Hindawi Publishing CorporationApplied Computational Intelligence and Soft ComputingVolume 2009, Article ID 657095, 9 pagesdoi:10.1155/2009/657095

Research Article

ANeuro-Fuzzy Controller for Collaborative Applications inRobotics Using LabVIEW

Hiram E. Ponce, Dejanira Araiza, and Pedro Ponce

Escuela de Graduados en Ingeniera y Arquitectura, Division de Ingeniera y Arquitectura,Instituto Tecnologico y de Estudios Superiores de Monterrey, Campus Ciudad de Mexico, Mexico City 14380, Mexico

Correspondence should be addressed to Pedro Ponce, pedro.ponce@itesm.mx

Received 15 November 2008; Revised 20 March 2009; Accepted 25 June 2009

Recommended by Francesco Morabito

A neuro-fuzzy controller was designed and implemented using LabVIEW over a mobile robotic platform. The controller is basedon fuzzy clusters, neural networks, and search techniques. Also, wireless communication with Bluetooth protocol was used tocommunicate the robot with the controller running in LabVIEW, allowing a simple collaborative task that consisted in pick andplace objects, through knowing the position of the robot and measuring the distance to the objects. The neuro-fuzzy controllerwas split in two parts: the position controller and the evasion controller against collisions.

Copyright 2009 Hiram E. Ponce et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

1. Introduction

Many problems that affect society can be solved usingsoftware and hardware that includes powerful tools fromartificial intelligence. For example, determining the amountof money necessary in a specific bank according to thedemand, having a database to know the most commonsicknesses in Mexican children to have enough medicineand institutions to provide health care assistance, makingdecisions in small businesses, all this means the possibilityto use technological resources to reach development andimprovement of lifes quality among people in differentcountries [1].

For all the mentioned above, the generation of genericand global tools to develop algorithms and programs is achallenging area that deserves the attention of the academiccommunity, as more trained professionals are needed inorder to develop and use the tools to improve situations andto solve problems. This project is focused in using moderntools like fuzzy control, neural networks, computationalsearch, wireless communication, and others, to generatematerial and ideas for implementing solutions. The resultsof it can be extrapolated beyond the task that was selected todevelop the collaborative work, but can be done in any otherfield as examples described in [2, 3].

The main objective of this project includes implementinga collaborative task in a group of microrobots, implicatingautonomous navigation in a determined area, through theuse and development of algorithms. In order to achievethis, the work was divided in three goals: designing andimplementing a neuro-fuzzy controller based on previousworks [4, 5] to get the autonomous navigation inside adetermined area and implementing the collaborative work.

Additionally, some specific objectives were stated: usingLabVIEW [6] to design and implement the algorithms toevaluate their use, designing a collaborative task for themicro robots used in the project, and generating a newstructural design, cheap and flexible, for future projects.

The first steps in the project implicated research aboutthe theory that sustains this work, mainly about fuzzycontrollers, neural networks, fuzzy clusters, and search tech-niques. Subsequently, the efforts were focused in designingthe neuro-fuzzy controller [7], including a model of thesystem to validate the controller, and making the properadjustments to the physical structure of the robots. Finally,implementation of intercommunication was achieved usingBluetooth technology, and the collaborative task wasdesigned, followed by several tests and measurements tovalidate the work.

2 Applied Computational Intelligence and Soft Computing

Fuzzy rules

Inferences DefuzzyficationFuzzyfication

Fuzzy cluster means withoptimized number of

membership functions

Neural networks based on trigonometric series

Inputs Outputs

Figure 1: Proposed neuro-fuzzy controller [4].

2. Prototype Description

Robots used in this project contemplate a 15 15 cm design,with a plastic base, two differential modified servomotors, asimple wheel for support, and different boards including amicroprocessor Basic Stamp 2, three Ping ultrasonic rangesensors for navigation place them one in front and the othertwo at left and right sides, and an Embedded BluetoothTransreceiver for wireless communication, on each robot.This system also includes a magnet for the collaborativetask.

In order to control the avoidance of obstacles andthe movement of the robot, a neuro-fuzzy controller wasimplemented to run over the LabVIEW platform [6], incommunication with the robots through Bluetooth protocol,using a Belkin USB-Bluetooth adapter.

3. Neuro-Fuzzy Controller

A neuro-fuzzy controller is used in robots in order to obtainthe desired movements on them, that is, reaching a finalposition getting from an initial position. Figure 1 is a blockdiagram of the neuro-fuzzy controller proposed. It is basedon trigonometric series [4] and an interface to communicatethe robot with the processing system. In fact, a collaborativework has to be in a real-time-based processing. Then,trigonometric artificial neural networks [4] are implementedbecause their training phase only requires one epoch. Inaddition, this controller allows obstacles avoidance andadaptation of parameters according to interactions betweenthe robot and its environment.

