Application of Fuzzy Control to a Sonar-Based Obstacle ... - CiteSeerX

TA4-1

Proceedings of the 2000 IEEE International Conference on Control Applications Anchorage, Alaska, USA • September 25-27, 2000

9:30

Application of Fuzzy Control to a Sonar-Based Avoidance Mobile Robot

Obstacle

Siripun Thongchai and Kazuhiko Kawamura Intelligent Robotics Laboratory Department of Electrical Engineering, Vanderbilt University, Nashville, TN 37235 {thongcs, kawamura}Ovuse, vanderbilt, edu Abstract This paper describes how fuzzy control can be applied to sonar based obstacle avoidance of HelpMate mobile robot. Behavior-based fuzzy control for HelpXlate mobile robot was designed. The design and implementation of fuzzy control system is described. The fuzzy controller provides the mechanism for solving sens(,r data fi'om all sonar sensors which present differ([ ab all individual high priority behavior. Usint4 I)(~havior-based solves architecture of behavior selection problem. The highest level behavior is called task-oriented behavior, which consists of two subtasks, wall t'ollowing and goal following. The lower level is t)bstacle avoidance behavior. The lowest is an emerg('n('y behavior. Visual Basic 6 code was developed for implementation. The fuzzy inference system was crear(-,d. Helpmate obstacle avoidance was implemented. The result shown that each behavior is work correctly. The HelpMate robot can avoid all obstacles that are det(-,cted by sonar sensors.

r

I

Control [4]. Each fuzzy controller for Hell)hlata.ch,'s provide high capability, while limiting the c~>mph'xitv of the individual modules. These approaclws cmLI}{, implemented and tested independently. Th(, syst(,m architecture in this application is based on 1)ehax'iorbased approaches which has three levels. The, high,~st level behavior is called task-oriented behavior whi(:h consists of two subtasks, wall following and goal f~,llowing. The lower level behavior is an obstacl~,-avoidinK behavior and the lowest is an emergency behavior.

The basic needed for all autonomous mobile robots is (>t}stacle avoidance ability that helps mobile robots m()v(, without collision in unmodified environments. l:/tr{cso,,, raT6ge .fit~.der.s can be used to avoid collision with mlexl}ected obstacles [1]. Sonar sensors are ultrasonic sensors which measure the time elapsed between tlw transmission of a signal and the receiving of an echo of the transmitted signal (time of flight) to determine tlw distance to an obstacle [2]. HdpMate is a lnobile robot which has SOlmr sensors, lidar sensor, stereo color vision, etc [3]. The data from so,mr sensors is the distance between the sonar and ob,}ect. which is adjusted and given directly to the fuzzy input. Therefore, the sonar sensor locations are very imI)ortant. The sonar sensor arrangement for HelpMate is shown in Figure 1. The fuzzy controller is based on the knowledge and exl)(~rience of human operators, known as Intelligent

IEEE

-i

Figure 1: The sonar arrangment for Hell)Mat(-'.

1 Introduction

0-7803-6562-3100510.00¢)2000

.,,,=

2 Fuzzy Controllers Design Fuzzy control is derived from the .frizzy logic mM ./:,zz.q set theory that were introduced in 1965 by L. A. Za(h,h of the University of California at Berkeley :7/. Tlw al,plieation of fuzzy control can be applied ill mmlv disciplines such as economics, data analysis, en~im,¢~l'ing and other areas that involve a high level of uncertainty. complexity, or nonlinearity. Examples of (>thor (:()ntrc)l

425

• Product-operation rule of fuzzy implication:

techniques used with fllzzy controllers are sliding mode c~mtrol, gain scheduled c.ontrol, adaptive control, etc.

i l[91{ 0l.

l-gArB (X, y) = # A (;C)#B (,if)

A control system based on the traditional Proportional, Int.egral, and Derivative (PID) technique may implement low-level behaviors. A high-level behavior may require intelligent control techniques such as fuzzy control. Fuzzy control can be applied for autonomous mobile robots which have complex control architectures. The application of the robotic reactive control approaches, particularly in the layered control system, are presented 1)3" Saffiotti, et al. [111. The fundamental of fltzzy control can be found in Palm, et al. !81. Passino and Yurkovich [12], Wang [10], and others.

(4)

2.3 D e f u z z i f i c a t i o n There are m a n y methods which can be used t~r c,)nverting the conclusions of the inference m e c h a m s m int~) the actual input for the process or for the plant. Center o.f Gravity (COG) defuzzi.ficatio,, m~'th~M is defined as equation (5). This procedure, als,, (.alh,cl Centroid m e t h o d or Center of Area, is the u,o,l provalent and physically appealing of all the defuzzification methods. #A (Yz) ~

2.1 ~ z z i f i c a t i o n F, zzificatio'n is defined as the mapping from a realvalued point to a fuzzy set. In most fuzzy decision syswms, non fllzzy input d a t a is m a p p e d to fuzzy sets Iw treating thent as singleton membership function, Gaussian membership functions, triangular membership function, etc. The Gaussian fuzzification maps .c* ~ U into fuzzy set A' in U has the following Gaussian membership flmction:

....exp

/J

COG, y - ~=t i=1

Other defuzzification techniques, C~ ,tcr-A.~,~ r,!l~ defuzzifieation, Maximum defuzzi.fieatio,, ew.. can be found in m a n y textbooks such as Passino aml Yurkovich [12], and Wang [10]. The combination result of the fuzzy lot4ic s\stems with centroid defuzzification (5), producl-infl,rence rule, singleton fuzzifier, and Gaussian nwnfl~ershil~ function (1) can be obtained as equation (6).

