a fuzzy local path planning and obstacle avoidance for ... · this paper presents a local fuzzy...

A Fuzzy Local Path Planning and Obstacle Avoidancefor Mobile Robots

H.Aminaiee

Department of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

Abstract

This paper presents a local fuzzy pathplanning and obstacle avoidance method basedon fuzzy logic. The main idea is to fuzzify theobstacles in the environment and use a fuzzylogic controller to guide the robot not to movetoo close to the obstacles. The human sense ofobstacles and his behavior in obstacleavoidance is provided the Fuzzy Obstacleconcept which is used in the obstacleavoidance unit.

The advantage of local path planningapproaches is their short response time andability to be used in real-timeimplementations. But these methods sufferfrom local minima. This method has decreasedthe probability of having a local minimumcompared to Khatib's potential field method.The new method is easily applicable in non-holonomic mobile robots.

Keywords: Mobile Robot, ObstacleAvoidance, Fuzzy Logic Controller, LocalPath Planning, Artificial Potential Field

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Despite the impressive advances in thefield of autonomous robotics in recent years, anumber of problems remain. Most of thedifficulties originate in the nature of real-world, unstructured environments, and in thelarge uncertainties that are inherent to theseenvironments. Also, the effect of controlactions is not completely reliable [3]. So thereis no need to apply the sensory data exactly in

our computations. Using this data as a fuzzydata in path planning methods, should be agood idea.

Path planning is a well-known and animportant problem in the field of robotics. Theobjective in this problem is to find a collisionfree path between a start and a goalconfiguration in an environment containingstationary or moving obstacles. In recentyears, there has been a great effort on motionplanning of mobile robots. And two majormethods in this field have arisen; Global PathPlanning approaches and Local Path Planningapproaches. Each method has its ownadvantages.

In global path planning approaches, theoptimal path can be obtained. But thesemethods suffer from extensive computationand the requirement of a priori knowledgeabout the environment. These methods can beused in industrial robots which do not need tohave flexibility or autonomy. So these robotsare not able to operate in new environments orto face unexpected situations.

The advantage of local path planning tothe global path planning approaches is theirshort response time and their real-timeimplementations and ability to be used inunknown environments. But they suffer fromlocal minima, limit cycles, and instabilityproblems [4].

The environments of real robots are rarelypredictable or perfectly know. So it does notmake sense to make precise plans beforemoving [3]. In this paper, we have provided anew fuzzy path planning and obstacleavoidance method by which the probability ofgoing to a local minimum trap is decreasedcompared to Khatib's artificial potential field

Proceedings of the 6th WSEAS Int. Conf. on FUZZY SYSTEMS, Lisbon, Portugal, June 16-18, 2005 (pp127-131)

method. The new method is easily applicablein non-holonomic mobile robots.

The paper is organized as follows: Insection 2, the concept of fuzzy obstacleavoidance is illustrated. Section 3, describesthe algorithm proposed for the objectavoidance unit, and shows the fuzzy logiccontroller for it. A brief description of the goalseeking method in the proposed algorithm is inSection 4. The simulation results are presentedin Section 5 followed by the conclusions inSection 6.

Note: In this text, we have added thedimension of the robot to the obstacles. Hencethe robot is considered as a point. See theconcept of C-Obstacles and C-Surface in [1].

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In developing the new strategy, theKhatib's obstacle avoidance method which isbased on an artificial potential field, isinfluenced. Khatib computes an artificialpotential field that has a strong repelling forcein the vicinity of obstacles and an attractingforce produced by the target location. Thesuperposition of the two forces created apotential field, which incorporates informationabout the environment. Following the steepestgradient from a start position, a path can befound that guides the robot to the targetposition avoiding obstacles [5].

Consider a human walking in a crowdedplace. He tries not to collide with peoplearound himself. He pays more attention to thepeople and objects in front of himselfcompared to the ones behind him. And as hegoes away from an object or a person, he paysless attention to it because the probability ofcollision with the object decreases.

The human sense of obstacles and hisbehavior in obstacle avoidance is provided theFuzzy Obstacle concept which is used in theobstacle avoidance unit.

Consider a C-Obstacle that can beexpressed as a set of points in configurationspace. The fuzzy version of this set can bedefined by the following; The membershipfunction of any point in this set is equal to zero

if this point is not belong to any C-Obstacle'sboundary. The membership equals to one,when the point is exactly front of the robot (orin directions which the robot can move) and itdecays as we move away from the robot.

Figure 1 : The Fuzzy Obstacle ConceptThe figure shows that the fuzziness of the

point decreases if the point goes behind therobot or far from the robot

The obstacle avoidance unit is based on afuzzy logic controller. The task of the fuzzyunit is to provide a control function, whichproduces an appropriate motor command fromthe given inputs. The control function can bedescribed as follows: on the one hand thefunction has to lead the mobile robot to itsattraction goal position; on the other hand ithas to force the robot to back up whenapproaching a fuzzy obstacle which conveys arepelling influence. The fuzzy rule-base isbuilt up by using common sense rules.

The inputs to the fuzzy obstacle avoidanceunit are the relative angle and distancebetween the robot and the points on theboundary of the obstacle, and relative distanceand angle between the robot and the goal.