The robot is a MIMO system in which we have tocontrol the rotation of the two wheels via two digital pulsescoming from themicrocontroller. So, it can be divided in twoneuro-fuzzy controllers to simplify the designing of the fuzzycontroller part: one that avoids collision against obstacles,and one to control the position through the motion in theservos, considering a position as the set point.

3.1. Neural Networks. Based on the definition of a fuzzycontroller and its parts, and referring to Sugeno approach inthe inference mechanism [8], neural networks can be appliedto the clusters obtained through the Fuzzy Cluster Means

x Y

c1

c2

wd

we

wn

c10

x0

x0

x0

x0

x0

s1

s1

s2

s3

1

2

1

2

sin

sin

sin

sin

...

...

......

...

Figure 2: Topology of a trigonometric artificial neural network [9].

(FCM) method. Fuzzy inferences of the form if then, and aneural network in the output, can be expressed like

Ri : if x1 is Bi1, . . . , xn is Bin then yi = gi(), (1)

where, yi = gi can be a group of trigonometric functions[4], considering that Fourier series can be used to model theinput in a neural network, and this method is useful to obtainthe coefficients of the weights [9].

Considering a Fourier series defined with (2) andsimilarities which describes neural networks as (3) it makesevident the possibility of its description, where, coefficientsof (2) are the Fourier Coefficients:

f (x) = 12a0 +

n=1(an cos(nx) + bn sin(nx)), =

l,

(2)

Y = f

n

i=0Xii

. (3)

Applied Computational Intelligence and Soft Computing 3

The topology includes two layers of neurons, the firstcomposed by neurons with a trigonometric activation func-tion and a weight that depends directly on the frequency. Thesecond layer adds the trigonometric functions, multiplied bytheir respective weights plus a constant, as Figure 2 shows[9]. The coefficients or weights in the neural network canbe calculated considering if the signal is even or odd. Aminimum squares method is used to find the coefficientsanalytically, through (4) converted in a matrix [9]:

m

i=1ig

(b0, b1, . . . , bp; xi

)sin

(jx

)

=m

i=1iyi sin

(jx

)for j /= 0.

(4)

3.2. Fuzzy Cluster Means. In the inputs, it has been provedthat better results are obtained using themethod called FuzzyCluster Means to adapt and tune membership functionsaccording to the environment surrounding a robot. Thismethod allows the minimization of the distance betweenthe elements of each cluster and the maximization of thedistances that separate the centroids in the clusters. This canbe defined by the objective function in (5).

Neural networks based on trigonometric series can beapplied after obtaining the clusters, to soften the shapesof the membership functions, and to approach them totrigonometric series [4]:

J = 12

N

x=1

c

i=1mx,id

2(zx, vi), (5)

where x,i is the membership value of the element x ={1, 2, . . . ,N} in the fuzzy cluster i = {1, 2, . . . , c}; vi is thecentroid in each cluster; zx, with x = {1, 2, . . . ,N}, is thegroup of data; m is the fuzzyfication value; d2(zx, vi) is theEuclidian distance between zx and vi; N is the number ofsamples in data; c is the number of clusters [10].

3.3. Tabu Search Method. The Tabu search method is aheuristic way to find a best solution combining several strate-gies: descendent method, long and short terms memory, anddiversification strategies. As the search is happening, some ofall the possible solutions are named as taboo and includedin a list through the memories, during a defined number ofiterations. These solutions will not be visited in some pointsof the search; meanwhile better solutions are found to replacean initial proposed solution [11].

An algorithm to find a better set of fuzzy rules than theproposed ones was developed and programmed, and it isincluded in Figure 3.

3.4. Design of Each Controller. The procedure of designing isdescribed in Figures 4 and 5, for both the position controllerand the evasion one.

The final neuro-fuzzy controller can be explained asfollowing.

Proposed solution (best)

Start

Comparing withpermissible solutions (not

taboo)

Update tabu list(memories)

Bestsolution

Current one

New oneUpdate best

solution

Endingcriteria

END

Yes

No

Figure 3: Tabu search algorithm.

(i) Two neuro-fuzzy controllers run parallel: one foravoiding obstacles and one for correcting the positionof robots.

