behavior control of a new designed mobile robot based on ... · behavior control of a new designed...

International Journal ofIntelligent Engineering & Systems

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Behavior Control of a New Designed Mobile Robot Based on Fuzzy Logic andNeuro Fuzzy Approaches for Monitoring Wall

Chokri Abdelmoula∗, Hanen Rouabeh, Mohamed Masmoudi

EMC Research Group, National Engineering School of Sfax, Sfax, Tunisia∗ Corresponding author’s Email: [email protected]

Abstract: This work describes the design and development of controllers based on artificial intelligence appliedto a newly designed mobile robot type-vehicle to control behavior for monitoring wall. Two approaches have beendeveloped and optimized to achieve this task. The first one is based on Fuzzy logic. This control algorithm combinesdifferent sensory information and provides a suitable control command allowing the mobile robot to follow the walldeviations. The second approach consists of the application of a hybrid-type Neuro-Fuzzy ANFIS controller for thesame task. An important feature of this approach is that the controller combines the advantages of both Fuzzy logicand Neural Networks.The simulation results are presented and implemented with VHDL using ANFIS architecture.

Keywords: Mobile robot; Wall following; Fuzzy logic; Neural networks; Neuro-fuzzy; ANFIS

1. Introduction

In the last decade, the control of wheeled mobilerobot motion has attracted an increasing interest a-mong researchers. The evolution of electronic andcomputer systems has introduced a very important pro-gress in autonomous mobile robots researches. Mo-bile robots are equipped with a set of sensors for per-ception and a set of actuators for motion and reaction.Navigation tasks of these robots can be controlled bythe use of many command techniques. Among these,we note the advanced techniques based on intelligentalgorithms. These algorithms based on artificial in-telligence are used to solve problems of navigation,obstacle avoidance, intelligent parking and wall fol-lowing behavior.

This paper discusses the optimization of a Fuzzycontroller for the purpose of improving wall followingbehavior of a newly designed mobile robot. Firstly,the Fuzzy Logic controller was designed and simulat-ed. Then, an optimized hybrid controller is developedusing a Neuro-Fuzzy intelligent algorithm based on

the Adaptative Neuro Fuzzy Inference System. Thiscontroller was designed and tested. In the final step,the obtained ANFIS architecture was implemented onFPGA using VHDL language.

In the literature many studies have focused on intel-ligent control of mobile robots. Researchers present-ed in [1, 2, 3, 4] have proposed various methods tocontrol navigation tasks using different intelligent al-gorithms. Many extensions have been carried out onwall following behavior [5]. Farooq.U et al [6] havedesigned a fuzzy logic based intelligent controller forwall following behavior. They used three ultrasonicsensors mounted on left side of the robot. The fuzzylogic controller allows the robot to follow wall on itsleft side at a set distance. The inputs of the controllerare the sensory data and outputs are motor command-s of the robot. Leehter Yaho and Yuan-Shiu Chen[7] have designed a type-2 fuzzy wall following con-troller for automated guided vehicle. The proposedcontroller is suitable for vehicles that use sonar sys-tem to measure distance to the wall. They stated that

International Journal of Intelligent Engineering and Systems, Vol.6, No.3, 2013 17

the type-2 fuzzy controller solved the noise problempresented by the sonar-based distance measuremen-t. The parallel parking task has also attracted a greatinterest among researchers. Panomruttanarug.B et al[8] have presented a fuzzy logic based on autonomousparallel parking system. They used four infrared sen-sors for distances data acquisition. The distances areused as inputs to the fuzzy rules in order to calcu-late a proper steering angle while backing to achievethe parking process. The experimental results demon-strated the effectiveness of their approach. Zhi-LongWang et al [9] proposed an autonomous parallel park-ing controller based on Neuro-Fuzzy approach for acar-like mobile robot. Their approach is based on theuse of ultrasonic and compass sensors, mounted onthe robot chassis, to decide on the turning angle. KDemirli and M Khoshnejad [10] have also developeda neuro-fuzzy model for autonomous parallel parkingof a car like mobile robot. They have focused on thecase when the parking space dimensions cannot be i-dentified. The proposed approach uses the data fromthree sonar sensors mounted in the front left and rightcorner of the car to decide on the turning angle.