(1)

E ~l a~ exp f (x) = l=1 [j=l

where ch... rr,, are positive parameters and the t-norm , is usually chosen as algebraic product or ,tin.

E

((5',

-- \

,

3 Behavior-Based Approaches There are many" applications of behavior-based al)proaches that have been presented during the last ti,w years [6][13}. Intelligent mobile robot c, mtlC,1 (all ,>,, nmltiple behaviors to generate several contr, fls in lh,. systems. Kasper, Fricke, and P u t t k a m e r hay,, roccm h presented the application of behavior-based ~,mt rol I~\ using R B F - a p p r o x i m a t i o n and neurM cell s t r u c t u r e s [13]. The results showed t h a t mobile rol~ots ~:an learn from demonstration. Yen and Pfluger [14] used a (,>rem a n d fusion m e t h o d for combining outputs of mulliple behaviors proposed by Payton aim t:{os(ml,latt ]15 I. The research behavior-based topics for lnol,ih, robots are still being developed by man?" researchers. The behavioral architecture in this paper is base(l on fuzzy control. The behavior-based fuzzy control (>t Helpmate consists of several behaviors. Each I,ehavior represents a concern in mobile robot control and relat e,~ sonar sensor data, robot status d a t a and Koal information to control robot. A simple architecture is .,hown in Figure 2.

f()l'lllS:

(2)

There are three types of fuzzy rule-based models fl~r function approximation: Mmndani model, TakagiSugeno-Kang (TSK) model, and Kosko's additive model (SAM). The following interpretations are used for the fuzzy I F - T H E N rule. • Mil,-operation rule of fuzz?' implication: # A - - . (.r, y) = min {/*a (m), pB (y)}

I-I a~exp

--

l=1 1_i=1

2.2 Inference E n g i n e Ftlrgg9 lr~]erence Engine is used to combine the fuzzy 1F-THEN in the fuzzy r~le-based and to convert input information into output membership functions. An inference mechanism emulates the expert's decisionmaking in interpreting and applying knowledge about how ~o perform a good control [12]. This can be implemented as a fuzzy rule-base. The rules may use tD, experts experience and control engineering knowledt~e. Fuzzy rule base consists of a collection of fuzzy I F - T H E N rules, which can be shown as the following R ' : IF 3' 1 is F~ C~ .... "~ .r,~ is F,l~ T H E N y is G'

(5)

**a (>)

(3)

426

cess to all sensor readings a n d processes its own (:omm a n d t o control the mobile robot, e.g. I 1 6 / 1 7 . The, final c o m m a n d is dependent on the priority of (,ach I~e,havior. T h e design p r o c e d u r e of a fuzzy ('~mt rol for ol~stacle avoidance is shown in the following st-ps. ObstaCle a~aid~rnce

4.1 Feature E x t r a c t i o n T h e r e are 10 sonars in the front and 4 sonars each on the left a n d the right, respectively. All sonar ~[ata mus~ be adjusted and implemented. After the locatkm all(I the direction of each sonar are known, a new sequent(' is provided in Table 1.

]:'nvlro/msc, nt

Figure

Table 1: I n p u t F e a t u r e for all soums.

2: Behavior-based fuzzy control architecture for obstacle avoidance

3.1 E m e r g e n c y B e h a v i o r

Front-Le~

Front-Right

Left

Right

Xll

89

X21 ~

85

3 : i 6 ~- ~'414

• 1",2 (;

S10

X22 =

86

X 1 7 ~-~ 5317

.~"27 --~ "~1

~

a?12 ~

The first, behavior of HelpMate is an emergency beh,vior, which has a higher priority thml other behaviors. Since this behavior depends on the safety distance, the sonar sensor d a t a is used directly.

X 1 3 ~--- 3 1 1

~C23 ~- 8 7

3218 ~

X14 ~

812

3'24 ~

88

3"19 ~- 8 1 5

Xl5

813

2'25

,'40

~

:=

816

-=

t4 [

,/'2h - - ,~3 .V29 7

'~'2

3.2 O b s t a c l e A v o i d a n c e B e h a v i o r The obstacle o.uoidauce behavior uses sonar sensor data to generate a fuzzy set t h a t represents the distone,' relating to H e l p M a t e ' s location. T h e behavior is o p o r a t e d I)y using the fuzzy controller. The fuzzy inlmt.~ are the fuzzy sets from front-left aim front-right. The live sensors on the front-left and the five sensors ,m the front-right are used for obstacle avoidance behavior. Therefore, five fuzzy controllers are required in this behavior. T h e o u t p u t is the avoiding rotation.

4.2 F u z z y C o n t r o l l e r D e s i g n A fuzzy controller can be designed as follows. 4 . 2 . 1 R e a d sonar d a t a a n d c o n s t r u c t t h r e e m e m b e r s h i p f u n c t i o n s for input: All d a t a fi'om sonar sensors are received a n d displayed. In this llt('r kingineering, Vanderbilt University, Nashvilh,. TN..~Iav 1998.

1¢( R . C . Arkin. Behaviour-Based Robotics, Press, Cambridge Massachusetts, 1998.

[20] M . F . Russo and M. M. Echols, A'utou~,ti,g Science and Engineering Laboratories with l/i.~u.,.l D,.~ic. John Wiley and Sons, 1999.

MIT

[7] L.A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, pp. 338 353. 1965.

430