(ii) Each neuro-fuzzy controller has the structure ofFigure 1, in which inputs are fuzzified by member-ships obtained from a fuzzy clustering determined bythe environment and trigonometric neural networks.Then, a set of optimal rules obtained by the Tabusearch method are evaluated in order to obtain adesired fuzzy output. Finally, outputs are computedby trigonometric neural networks. As seen, member-ship functions and rules are changing depending onthe environment, given to robots the characteristic ofadaptation.

(iii) Avoidance obstacles neuro-fuzzy controller uses theleft, right, and center measured distances between therobot and the nearest obstacle. Outputs are the pulsesfor correcting the position of the robot generated bysome obstacle.

(iv) Position neuro-fuzzy controller uses the final posi-tion as inputs and outputs are the pulses for correct-ing the position of the robot.

(v) Avoidance obstacle controller has the highest priority.

4 Applied Computational Intelligence and Soft Computing

Membershipfunctions and

neural network

training (50neurons)

Fuzzy rules

definition

Adjustingparameters

with maximumevaluation

Inputsand

outputsselection

Validationwith the model ofthe plant

Figure 4: Design of the position controller.

Inputsand

outputsselection

Membershipfunctions

through FCMand neural

networktraining (16

neurons)

Fuzzyrules

definition

Adjustingparameters

withmaximumevaluation

Validationthrough

tests

Figure 5: Design of the evasion controller.

(0.5, 1.5) m(1.25, 1.25) m

(1, 1) m

(1.25, 0.5) m

(1.75, 1) m

(0, 0) x

y

2 m

2 m

(1.9, 0.1) m

(0.1, 1.5) m

Figure 6: Fixed parameters for the collaborative task.

4. Collaborative Task

The general idea of the collaborative task selected implies thedetection and collection of small objects through the use ofbasic sensorsultrasonic and infrared sensors. After localiz-ing and collecting the objects, the robots move to depositthem in a certain place, using intercommunication betweenthe computer and the robots to control the movements.

Certain physical parameters have been stated for thecollaborative task: a plane surface with 2 2m per side,15 cm tall walls around the surface, five spherical objectswith metallic incrustations, and half illuminated space.Other specific parameters are 10 cm minimum of separationbetween each object, with a predetermined distribution forthe objects and also for the robots. This is illustrated inFigure 6.

The proposed algorithm for the scenario is shown in aschematic way in Figure 7. The initial position of each robotis called home, and it is the place where the objects will bedeposited after the recollection. In particular, the algorithmwas proved with two robots. Actually, scaling the number of

robots is also possible with the implementation of this neuro-fuzzy controller.

5. Results

5.1. Neuro-Fuzzy Controller. The main result is the genericneuro-fuzzy controller. It was designed based on a Sugenoapproach for generic fuzzy controllers, and it was pro-grammed on LabVIEW [6] as the frame to create a genericmethod that allows to create any kind of neuro-fuzzycontroller only changing basic parameters like the numberof membership functions. This generic controller allows thescalability on the number of robots in the collaborative task.The total controller implemented on LabVIEW is shown inFigure 8.

5.2. Position Controller. With all programs ready in Lab-VIEW [6], several tests were made to verify the positioncontroller error in different trajectories and also to provethe right behavior of the evasion controller using the mostcommon obstacle cases in the proposed scenario.

For the position controller, a linear desired positionresults in the trajectory shown in Figure 9, with an error of5.46%. On the other hand, a diagonal desired position hasthe trajectory of Figure 10 and an error of 6.2%.

The set of fuzzy rules proposed was verified using Tabusearch method, with this set of rules and other selectedrandomly, with the results shown in Tables 1 and 2 for bothcases, respectively.

5.3. Mathematical Model of the Plant. In order to finish thedesign and adjustment of the position control, a mathemati-cal model from the navigation behavior was needed. For this,the servomotors were characterized first, and then a modelthat predicts the position according to the real motion of theservos was developed.

This model describes the relation between the pulsesapplied to the servos and the displacement considering thedirection of the turn. Characterization of servomotors was

Applied Computational Intelligence and Soft Computing 5

More objects?

Determiningpositions of the

objects

Determining distances between home and objects

Ordering objects from nearest to

farthest

Master decides which object is going to pick

Communicationbetween robots

Communicates decision

Picks object

Goes back to home

End of the task

No

Yes

Yes

Object selected= picked?

No

Figure 7: Proposed algorithm for the simple collaborative task.

done. The result of the measures is shown in Figure 11, andthis can be expressed as(6)

= 11.927p + 9.220. (6)

Define the input variable u = [u1 u2]Tas the vectorof machine times that are needed to move the wheels, andthe output variable y = [y1 y2 y3]Tas the position vectorin the 2D navigation plane, with direction angle extensionrespecting from a coordinated plane M in R2 defined in theinitial states.