There are many other researches focusing on the ob-stacle avoidance task and navigation of mobile robotsin crowded environment. Raguraman.S.M et al [11]have developed a fuzzy logic controller for mobilerobot navigation in an indoor environment. The fuzzysystem is designed with two basic behaviors- obstacleavoidance and a target seeking behavior. The inputsto the fuzzy logic controller are the desired directionof motion and the readings from the sensors where-as the outputs are the accelerations of robot wheels.Singh.M.K et al [12] used an adaptative Neuro-FuzzyInference System to control the navigation of a mobilerobot in a dynamic environment. A learning algorithmbased on neural network technique has been develope-d to tune the parameters of fuzzy membership func-tions, which smooths the trajectory generated by thefuzzy logic system. Using the developed ANFIS con-troller, the mobile robots are able to avoid static anddynamic obstacles, and reach the target successfullyin cluttered environments. Khaldoun K. Tahboub andMunaf S. N. Al-Din [13] have also developed a neuro-fuzzy reasoning scheme for mobile robot navigation.Their approach is based on decomposing a multidi-mensional fuzzy system into a set of simple paral-lel neural networks. The main advantage consists inreducing the number of if-then rules by introducingweighting factors to the sensor inputs. Four simpleneural networks are used to determine these factors.

Each neural network is responsible for determiningthe weighting factor for one sensor input. Simula-tion results of this approach demonstrated the meritsof the proposed system. Aleksandar Rodic et al [14]have presented a sensor-based intelligent mobile robotnavigation control scheme. Their approach deals withcombined, fuzzy and dynamic control of autonomousmobile robots and their motion in unknown environ-ment with different obstacles. A detailed descriptionof the proposed fuzzy inference system structure ispresented and some implementation aspects are de-scribed as well as experimental control verification.

Many studies have been conducted on the hardwareimplementation of intelligent algorithms for mobilerobot control. Chan Zhi and Muhammad Nasirud-din M. [15] have presented an obstacle avoidance pro-gram for mobile robot that incorporates a neuro-fuzzyalgorithm using Altera Field Programmable Gate Ar-ray (FPGA) development board. A mobile robot servesas the test platform of the program. An ultrasonic sen-sor and servo motors are used as the test platform’ssensing element and actuator, respectively. The neuro-fuzzy algorithm is successfully implemented on theAltera FPGA development board. A VHDL imple-mentation of an intelligent navigation system usinghybrid fuzzy controllers has been proposed by NianZhang et al in [16]. Two parallel fuzzy logic con-trollers are utilized to conquer the major mobile robotnavigation challenges in narrow concave U-shaped ob-stacles, and deep narrow passage, and to track a targetin an environment with varying obstacles. The result-s demonstrate the suitability of the FPGAs in controltasks for multiple fuzzy logic controllers. The imple-mentation of the control units for the robot with FPGAis an important approach in the way for building a realprototype.

A solution to overcome limitations of fuzzy con-troller (non-existence of a standard method for ex-tracting the Fuzzy rules) will be presented in this work.This problem can be solved by using the learning char-acter of Neural Networks combined with Fuzzy infer-ence system. To achieve this, we proceeded to developan optimized hybrid controller using a Neuro-Fuzzyapproach based on ANFIS architecture.

The remainder of this paper is organized as follows.Section 2 presents the kinematics modeling of the mo-bile robot. Section 3 illustrates the optimized con-trollers based on Fuzzy and Neuro Fuzzy mobile robotcontrol. Section 4 gives explanatory details about theANFIS hardware implementation. In Section 5, dif-ferent results are explored and discussed. A summary


of the key points concludes the paper in section 6.

