Virtual Distance Sensor Information for Effective Obstacle Avoidance of Mobile Robots

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JOURNAL OF COMPUTER SCIENCE AND ENGINEERING, VOLUME 17, ISSUE 1, JANUARY 2013

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Virtual Distance Sensor Information forEffective Obstacle Avoidance of Mobile

RobotsMarwa T. Yousef, Shahira M. Habashy, Hosam Eldin I. Ali and Elsayed M. Saad

AbstractIn this paper, an optimization technique for the virtual sensors calculations is proposed to reduce the computation

time during the motion of robot with obstacles avoidance. This less computation has a good effect on the reaction of the robot.

Where, it gives the robot a high reactionspeed. Advanced Artificial Potential Field (AAPF) controller is used to guide the robot

towards the best next step. Genetic Algorithms is also used to select the optimum values of the forces factors at AAPF

controller. These optimum values make the robot's path much smoother. Simulation on Windows Vista using Matlab Software is

executed to illustrate the proposed approach. Results are compared to previous work illustrating the superiority of the proposed

work.

Index TermsMobile Robot, Obstacle Avoidance, Virtual Sensor, Advanced Artificial Potential Field Controller.

1 INTRODUCTION

obile robots is improved from path guidance me-thods to autonomous mobility, resulting on ob-stacle avoidance of robots have emerged steadily

over the years including directional command methods,such as artificial potential fields [1-5] and speed-spacecommands, such as the curvature foundation system [6-9]. However, most previous results remain a challengingproblem as most existing methods have not considered

the mobility of robots and obstacles. These algorithmscause a robot to move very slowly for obstacle avoidance.If a robot moves very slowly, most of the established al-gorithms can be applied to avoid obstacles. As it movesfaster and faster, avoidance control is more difficult andthe robot tends to collide more frequently with obstacles.

The virtual sensor concept that was introduced in [10]is modified in our work to less computation. This conceptis similar to that of the Doppler Effect. When a robotheads to an obstacle, the distance on the robot sensor islonger than the virtual sensor. Likewise, the physical dis-tance on the robot sensor is shorter than the virtual onewhen it goes away from an obstacle. The introducedmodification of virtual sensor at computation permits therobot to acquire a high speed reaction.

The philosophy of the artificial potential field ap-proach which is used in the current work can be schemat-ically described as: the robot moves in a field of forces.The position to be reached is an attractive pole for therobot and obstacles are repulsive surfaces for the manipu-

lator parts. The net force determines the behavior of therobot against the objects in its environment. The theory ofthe advanced artificial potential field (AAPF) approach isdescribed in [11].

The Genetic Algorithm (GA) is used as in [12] to selectthe optimum factors of the repulsive and the attractiveforces in the offline state which can be then applied on therobot in the online state. The optimum factors are those

that make the robot motion much smoother. Simulationon Matlab is executed for testing the performance of theproposed system in robot motion with obstacle avoid-ance. The simulation is done at four workspaces as in [10],[12]. The simulation results are then used to compare theperformance of the proposed system and the establishedsystem in [10], [12] to evaluate the proposed system effec-tiveness. The odometry of the robot is calculated usingthe angle approach [13].

This paper is organized as follows. Section 2 illustratesthe proposed scheme of virtual sensor calculations. Sec-tion 3 shows the Advanced Artificial Potential Field(AAPF). Section 4 describes the determination of the op-timum factors of the potential field controller forces usingGA and illustrates the GA fitness function which is usedin our work. Section 5 shows the performance of the pro-posed system by simulation in four different cases. Inaddition, comparison with the established system in [10],[12] will be reviewed. Conclusion and future work aregiven in Section 6.

2 VIRTUAL SENSORCALCULATIONS

The concept is used in [10] needs to large computationwhich decreases the speed of robot reaction during its

motion. So, a modification to obtain simple computationto increase the speed of robot reaction is proposed.To make the distance on the robot sensor longer than

the virtual sensor when a robot heads to an obstacle and

Marwa T. Yousef is with the Automatic Control Department, TibbenInstitute for Metallurgical Studies, 11913.

Shahira M. Habashy is with Faculty of Engineering, Helwan University,11792.

Hosam Eldin I. Ali is with Faculty of Engineering, Helwan University,11792.

