Evolutionary Artificial Potential Field — Applications to Mobile Robot Path Planning

  • Prahlad Vadakkepat
  • Tong-Heng Lee
  • Liu Xin
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 116)


This chapter discusses the application of the evolutionary artificial potential field (EAPF) in mobile robot path planning. The parameters of the evolutionary artificial potential field are optimized with the multi-objective evolutionary algorithm. The EAPF is utilized in a robot soccer system.


Mobile Robot Path Planning Evolutionary Robotic Robot Soccer Artificial Potential Field 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kim J.-H. and Vadakkepat P. (2000) Multi-agent systems: A survey from the robot soccer perspective. Int. J. of Intelligent Automation and Soft Computing 6(1), pp 3–17Google Scholar
  2. 2.
    Jennings NR and Wooldridge M (1996) Software Agents. IEE Review 42(1), pp 17–21CrossRefGoogle Scholar
  3. 3.
    Tyrrell T (1993) The use of hierarchies for action selection. From Animals to Animats 2, The MIT Press, pp 138–147Google Scholar
  4. 4.
    Brooks RA (1991) Intelligence without representation. Artificial Intelligence, 47(1), pp 139–159CrossRefGoogle Scholar
  5. 5.
    Kawauchi Y, Inaba M and Fukuda T (1993) A Principle of Distributed Decision Making of Cellular Robotic System (CEBOT). IEEE Proc. Int. Conf. Robotics and Automation, vol 3, USA, pp 833–838CrossRefGoogle Scholar
  6. 6.
    Kube CR and Zhang H (1996) The Use of Perceptual Cues in Multi-Robot Box-Pushing. IEEE Proc. Int. Conf. Robotics and Automation, pp 2085–2090Google Scholar
  7. 7.
    Brumitt BL and Stentz A (1996) Dynamic Mission of Planning for Multiple Mobile Robots. IEEE. Proc. of Conf. Robotics and Automation, pp 2396–2401Google Scholar
  8. 8.
    Kim JH, Shim HS, Kim HS, Jung MJ, Choi IH, and Kim JO (1997) A cooperative multi-agent system and its real time application to robot soccer. Proc. IEEE Int. Conf. on Robotics and Automation, Albuquerque, New Mexico, vol 1, pp 638–643CrossRefGoogle Scholar
  9. 9.
    Masoud AA (1998) Integrating Directional Constraints in Motion Planning Using Nonlinear, Anisotropic, Harmonic Potential Fields. Proc of the 1998 IEEE ISIC/CIRA/ISAS Joint Conference, Gaithersburg, MD, pp 14–18Google Scholar
  10. 10.
    Tsuji T, Morasso PG and Kaneko M (1996) Trajectory Generation for Manipulators Based on Artificial Potential Field Approach with Adjustable Temporal Behavior. Intelligent Robots and Systems’96, IROS 96, Proc. of the 1996 IEEE/RSJ International Conference, vol 2, pp 438–443Google Scholar
  11. 11.
    Wang Y and Chirikjian GS (2000) A New Potential Field Method for Robot Path Planning. Proc. of IEEE Int. Conf. on Robotics and Automation, vol 2, pp 977–982Google Scholar
  12. 12.
    Jin-Oh Kim, Pradeep K. Khosla (1992) Real-Time Obstacle Avoidance Using Harmonic Potential Functions. IEEE Trans. Of Robotics and Automation, vol 8, no. 3, pp 338–349CrossRefGoogle Scholar
  13. 13.
    Timothy ER and McCartney R (2000) A Cost Term In An Evolutionary Robotics Fitness Function. Proc. of Congress on Evolutionary Computation, vol 1.1, pp 125–132Google Scholar
  14. 14.
    Nolfi S (1998) Evolutionary Robotics: Exploiting the full power of selforganization. Self-Learning Robots II. Bio-robotics, Digest No. 1998/248, IEE pp 3/1 -3/7Google Scholar
  15. 15.
    Dozier G, Homaifar A, Bryson S and Moore L (1998) Artificial potential field based robot navigation, dynamic constrained optimization and simple genetic hill-climbing. Proc. Evolutionary Computation, IEEE World Congress on Computational Intelligence, pp 189–194Google Scholar
  16. 16.
    Vadakkepat P, Tan KC and Wang ML (2000) Evolutionary artificial potential fields and their application in real time robot path planning. Proc. Congress on Evolutionary Computation, vol 1, pp 256–263Google Scholar
  17. 17.
    Shim HS, Jung MJ, Kim HS, Kim JH and Vadakkepat P (2000) A Hybrid Control Structure for Vision Based Soccer Robot System. Intelligent Automation and Soft Computing, vol 6, no 1, pp 89–101Google Scholar
  18. 18.
    Vadakkepat P, Lee TH and Xin L (2001) Application of Evolutionary Artificial Potential Field in Robot Soccer System. Joint 9th IFSA World Congress and 20th NAFIS International Conference, Vancouver, Canada, pp 2781–2785Google Scholar
  19. 19.
    Tan KC, Wang QG, Lee TH, Khoo TT and Khor EF (1999) A Multi-Objective Evolutionary Algorithm toolbox for Matlab (,kctan/moea.htm)Google Scholar
  20. 20.
    Rana AS and Zalzala AMS (1995) An evolutionary algorithm for collision free motion planning of multi-arm robots. First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, pp 123–130Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Prahlad Vadakkepat
    • 1
  • Tong-Heng Lee
    • 1
  • Liu Xin
    • 1
  1. 1.Department of Electrical and Computer Engineering 4 Engineering Drive 3National University of SingaporeSingapore

Personalised recommendations