2. Kinematic Modeling of the mobile robot

This work is developed for a vehicle -type mobilerobot [17]. The rear wheels are fixed and the frontones are the steering wheels which are responsible fordirection change. The movement of the robot is con-trolled by the steering angle ϕ of the front wheels. Thekinematics modeling of the robot in the environmentis shown in Fig. 1.

The frame R (O, X, Y) is related to space navigation.Point M ( XM,YM ) is located at the middle of the twofront wheels θ is the angle of orientation relative tothe X axis.

Figure 1 Kinematics modeling of the mobile robot

The motion of the robot in the R frame is describedby:

dXM =vcos(θ)dYM = vsin(θ)

θ = vsin(ϕ)L

(1)

Where the velocity v is constant and equal 0.2 m/s.ϕ is the steering angle and L the distance betweenfront and rear wheels axis. By the discretization ofthese equations using the Euler method, the kinemat-ics model becomes:

XM(k+1)=XM(k)+h · v · cosθ(k)YM(k+1)= YM(k)+h · v · sinθ(k)

θ(k+1)= θ(k)+h · v·sinϕ(k)L

(2)

Where h is the sampling step.

3. Fuzzy and Neuro-Fuzzy Robot Control

3.1 Development of the fuzzy controllerThe used Fuzzy controller for the mobile robot wall

following behavior takes as inputs the two variablesE d and E theta shown in Fig. 2. In order to calcu-late these variables, we use two infrared sensors Cd1and Cd2 installed on the right if the followed wal-l is on the right and vice verse. The different valueDd1 −Dd2 and d are used to calculate the orientationwith the wall E theta (where Dd1 and Dd2 present re-spectively the distance values returned by sensors Cd1and Cd2, and d the distance between the two sensors).The error on the desired distance E d is also calculat-ed using these values.

E theta = tang−1 (Dd1 −Dd2)

d(3)

E d = Dd1 −D desired (4)

Figure 2 The input/output variables of the controller

The first step in designing the Fuzzy controller isthe variables partition into Fuzzy sets. For the twoinput variables the number and type of membershipfunctions are fixed as five triangular functions shownin Fig. 3 and Fig. 4. This distribution is based on theknowledge of the system and the desired accuracy.

The linguistic variables for both variables are noted:NB (Negative Big), NS (Negative Small), ZE (Zero),


PS (Positive Small) and PB (Positive Big). The outputϕ is partitioned into five singletons. It is representedby five linguistic variables: NB (Negative Big), NS(Negative Small), ZE (Zero), PS (Positive Small), PB(Positive Big). Fig. 5 shows the partition of the outputvariable ϕ .

Figure 3 Fuzzy sets of the variable E theta(rad)

Figure 4 Fuzzy sets of the variable E d(m)

Table 1 presents the Fuzzy rules that describe thebehavior of the robot. There is no general methodto extract these rules and their determination is basedon both human experience and the conditions of thesystem to be controlled. We can distinguish differenttypes of rules as follows:

If(E d is NB ) and (E theta is NS) then (ϕ is PB)

3.2 Application of ANFIS for the wall followingbehavior

The use of Fuzzy logic to control the mobile robotshows good results. However some weaknesses arisefrom the difficulty of obtaining knowledge from a hu-man expert. To overcome this difficulty we decided to

Figure 5 The output variable angle ϕ (rad)

Table 1 Fuzzy rules

E dNB NS ZE PS PB

Eth

eta PB ZE NS NS NB NB

PS PS ZE NS NS NBZE PS PS ZE NS NSNS PB PS PS ZE NSNB PB PB PS PS ZE

ϕ

introduce the extracting knowledge concept withoutthe need of expert. Automatic extraction of knowl-edge is based on the Neural Networks learning char-acter. Thus appears the Neuro-Fuzzy approach thatcombines the advantages of both techniques.