Elsayed M. Saad is with Faculty of Engineering, Helwan University,11792.

M


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shorter than the virtual one when it goes away from anobstacle, the square of the actual sensor reading should beapplied to give the virtual value that achieves our goal.When the actual distance is less than 1, 1 is the thresholdreading between the robot and the obstacle, it is squared.

This makes the virtual reading smaller than the actual.But when the actual is more than the threshold reading,squaring it makes the virtual reading bigger than the ac-tual. This idea is illustrated by the following characteris-tics curve.

Using higher power than 2 makes the virtual readingdevolve zero.

Fig. 1. The characteristics of the virtual sensor reading

Of course the proposed modification with this simplecomputation leads to increases the speed of robot reac-tion.

3 ADVANCEDARTIFICAIL POTENTIAL FIELD(AAPF)

The controller generates a virtual force field with repul-sion between obstacle and robot and attraction betweentarget and robot. As the robot approaches the obstacle therepulsive force increases. On the other hand, the attrac-tive force to the goal will be decreased as it approaches its

goal. So, the artificial potential field Uart consists ofattractive Ug and repulsive Uo forces. Where, therepulsive force is inversely proportional to the distancebetween the robot and the obstacles. And the attractiveforce is proportional to the distance between the robotand the goal. These logic rules are stated by the followingequations [10], [11].

UartPrt,Pot,Pg= UoPrt,Pot+UgPot,Pg(1)

UoPrt,Pot=Krln ifo0 if>o (2)

UgPot,Pg = 12 Prt Pg2

(3)

Where: Prt is the position of the robot which is calcu-lated by using the angle approach as in [13], Pot is theposition of the obstacle, Pg is the goal position, Kr is therepulsive force factor, is the attractive force factor, isthe virtual sensor reading of the distance between therobot and the obstacle which is calculated as in [10], ando is the threshold distance between the robot and theobstacle.

The forces on the robot can be calculated from the gra-dient function of the artificial potential field as follows[10].

= ,,= , ,= FoP+FgP (4)

Where, P=Prt, FoPrtis the repulsive force andFgPrtis the attractive force.

4 DETERMINING THE OPTIMUMFACTORS AND

FITNESS FUNCTION

The attractive and repulsive coefficients Ka, Kr of theforces are determined empirically in [10], that shouldn'tgrantee a fast and smooth robot path. To overcome thatempirical factors determination, one of the optimizationtechniques such as (GA) can be used to better determinethose factors.

The fitness function which is used in GA optimizer isthe smoothness (SM) that is defined as a criterion of theevaluation to measure the various robot trajectories as:

= 1=0 (5) = 1 11 (6)

Wherem= the last pose number of robot trajectory, xn and yn represent the robot position at nth sampling time.n is the angle between current and former position atnth sampling time.

First the optimum parameters for the potential fieldcontroller are obtained in offline state. It includes guidedrandom search operation using GA to determine the op-timum factors for Kr and Ka which are the factors of the

repulsive and the attractive forces respectively. Four dif-ferent environments for testing the improved potentialfield controller are used. The first environment has oneobstacle, the second environment has two obstacles, thethird one has three obstacles, finally the fourth has multi-knee corridor. The start point of the robot at the first threeworkspaces are (0,0), while at the fourth, the start point ofthe robot is (2,0). The end points (goals) at the four work-spaces are (4,2), (4,3), (4,3) and (6.5,6.5) respectively.These cases are those typically used in [10]. Ka and Krvalues for each workspace according to using GA are de-noted in Table 1.


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TABLE1

THE OPTIMUM OALUES OF FORCES FACTORS AT THE FOURCASES

CASE 1 CASE 2 CASE 3 CASE 4

KA 14.9226 24.8609 12.096 8.3922

KR 0.0511 0.0631 0.0084 0.0013

5 SIMULATIONRESULTS

The robot paths at the four cases are shown in Fig. 2, 3, 4,5 respectively. Where: Fig. 2.a, Fig. 3.a, Fig. 4.a and Fig.5.a represent the simulation results in [10].