The output variable of the unit is themotor command τ. This command can beinterpreted as an actuation command for therobot's direction motor, and fed to the mobileplatform at each iteration.


Figure 2: The Fuzzy Obstacle Avoidance Unit

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The fuzzy controller in our approach is atwo-level fuzzy controller. In the first level,the relative angle and distance data of thepoint is converted to a fuzzy point, and thisfuzzy point is passed to the second levelproducing the motor command.

(a) Angle from Mobile Robot to the point

(b) Distance from Mobile Robotto the point

(c) Dangerousness of the point

Figure 3 : Fuzzy sets for the Mobile Robot

Each input space is partitioned by fuzzysets as shown in figures 3-a and 3-b. Theasymmetrical triangular and trapezoidalfunctions are utilized to describe each fuzzyset to allow a fast computation.

Table 1 shows and example for the rulesdefined in the first level of fuzzy controller.These rules can be written as sentences withtwo antecedents – relative angle and relativedistance – and one conclusion.

The rules for the second level of theobstacle avoidance unit can be written assentences with two antecedents and oneconclusion. The rule base for the second levelof the obstacle avoidance unit is shown inTable 2.

rule 1: If distance is very far and angle isfar left then point is very safe.rule 2: If distance is very far and angle isquite left then point is quite safe.rule 3: If distance is very far and angle isforward then point is safe.rule 4: If distance is close and angle isforward then point is dangerous.rule 5: If distance is very close and angleis forward then point is very dangerous.

Table 1 : A rule base for the first level of obstacle avoidance unit

of the Mobile Robot

rule 1: If dangerousness is very safe andangle is far left then command is verysmall right.rule 2: If dangerousness is very safe andangle is close left then command is smallright.rule 3: If dangerousness is dangerous andangle is quite right then command is bigleft.rule 4: If dangerousness is verydangerous and angle is far left thencommand is small right.rule 5: If dangerousness is verydangerous and angle is forward thencommand is very big right.

Table 2 : A rule base for the second level of obstacle avoidance unit

of the Mobile Robot


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To define the goal seeking method for themobile robot, the human behavior dealing withthe goal is considered.

In this method, the goal has a fuzzy valueof dangerousness. The goal's dangerousness islow when the mobile robot can see the goal. Inother words, when the mobile robot can sensethe goal with it's ultra sound sensors (forexample), the goal can be reached easily so ithas a low dangerousness. The goal would havedangerousness if there is a barring obstacle, sothe robot can't find it's path, straight to thegoal. The dangerousness of the goal increaseswhen the barring obstacle is close to the goalor the mobile robot.

The relative distance and angle of theobstacles to the goal goes into a fuzzy logiccontroller to provide a fuzzy value ofdangerousness. The second layer of fuzzycontroller uses this value to make goal seekingcommands to the motors. The dangerousnessof the robot determines how much should therobot pay attention to the goal in it's real-timepath planning.

This approach in goal seeking helpsmobile robot to fall in local minima in lessconditions.

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The behavior of the proposed algorithm isverified with a mobile robot simulationsoftware provided by the author (programmedin C++). The robot moves smoother by thenew algorithm compared to artificial potentialfield approach written before by the author,And does not fall into local minima in manycases. Figure 4 shows a simulation step of themobile robot.

Figure 4 : A simulation step for the Mobile Robot

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This paper proposed a fuzzy basednavigator for the obstacle avoidance andnavigation problem in mobile robots. The newfuzzy local path planning approach is based onhuman sense of obstacles and goals. Thesimulation results show that the robot movesmoother compared to the artificial potentialfield method. Further research needs to bedone to improve the algorithm handling thelocal minima problem.

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[1] J.C. Latombe, “Robot Motion Planning”,Kluwer Academic Publishers, SecondEdition, 1991, ISBN: 0-7923-9206-X

[2] V. Meigoli, S. Kamalodin, Y. Nikraveshand Heidar Ali Talebi, “A New GlobalFuzzy Path Planning and ObstacleAvoidance Scheme for Mobile Robots”, inProc. IEEE Int. Conf. On AdvancedRobotics, pp. 1296-1301, 2003

[3] Panagiotis G. Zavlangas, Prof. Spyros G.Tzafestas, Dr. K. Althoefer, " FuzzyObstacle Avoidance and Navigation forOmnidirectional Mobile Robots", ESIT2000

[4] C. Barret, M. Benerguieg, H. Maaref,“Fuzzy agents for reactive navigation of amobile robot”, First Int. Conf.Knowledge-Based Intelligent ElectronicSystems, pp. 649-658, May 1997


[5] Khatib, O. "Real-Time obstacle avoidancefor Manipulators and Mobile Robots",The International Journal of RoboticsResearch, pp. 90, 1986

[6] Moreno- Velo, I. Baturone, S. Sanchez-Solano, A. Barriga, “X-Fuzzy 3.0: ADevelopment Environment for Fuzzy

Systems”, Proc. 2nd IEEE Int. Conf. onFuzzy Logic and Technology (EUS-FLAT'2001), pp. 93-96, Leicester, 2001

[7] M.Sugeno, M. Nishida, “Fuzzy Control ofa Model Car”, Fuzzy Sets and Systems 16,pp. 103 – 113, 1985b


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