In our work we have optimized a hybrid controllerbased on Neuro-Fuzzy approach called ANFIS (Adap-tive Neuro Fuzzy Inference Based System). This ar-chitecture exploits the Sugeno Fuzzy model known asTSK (TSK Fuzzy model) proposed by Takagi, Sugenoand Kang [18] based on 5-layer neural network archi-tecture. Each layer is designed to achieve a step in theFuzzy inference system.

The concept of the wall following ANFIS controlleris the same one used with the Fuzzy controller. TheANFIS network obtained is shown in Fig. 6.

This network has five layers:The first layer: Consists of 6 neurons that classifyeach of the two input variables E d and E theta in-to three Fuzzy sets, using ≪gbellmf≫ membershipfunctions. Each neuron calculates its activation, whichis equal to the degree of membership of the input ele-ment to a Fuzzy set.The second layer: Consists of nine neurons, eachneuron represents a Fuzzy rule and calculates its ac-tivation by a simple product. The outputs are Wi with


i varying from 1 to 9.The third layer: Represents the normalization layer,consisting of 9 neurons, each one calculates the corre-spondent normalized weight. The outputs are Si withi varying from 1 to 9 and given by the following equa-tion:

Si = wi/9

∑i=1

wi (5)

The fourth layer: Consists of 9 neurons, used to cal-culate the consequences of the rules as follows:

fi = Si(piE d +qiE theata+ ri) (6)

With pi,qiand ri represent the consequent parame-ters.The fifth layer: This layer comprises a single neuronthat computes the output of the ANFIS through thefollowing sum:

Out put =9

∑i=1

f i (7)

Figure 6 Architecture of the wall following ANFIS con-troller

The ANFIS training is achieved in two steps. Thefirst stage is designed to adjust and calculate the con-sequent parameters using least square error algorith-m by keeping the membership functions parameter-s fixed. The second one is designed to adjust andcalculate the membership functions parameters withthe back propagation algorithm by keeping the conse-quent parameters fixed [19].

The input variables are partitioned into three Fuzzysets using the gbellmf function, which is described bythe following equation:

F(x,a,b,c) =1

1+ | x−ca |2b (8)

The distribution of these variables before and afterANFIS training for variable E d is shown respective-ly in Fig. 7 and Fig. 8 Fig. 9 and Fig 10 show thedistribution of E theta variable.

Figure 7 Variable E d before learning

Figure 8 Variable E d after learning

After optimizing the architecture using ANFIS con-troller, results are summarized and presented respec-tively in table 2 and table 3. Table 2 shows the twovariables E d and E theta membership functions pa-rameters before and after learning. Table 3 lists therules consequent parameters obtained after the learn-ing operation.

The ANFIS learning phase is done with a databaseof 1870 examples, the error obtained for this phaseafter 5000 iterations is about 0.0095964. A databasecomposed of 300 different examples is used to testthe efficiency of the obtained architecture. The errorobtained for testing phase is about 0.010132, which isan acceptable error.


Figure 9 Variable E theta before learning

Figure 10 Variable E theta after learning

Table 2 Membership functions parameters before and afterlearning

Parameters Parametersbefore learning after learninga b c a b c

Ed

0.175 2 -0.45 0.3306 2.058 -0.47160.175 2 0.05 0.2499 2.159 -0.03440.175 2 0.35 0.1094 2.168 0.4988

Eth

eta 0.3927 2 -0.983 1.288 1.628 -1.549

0.3927 2 0 0.8291 1.921 0.16410.3927 2 0.883 0.7576 2.387 1.453

Table 3 The consequent parameters obtained after learning

p q rS1 8.658 3.431 3.698S2 -4.975 -1.94 -1.309S3 -1.097 -0.4317 0.03616S4 -2.219 -0.6785 -0.322S5 0.3801 -1.524 -0.1554S6 -6.489 -5.768 5.892S7 -9.963 2.529 7.701S8 5.054 -0.6862 -4.072S9 -24.91 0.9169 19.04