(a)

(b)

(c)

Fig. 2.The robot path (a) in [10], (b) in [11], and (c) the proposed

system

Dong Jin Seo, Nak Yong Ko, and Jung Eun Son in [10]used the APF controller and the virtual sensor concept

but with complicated calculations. It didn't use the GA tooptimize the forces factors.Fig. 2.b, Fig. 3.b, Fig. 4.b andFig. 5.b represent the simulation results in [11]. This hasused the AAPF controller which has less computationthan APF controller with the GA to optimize the forcesfactors. And it used the virtual sensor concept which isproposed in [10]. Fig. 2.c, Fig. 3.c, Fig. 4.c and Fig. 5.crepresent the simulation results of the proposed systemwhich contains the improvement of virtual sensor calcula-tions.

(a)

(b)

(c)

Fig. 3.The robot path (a) in [10], (b) in [11], and (c) the proposed

system


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(a)

(b)

(c)

Fig. 4. the robot path (a) in [10], (b) in [11], and (c) the proposedsystem

(a)

(b)

(c)

Fig. 5. the robot path (a) in [10], (b) in [11], and (c) the proposedsystem

At figures 2, 3, 4, and 5, the motion paths (a) at the fourcases present that the robot with empirical values of Kaand Kr leads to slow evasive action than (b), (c). The robotin (a) of each figure makes abrupt changes in its pathswhile the robot at (b), (c) display smoother changes. Thesmoothness achieved with the proposed system is betterthan that achieved with the previous work.

TABLE 2

THE COMPARISON OF THE SMOOTHNESS BETWEEN THE PRO-POSED METHOD AND THE PREVIOUS WORKS

SMOOTH-NESS

CASE 1 CASE 2 CASE 3 CASE 4

APFWITHOUTGA[10]

133.731 303.495 510.116 1420.295

AAPFWITH GA

[11]0.627 0.961 0.980 54.051

THE PRO-PRO-

POSED

SCHEME

0.672 1.083 0.924 11.602

The smoothness achieved at each case is comparedwith the previous works and is shown in Table 2. Asshown from the table 2, an optimization of the smooth-


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ness is occurred with the proposed approach. When thepath of the robot is smoother it means that the robot takesa short path and less time to go to the goal. And the reac-tion speed of the robot will be higher at the proposed sys-tem than [10], [11] due to simplifying the calculations ofvirtual sensor reading.6 CONCLUSION

In this paper, Modification of virtual sensor calculationsis introduced. Of course this modification gives less com-putation. Then, the reaction of the robot will achievehigher speed than the robot that uses a method that wasintroduced in [10]. Advanced artificial potential field con-troller is used. The potential fields controlling the robotmotion are: attractive one (from the goal), and repulsiveone from the obstacles. The GA optimizer was used indifferent robot environments to select the optimum fac-tors of this controller to reach the goal while avoiding

obstacles. The optimized parameters are tested in fourdifferent environments. The results show the superiorityof the proposed controller generally and inclusively com-pared to the works in [10], [11]. Graceful optimizationregarding the path motion is achieved. The real imple-mentation of the proposed work and robots formationcan be done as a future work.

REFERENCES

[1] O. Khatib, "Real-Time Obstacle Avoidance for Manipulators and Mo-bile Robots", Int. J. Robotics Research, Vol. 5, No. 1, Spring 1986, pp. 90-98.

[2] J. Borenstein and Y. Koren, "The Vector Field Histogram-Fast ObstacleAvoidance for Mobile Robots", IEEE Trans. on Robotics Automation, Vol.7, No. 3, June 1991, pp. 278-288.

[3] J. Borenstein and Y. Koren, "Obstacle Avoidance with Ultrasonic Sen-sors", IEEE Journal of Robotics, Vol. 4, No. 2, 1988, pp. 213-218.

[4] E. Shi, T. Cai, C. He and J. Guo, "Study of the New Method for Improv-ing Artificial Potential Field in Mobile Robot Obstacle Avoidance", Proc.of IEEE International Conference on Automation and Logistics, 2007, pp 282-286.

[5] J. Gong, Y. Duan, Y. Man, and G. Xiong, "VPH+: An Enhanced VectorPolar Histogram Method for Mobile Robot Obstacle Avoidance", Proc.of International Conference on Mechatronics and Automation, 2007, pp. 2784-2788.

[6] R. G. Simmons, "The Curvature-Velocity Method for Local ObstacleAvoidance",Proc. of IEEE International Conference on Robotics and Automa-tion, April 1996, pp.3375-3382.