In general, the action of the plan can be divided intwo main blocks: the angular displacement model and theplacement in theM plane, as Figure 12 shows.

The angular displacement model considers an inputvariable u = [u1 u2]T , which contains the informationabout the activation time in the rotation of each wheel, u1is the activation time in the left wheel, and u2 representsthe activation time in the right wheel. The output variableyd = R in this block is the relative angular displacementof plane R in R2, with its center in the origin GM , mappedreferring to the robot itself. This block determines the relativeangular displacement according to the previous location andthe new position after the movement (Figure 11).

A series of criteria was determined to delimitate theangular displacement model. (a) The robot can moveforward or backward and the vector u has its elements withthe same value. (b) The robot can turn to the left, andu1 = 0, u2 /= 0. (c) The robot can turn to the right, and

6 Applied Computational Intelligence and Soft Computing

xpni

ConexionFinal.vi

Connection IDI1I2

Error outD1

D2Distancia (cm)

DistanciaSensor

90

A

Tabla

eMag

eAng

ControlPosicion.viA

FAMVector de pulsosVector de entrada

If true: controlEvasion ACTIVO If false: controlPosicion ACTIVO

ModeloPlanta.viPos AntPulsos

Posicion actual

RetroalimentaciondePosicion

C2P.vixy

Error IOVectorPolar Vector

m

ControlEvasion.vi

AFAM

Vector de entradaVector de salidas

Posx

MenG.viaxDxG

yDyG

Error IOyDes

xDes0

0PulsoString.viVector de pulsos

I1I2D1D2 Izq

Cen

DerDelta

en

Cen

Der

0.1

Figure 8: Controller developed in LabVIEW (Block diagram).

x

0.4 m

y

Figure 9: Trajectory for a linear desired position.

y

0.6 m

0.6 m

x

Figure 10: Trajectory for a diagonal desired position.

Cycle activation time

An

gle

of d

ispl

acem

ent

in t

he

serv

omot

or

0306090

120150180210240270300330360

0 5 10 15 20 25 30

Figure 11: Measurement of angles in the servomotor.

Angulardisplacement

model

Location inthe plane M

model

TGM ,,=

=

3

2

1

y

y

y

y=2

1

u

uu

xG yG

R

R

Figure 12: Block diagram of the model.

u1 /= 0, u2 = 0. (d) The robot can remain static and thevector u has 0 values. (e) A movement forward takes place ifthe vector is positive. (f) A movement backward takes placeif the vector is negative.

Applied Computational Intelligence and Soft Computing 7

Table 1: Tabu method results for the proposed FAM.

Initial Tabu result

Left pulse Right pulse Left pulse Right pulse

0 10 0 10

0 5 0 5

10 10 10 10

5 0 5 0

10 0 10 0

0 10 0 10

0 5 0 5

5 5 5 5

5 0 5 0

10 0 10 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 0 0 0

0 10 0 10

0 5 0 5

5 5 5 55 0 0 10

10 0 10 0

0 10 0 10

0 5 0 5

10 10 10 105 0 5 0

10 0 10 0

GM

A

B

u1

u2

R

Figure 13: Angular displacement model alter applying a group u ofpulses to the wheels. Point GM represents the point of interest in therobot, and plane R is defined from it. (a) Plane R before the pulseinput, (b) plane R after the movement produced by the pulses.

The absolute displacement depends on the actual posi-tion in plane M; so a point GM is defined as the origin ofEuclidian plane R (Figure 14). Point GM has the directionvalue measured in planeM. Mathematically,GM is a positionvector (7):

GM =[xG yG R

]T. (7)

Table 2: Tabu method results for the random FAM.

Initial Tabu result

Left pulse Right pulse Left pulse Right pulse

0 0 0 0

0 0 0 0

0 0 0 0

0 5 0 5

0 5 0 5

10 0 10 0

10 10 10 10

5 0 5 0

5 0 5 0

10 10 10 10

0 10 5 0

0 5 0 5

5 0 5 0

10 0 0 0

10 0 0 0

5 5 5 5

5 0 5 0

0 5 0 5

10 0 10 0

10 0 10 0

10 10 10 10

0 10 0 10

0 10 0 10

5 0 5 0

5 0 5 0

The transformation between plane R and planeM is doneusing homogeneous matrices [12, 13], where the mapping : R M starts with (8):

= T, (8)

where

T =

cosR sinR xGsinR cosR yG

0 0 1

,

=

0

1

.

