A Behavior Architecture for Autonomous Mobile Robots Based on Potential Fields

  • Tim Laue
  • Thomas Röfer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3276)


This paper describes a behavior-based architecture which integrates existing potential field approaches concerning motion planning as well as the evaluation and selection of actions into a single architecture. This combination allows, together with the concept of competing behaviors, the specification of more complex behaviors than the usual approach which is focusing on behavior superposition and is mostly dependent on additional external mechanisms. The architecture and all methods presented in this paper have been implemented and applied to different robots.


Mobile Robot Motion Planning Obstacle Avoidance Motion Behavior Object Instance 
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.


  1. 1.
    Khatib, O.: Real-time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research 5, 90–98 (1986)CrossRefGoogle Scholar
  2. 2.
    Latombe, J.C.: Robot Motion Planning. Kluwer Academic Publishers, Boston (1991)Google Scholar
  3. 3.
    Arkin, R.C.: Behavior-Based Robotics. MIT Press, Cambridge (1998)Google Scholar
  4. 4.
    Johannson, S.J., Saffiotti, A.: Using the Electric Field Approach in the RoboCup Domain. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, p. 399. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Meyer, J., Adolph, R.: Decision-making and Tactical Behavior with Potential Fields. In: Kaminka, G.A., Lima, P.U., Rojas, R. (eds.) RoboCup 2002. LNCS (LNAI), vol. 2752, pp. 304–311. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Weigel, T., Gutmann, J.S., Dietl, M., Kleiner, A., Nebel, B.: CS-Freiburg: Coordinating Robots for Successful Soccer Playing. IEEE Transactions on Robotics and Automation 18, 685–699 (2002)CrossRefGoogle Scholar
  7. 7.
    Ball, D., Wyeth, G.: Multi-Robot Control in Highly Dynamic, Competitive Environments. In: Browning, B., Polani, D., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003: Robot Soccer World Cup VII. LNCS (LNAI). Springer, Heidelberg (2004) (to appear)Google Scholar
  8. 8.
    Laue, T.: Eine Verhaltenssteuerung für autonome mobile Roboter auf der Basis von Potentialfeldern. Diploma thesis, Universität Bremen (2004)Google Scholar
  9. 9.
    Maes, P.: How To Do the Right Thing. Connection Science Journal 1, 291–323 (1989)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Brooks, R.: A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation 2, 14–23 (1986)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Brooks, R.: Intelligence without representation. Artificial Intelligence Journal 47, 139–159 (1991)CrossRefGoogle Scholar
  12. 12.
    Reif, J.H., Wang, H.: Social Potential Fields: A Distributed Behavioral Control for Autonomous Robots. In: Goldberg, K., Halperin, D., Latombe, J.C., Wilson, R. (eds.) The Algorithmic Foundations of Robotics, pp. 331–345. A. K. Peters, Boston (1995)Google Scholar
  13. 13.
    Arkin, R.C.: Motor Schema-Based Mobile Robot Navigation. The International Journal of Robotics Research 8, 92–112 (1989)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Balch, T., Arkin, R.C.: Behavior-based Formation Control for Multi-robot Teams. IEEE Transactions on Robotics and Automation 14, 926–939 (1999)CrossRefGoogle Scholar
  15. 15.
    Koren, Y., Borenstein, J.: Potential Field Methods and Their Inherent Limitations for Mobile Robot Navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation, Sacramento, California, USA, pp. 1398–1404 (1991)Google Scholar
  16. 16.
    Behnke, S.: Local Multiresolution Path Planning. In: Polani, D., Browning, B., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003. LNCS (LNAI), vol. 3020, pp. 332–343. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Hart, P., Nilsson, N.J., Raphael, B.: A Formal Basis for the Heuristic Determination of Minimum Cost Paths in Graphs. IEEE Transactions on Systems Science and Cybernetics SSC-4, 100–107 (1968)CrossRefGoogle Scholar
  18. 18.
    Röfer, T., Brunn, R., Dahm, I., Hebbel, M., Hoffmann, J., Jüngel, M., Laue, T., Lötzsch, M., Nistico, W., Spranger, M.: GermanTeam 2004. The German RoboCup National Team. In: RoboCup 2004: Robot Soccer World Cup VIII. LNCS (LNAI), Springer, Heidelberg (2004) (to appear)Google Scholar
  19. 19.
    Lötzsch, M., Bach, J., Burkhard, H.D., Jüngel, M.: Designing Agent Behavior with the Extensible Agent Behavior Specification Language XABSL. In: Browning, B., Polani, D., Bonarini, A., Yoshida, K. (eds.) RoboCup 2003: Robot Soccer World Cup VII. LNCS (LNAI). Springer, Heidelberg (2004) (to appear)Google Scholar
  20. 20.
    Kurlbaum, J., Laue, T., Lück, B., Mohrmann, B., Poloczek, M., Reinecke, D., Riemenschneider, T., Röfer, T., Simon, H., Visser, U.: Bremen Small Multi-Agent Robot Team (B-Smart) Team Description for RoboCup 2004. In: RoboCup 2004: Robot Soccer World Cup VIII. LNCS (LNAI). Springer, Heidelberg (2004) (to appear)Google Scholar
  21. 21.
    Behnke, S., Rojas, R.: A hierarchy of reactive behaviors handles complexity. In: Hannebauer, M., Wendler, J., Pagello, E. (eds.) ECAI-WS 2000. LNCS (LNAI), vol. 2103, p. 125. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  22. 22.
    Jäger, H., Christaller, T.: Dual Dynamics: Designing Behavior Systems for Autonomous Robots. In: Fujimura, S., Sugisaka, M. (eds.) Proceedings of the International Symposium on Artificial Life and Robotics (AROB 1997), Beppu, Japan, pp. 76–79 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tim Laue
    • 1
  • Thomas Röfer
    • 1
  1. 1.Bremer Institut für Sichere Systeme, Technologie-Zentrum Informatik, FB 3Universität BremenBremenGermany

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