4. Hardware Implementation of the ANFIS

After setting all parameters of the ANFIS architec-ture, we implement it on FPGA with VHDL languageusing the Altera Quartus II environment. In order toobtain an optimized architecture, we choose the da-ta encoding type which ensures the necessary accura-cy. The natural numbers between 0 and 2n−1 coded innatural binary with n-bit, and negative integers encod-ed in two’s complement. For real numbers we havechosen floating point encoding. We used the float-ing point representation normalized by the standardIEEE754 single precision with 32-bits.

Five blocks are created to implement the ANFIS.Block 1: Fuzzification block, it has six ROM typememories that contain the discretization of the six mem-bership functions.Block2: Rules layer’s block, it makes a simple prod-uct between the two inputs to give the output weightsWi.Block3: Normalization block takes as input the weight-s Wi and gives as output the normalized weights, de-scribed by equation (5).Block4: Presents the fourth network layer, it is re-sponsible for calculating the rules consequences byequation (6). All calculations are performed by theIEEE 754 floating point representation.Block5: Calculates the output ϕ by the sum of fi asrepresented by equation (7).

The complete block diagram of the ANFIS networkused is shown in Fig. 11.

For the implementation we have used a cyclone IIIEP3C120F7807 FPGA. The compilation summary ofthe presented ANFIS VHDL prototype is described inTable 4.


Figure 11 Complete architecture of the ANFIS network

Table 4 The ANFIS hardware resources

Total memory bits 601.859/3.981.312 (15 %)Total pins 120/532 (23%)

Total logic elements 80.893/119.088 (68%)

5. Simulation Results

This section presents the MATLAB simulation re-sults for both controllers. The Fuzzy controller wastested as an example where the followed wall doesn’tcontain any deviation. The robot direction is parallelto the wall but the distance between them is greaterthan the desired distance. The ANFIS controller wastested for a more complicated form of wall that con-tains many deviations.

5.1 Simulation results of fuzzy controllerFig. 12 and Fig. 13 show respectively, the trajectory

and the output steering angle generated by the Fuzzycontroller for an example where the initial parametershave the following values: X=0, Y=0 and angle θ =π/2.

The robot’s initial position is characterized by theangle θ = π/2( θ angle of orientation relative to theaxis (O, X)) and a zero angle with the axis (O, Y), sorobot’s direction is parallel to axis (O, Y). In additionto that, the followed wall is parallel to this axis, so wecan say that the robot’s direction is parallel to the wallbut the distance between them is greater than the de-sired distance D desired = 0.4 m. As a consequencethe robot will move forward by turning right to reachthe desired distance. When approaching this distanceit begins to rotate in opposite direction, so the angle ϕwill increase in the negative direction when the robotapproaches the desired distance. This angle will re-

Figure 12 Robot trajectory by the fuzzy controller

Figure 13 Variation of the angle ϕ (rad)

turn gradually to zero until it keeps a direction parallelto the wall where the angle is zero for parallel followwith the desired fixed distance which is 0.4m.

5.2 Simulation results of ANFISAfter training the operation of the ANFIS network

and setting the parameters of membership function-s and consequent parameters, we tested the networkwith a new test database containing 300 examples.The parameters of the initial robot’s position are: X=0.2 m, Y=0m and angle θ = π/2, the wall to be fol-lowed is at a distance of 0.6 m from the robot. Thisdistance is greater than the desired distance which is0.3m. The wall has 3 deviations, so the robot mustfollow these deviations keeping the desired distancefixed. The results are obtained adequately to the de-sired movement in the environment of the mobile robotand are presented respectively in Fig. 14 and Fig. 15.