(9)

The coordinates in correspond to the new position inplaneM. The new angle of direction is (10):

k+1R =

kR, u1 = u2,kR + R, u1 = 0, u2 /= 0,kR R, u1 /= 0, u2 = 0.

(10)

8 Applied Computational Intelligence and Soft Computing

y

yG

xG

M

M

yR xR

Plane R

Plane Mx

R

GM

Figure 14: Relation between planeM and plane R, both inR2.

Robot

RobotRobot

Robot

30 cm

Robot

40 cm

Figure 15: Different cases for the evasion controller evaluation.

The output vector is defined as y = [y1 y2 y3]T , wherethe values of its element imply (11):

y =

k+1R

. (11)

Twenty observations were taken to validate the proposedmodel.

5.4. Evasion Controller. Four different cases were tested toverify the evasion against obstacles, which are shown inFigure 15 together with the trajectory followed by the roboton each case. The evaluation of this controlleris acceptable.

5.5. Wireless Communication. In order to send and receivethe numerical data from themicrocontroller to the computerand back, a basic protocol was designed. This protocol sentcharacters such as go or hi as the control signal, and thenthe respective digits of the correction signal or the feedback.

To establish and implement the communication throughBluetooth, a routine was designed based on predeterminedblocks including exploration to find the device, sending, andreceiving.

5.6. Designing a New Robot. Searching for a more flexibleand less expensive platform, a new structure was designed

Figure 16: Implementation of the new structural design.

1

2

3

1

2

Figure 17: Trajectories followed by the two robots during thecollaborative task.

Figure 18: A robot taking an object with the magneto.

and implemented as shown in Figure 16, using plasticsand polymers to substitute aluminum and other heaviermaterials. The saving was of 16% in costs and 10% in weight.

5.7. Collaborative Task. Several experiments were made toobserve the performance of the proposed system duringthe collaborative task, and the results of the trajectoriesfollowed by the robots are shown in Figure 17, according tothe algorithm proposed previously. The total time was 17

Applied Computational Intelligence and Soft Computing 9

minutes and 20 seconds. According to the results, time mightbe enhanced by proving other communication protocols andremoving the master station. In that way, robots have tohave a better microcontroller that allows programming theentire neuro-fuzzy controller inany one. On the other hand,Figure 18 shows how a robot is catching an object with themagneto at the front part in this collaborative task. However,the neuro-fuzzy controller has its main characteristic that canbe used in other tasks different from that one, because of itsinherent adaptation to the environment.

6. Conclusions

A neural fuzzy-controller topology was proposed to controlthe autonomous navigation of a robot, and it was imple-mented in LabVIEW [6] to be tested in a collaborativetask. The controller acts as expected, collecting the objectsand evading obstacles. The proposed blocks of algorithmsallowed adjusting parameters as many times as it wasnecessary, and the whole methodology gave a possibility toextrapolate the results to other situations and tasks. Thefinal prototype of the generic neuro-fuzzy controller wasprogramming on LabVIEW, and it can be extended to othersituations [2, 3].

References

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[2] R. Ranjan, A. Awasthi, N. Aggarawal, and J. Gulati, Applica-tions of fuzzy and neuro-fuzzy in biomedical health sciences,in IEEE International Conference on Electro Information Tech-nology, pp. 6065, East Lansing, Mich, USA, 2006.

[3] P. P. Bhogle, B. M. Patre, L. M. Waghmare, and V. M. Pan-chade, Neuro fuzzy temperature controller, in Proceedingsof the IEEE International Conference on Mechatronics andAutomation (ICMA 07), pp. 33443348, 2007.

[4] P. P. Cruz, et al., A Novel Neuro-Fuzzy Controller Based onBoth Trigonometric Series and Fuzzy Clusters, Tecnologicode Monterrey, Mexico, 2006.

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[7] K. S. Rattan and G. S. Sandhu, Design of a proportionalplus derivative neuro fuzzy controller, in Proceedings of 18thInternational Annual Conference of the North American FuzzyInformation Processing Society (NAFIPS 99), pp. 874878,New York, NY, USA, 1999.

[8] H. Nguyen, et al., A First Course in Fuzzy and Neural Control,Chapman & Hall, Boca Raton, Fla, USA, 1st edition, 2002.

[9] P. P. Cruz and R. S. M. Suarez, Neural Networks Based onFourier Series, Tecnologico de Monterrey, Mexico, 2005.

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