[7] N. Y. Ko, R. G. Simmons, and K. S. Kim, "A Lane Based ObstacleAvoidance Method for Mobile Robot Navigation",Journal of MechanicalScience and Technology, Vol. 17, No. 11, 2003, pp. 1693-1703.

[8] A. Kelly, "An Intelligent Predictive Control Approach to the HighSpeed Cross Country Autonomous Navigation Problem", Tech ReportCMU-CS-TR-95-33, School of Computer Science, Carnegie Mellon Universi-ty, 1995.

[9] C. Schlegel, "Fast Local Obstacle Avoidance under Kinematic and Dy-namic Constrains for a Mobile Robot", Proc. of IEEE/RSJ InternationalConference on Intelligent Robots and Systems, 1998, Vol. 1, pp. 594- 599.

[10] Dong Jin Seo, Nak Yong Ko, and Jung Eun Son, "A Method for Com-bining Odometry and Distance Sensor Information for Effective Ob-stacle Avoidance of Autonomous Mobile Robots", International Journal ofControl, Automation, and Systems, Vol. 8, No. 3, 2010, pp. 597-603.

[11] Marwa Taher, Elsayed Mostafa, Shahira Mahmoud, Obstacle Avoid-ance with Virtual Sensor in Mobile Robots Motion using the Advanced

Potential Field Controller, International Journal of Computer Applications,(0975 8887) Volume 53No.4, September 2012.

[12] Marwa Taher, Hosam Eldin Ibrahim, Shahira Mahmoud, ElsayedMostafa, Tracking of a Moving Target by Improved Potential Field

Controller in Cluttered Environments, IJCSI International Journal of

Computer Science Issues, Vol. 9, Issue 2, No 3, March 2012, pp. 472 480.[13] Philip Machler, "Robot Positioning by Supervised and Unsupervised

Odometry Correction", PhD thesis, Department of Information, colePoly Technique Federal of Lausanne, 1998.

Marwa T. Yousefreceived her B.Sc. degree in Computer Engineer-ing and her M.Sc. degree in Electronic Engineering from HelwanUni-versity, Cairo, Egypt in 2001 and 2006, respectively. She has two-published papers at M.Sc. degree. She is currently a Research As-sistantat Tibben Institute for Metallurgical Studies, Cairo, Egypt.Herresearch interests include robot motion, tracking and their indu-strialapplications.

Shahira M. Habashyreceived her B.Sc. degree in Communications&

Electronics Engineering, her M.Sc. degree in Communications&Electronics Engineering, and her Ph.D. degree in Communications&Electronics Engineering from Helwan University, Cairo, Egypt,in1997, 2000, and 2006 respectively. She has seven published pa-pers.She is currently a Teacher at Electronics, Communication&Computer Department, Faculty of Engineering, Helwan University.

Hosam Eldin I. Alireceived his B.Sc. degree in Communications&Electronics Engineering, his M.Sc. degree in Computer Engineer-ing, and his Ph.D. degree in Computer Engineering from HelwanUniversity,Cairo, Egypt, in 2000, 2004, and 2009 respectively. Hehasthree published papers at M.Sc. degree, and seven publishedpapersat Ph.D. degree. He is currently a Teacher at Electronics,Communication& Computer Department, Faculty of Engineer-ing,HelwanUniversity.

Elsayed M. Saadis a Professor of Electronic Circuits, Faculty ofEn-gineering,Univ. of Helwan.He received his B.Sc. degree in Electrica-lEngineering( Communication section) from Cairo Univ. ,his Dipl.-Ingdegree and Dr.-Ing degree from Stuttgart Univ. , West Germany,at1967,1977 and 1981 respectively . He became an AssociateProf.and a Professor in 1985, and 1990 respectively. He was anInternationalscientific member of the ECCTD, 1983. He is Authorand/orCo-author of 132 scientific papers. He is a member of thenationalRadio Science Comittee, member of the scientific consultantcommitteein the Egyptian Eng. Syndicate for Electrical Engineers, tillMay1995, Member of the Egyptian Eng. Sydicate, Member of theEuropeanCircuit Society (ECS), and Member of the Society of Elec-trial Engineering (SEE).

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Virtual Distance Sensor Information for Effective Obstacle Avoidance of Mobile Robots