Figure 14 Robot trajectory by the ANFIS controller

Figure 15 Variation of angle ϕ (rad)

5.3 The ANFIS testing and validation with VHDLTo check the validity of the ANFIS architecture im-

plemented with VHDL language, a functional simula-tion is carried out. We compared the theoretical valuesgiven by the MATLAB program with those given byVHDL. We take the following values as an example ofthe input values to test the ANFIS controller in MAT-LAB:

E d = 0.121 et E theta = 0.225. The value of thecorresponding output generated by the controller inMATLAB is ϕ = - 0.262.

Then we validate this result by simulating the imple-mented architecture of ANFIS with the same inputs.The simulation results are presented in Fig. 16.

The value of ϕ obtained by the VHDL simulation is:ϕ =10111110100001011011001001111110This value converted to real number with single pre-

cision is: ϕ = -0.2611274.The two values of ϕ obtained by MATLAB and VHDL

are very close. Table 5 presents some other valuestested and compared with the theoretical values:

Figure 16 Simulation results of ANFIS controller

Table 5 Examples of test

Theoritical VHDL

ϕ ϕ

values values

E d = 0.38 -0.141 -0.1413377

E theta =−0.12 1011111000010000101110

1011010100

E d =−0.122 -0.112 -0.11199709

E theta = 0.23 1011110111100101010111

1010111011

E d = 0.753 -0.213 -0.21320324

E theta = 0.17 1011111001011010010100

0111110011

E d =−0.35 0.48 0.4803706

E theta =−0.175 0011111011110101111100

1100100011

5.4 DiscussionThe optimized Fuzzy controller shows good result-

s for different forms of wall as presented previously.Fuzzy logic shows ability to formalize and simulatethe operator’s expertise for robot control. It allowstaking into account the different variables of the prob-lem. However, it is limited by some disadvantagessuch as the non-existence of a standard and a system-atic method to transform the experiences of an expertto a base rule, with no general procedure for choos-ing the optimal number of rules. The optimized con-troller requires much time to set the suitable numberand the more efficient type of membership function-s and Fuzzy rules for wall following behavior con-trol. The use of the hybrid-type ANFIS control sys-tem for monitoring wall allows the automatic extrac-


tion of knowledge without the need of human expert.The ANFIS combines the features of Fuzzy logic withthat of Neural Networks. The learning character ofthese networks has to adjust the membership func-tions parameters and rules consequences parametersto get an optimized architecture. The simulation ofthe resulting ANFIS controller shows efficiency andperformance in the robot control. Compared with theFuzzy controller, the ANFIS gives effective resultswith 3 Fuzzy sets for each of the two inputs and 9Fuzzy rules, instead of 5 Fuzzy sets and 25 rules forthe Fuzzy controller. The character of learning canlead to an optimal architecture more easily and with-out wasting much time for the formalization of Fuzzyrules.

6. Conclusion

This work describes the optimization and the devel-opment of Fuzzy logic and Neuro-Fuzzy based con-trollers for an operation of monitoring wall applied toa newly designed mobile robot. The Fuzzy controllerallows the robot prototype to follow the wall devia-tions, keeping a desired fixed distance with respect toit. The simulation results of this controller are satis-factory, but it is not possible to avoid long time to setthe architecture that best meets the problem. As a so-lution, a Neuro-Fuzzy control system was applied tothe same task. A hybrid-type ANFIS controller is de-signed and tested. The test results show more efficien-cy and performance when using this type of control.The ANFIS architecture obtained was implementedusing VHDL on FPGA. The implementation on FP-GA has been chosen based on its low cost, reliabilityand reconfigurable characters. The use of these twocontrollers shows efficiency in laboratory application;through the major advantages they possess particularNeuro-Fuzzy approach that combines the advantagesof both techniques.

This application is a part of our work in developingautonomous mobile robot navigation systems. Ourperspectives are oriented towards developing a multi-sensory based architecture for on road intelligent ve-hicles navigation.

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