) Then (u RIGHT)

2 If (e Z) Then (u STRAIGHT)

3 If (e P) Then (u LEFT)

Developed microcontroller board mounted on mobile

robot implemented all these operations.

Results and Discussion

Realized experiments were classified in two stages.

In first stage, fuzzy speed controller has been tested. In

this stage, image processing was disabled and control

was performed at a different desired reference speed

(Fig. 9a, where SV represents speed set value and PV

represents speed procure value). Speed controller

algorithms have been implemented in PC side. Controller

output was sent to mobile robot by wireless RF signals

and feedback signal was transferred back to controller

in same way. Second experiment in first stage has been

performed at high-speed test of controller. In this

experiment, when vehicle reached at high speed, DC

motor produced noises as a result of arc problem. These

noises affected RF communication unit and caused

fluctuations on actual speed (Fig. 9b).

In second stage of experiments, reference speed was

calculated according to curvature of trajectory. Reference

error and error deviations were applied to controller.

Output of controller was transferred in form of 433 MHz

RF modulated signal to mobile robot. Experiments in this

stage were speed and position control according to a

random variable trajectory (Fig. 10), different color of

trajectory strip (Fig. 11) and discrete trajectory strip (Fig.

12). Experiment on variable trajectory shows that

reference speed changes depending on each captured

image. In this experiment, higher curvature means lower

speed, whereas lower curvature means higher speed. In

position control (Fig. 10), reference trajectory (REF) is

trajectory tracked and position error (ERR) is deviation

in millimeters from trajectory (deviation from the middle

of optical sensors). When vehicle losses trajectory, it

means that deviation error is bigger than 72 mm. In this

experiment, maximum deviation was approx. 48 mm (Fig.

10). In different color experiment, whether trajectory color

was white or not, image processing didn’t affect position

controller. In random variable discrete strip trajectory

experiment, vehicle speed fluctuated and position

(a) (b)

Fig. 8—Position control: a) Input membership function; b) Output membership function


a)

b)

Fig. 9—Experimental result of fuzzy speed controller at: a) Low speed; b) High speed

Fig. 10—Experimental result of fuzzy speed and position controller on variable trajectory


controller has not been affected excessively.

Conclusions

Autonomous mobile robot was developed with control

of speed and position on variable trajectory using CCD

camera and optical sensors in conjunction with fuzzy edge

detection and line extraction algorithms. Position controller

was observed not to depend on image processing

algorithms, because position controller has been operated

independently from speed controller and run very quickly

using microprocessors based optical sensors mounted on

vehicle. Optical sensors continuously record vehicle

position status in the memory, even if vehicle losses its

Fig. 11—Experimental result of fuzzy speed and position controller on green color trajectory

Fig. 12—Experimental result of fuzzy speed and position controller on variable and discrete trajectory


trajectory, it can find its trajectory again by these recorded

data. In image processing stages, fuzzy edge detection

and position controller has not been found affected unless

trajectory tracked color is not close to black. Also, dark

and light scenes, does not affect fuzzy edge detection

control. Using headlamps mounted on mobile robot,

vehicle can be guided in dark conditions. With wireless

image transferring and remote controllability of vehicle,

various applications can be carried on: i) Security and

monitoring applications with the capability of wireless

image transferring, Internet base long range remote control

and short range remote controlling by PC; ii) Material

transfer applications; iii) Measuring radiation, magnetic

field or harmful chemical gases; and iv) Useful for bomb

destruction and mine sweep vehicle.

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