POTBUG: A Mind’s Eye Approach to Providing BUG-Like Guarantees for Adaptive Obstacle Navigation Using Dynamic Potential Fields

  • Michael Weir
  • Anthony Buck
  • Jon Lewis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


The problem we address is adaptive obstacle navigation for autonomous robotic agents in an unknown or dynamically changing environment with a 2-D travel surface without the use of a global map. Two well known but hitherto apparently antithetical approaches to the problem, potential fields and BUG algorithms, are synthesised here. The best of both approaches is attempted by combining a Mind’s Eye with dynamic potential fields and BUG-like travel modes. The resulting approach, using only sensed goal directions and obstacle distances relative to the robot, is compatible with a wide variety of robots and provides robust BUG-like guarantees for successful navigation of obstacles. Simulation experiments are reported for both near-sighted (POTBUG) and far-sighted (POTSMOOTH) robots. The results are shown to support the theoretical design’s intentions that the guarantees persist in the face of significant sensor perturbation and that they may also be attained with smoother paths than existing BUG paths.


Potential Field Sensor Range Travel Path Obstacle Distance Goal Direction 
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.
    Russell, S.J., Norvig, P.: Artificial Intelligence, a Modern Approach. Prentice Hall, Englewood Cliffs (2002)Google Scholar
  2. 2.
    Bell, G., Weir, M.: Forward chaining for robot and agent navigation using potential fields. In: Twenty-seventh Australasian Computer Science Conference (ACSC 2004), vol. 26 (2004)Google Scholar
  3. 3.
    Lumelsky, V., Stepanov, A.: Path planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape. Algorithmica 2(4), 403–440 (1987)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Lumelsky, V., Mukhopadhyay, S., Sun, K.: Dynamic path planning in sensor-based terrain acquisition. IEEE Trans. Robotics and Automation 6(4), 462–472 (1990)CrossRefGoogle Scholar
  5. 5.
    Rao, N.S.V., Kareti, S., Shi, W., Iyenagar, S.S.: Robot Navigation in Unknown Terrains: Introductory Survey of Non-Heuristic Algorithms. Oak Ridge National Laboratory Technical Report, ORNL/TM-12410, 1–58 (July 1993)Google Scholar
  6. 6.
    Latombe, J.: Robot Motion Planning. Kluwer Academic Publishers, Dordrecht (1991)Google Scholar
  7. 7.
    Dudek, G., Jenkin, M.: Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  8. 8.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research 5(1) (1986)Google Scholar
  9. 9.
    Arkin, R.: Behavior-based robotics. MIT Press, Cambridge (1998)Google Scholar
  10. 10.
    Koren, Y., Borenstein, J.: Potential field methods and their inherent limitation for mobile robot navigation. In: IEEE Conference on Robotics and Automation, pp. 1398–1404 (1991)Google Scholar
  11. 11.
    Lumelsky, V.J., Skewis, T.: Incorporating range sensing in the robot navigation function. IEEE Transactions on Systems, Man, and Cybernetics 20(5), 1058–1068 (1990)CrossRefGoogle Scholar
  12. 12.
    Kamon, I., Rivlin, E.: Sensory-based motion planning with global proofs. IEEE Transactions on Robotics and Automation 13(6) (1997)Google Scholar
  13. 13.
    Kamon, I., Rivlin, E.: Range-sensor-based navigation in three-dimensional polyhedral environments. The International Journal of Robotics Research 20(1) (2001)Google Scholar
  14. 14.
    Koditschek, D.: Exact robot navigation by means of potential functions: Some topological consideration. In: IEEE Conference on Robotics and Automation, pp. 1–6 (1987)Google Scholar
  15. 15.
    Alvarez, D.: Online motion planning using Laplace potential fields. In: IEEE International Conference on Robotics and Automation, pp. 3347–3352 (2003)Google Scholar
  16. 16.
    Franceschini, N., Pichon, J.M., Blanes, C.: From insect vision to robot vision. Philosophical Transactions of the Royal Society B 337, 283–294 (1992)CrossRefGoogle Scholar
  17. 17.
    Huang, W.H., Fajen, B.R., Fink, J.R., Warren, W.H.: Visual navigation and obstacle avoidance using a steering potential function. Robotics and Autonomous Systems 54(4), 288–299 (2006)CrossRefGoogle Scholar
  18. 18.
    Gorse, D., et al.: The new era in supervised learning. Neural Networks 10(2), 343–352 (1987)CrossRefGoogle Scholar
  19. 19.
    Lewis, J., Weir, M.: Subgoal chaining and the local minimum problem. In: IEEE International Joint Conference on Neural Networks (1999)Google Scholar
  20. 20.
    Lewis, J., Weir, M.: Using subgoal chaining to address the local minimum problem. In: International ICSC Symposium on Neural Computation (2000)Google Scholar
  21. 21.
    Xi-yong, Z., Jing, Z.: Virtual local target method for avoiding local minimum in potential fields based navigation. Journal of Zhejiang University SCIENCE 4(3), 264–269 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael Weir
    • 1
  • Anthony Buck
    • 1
  • Jon Lewis
    • 1
  1. 1.School of Computer ScienceUniversity of St AndrewsSt AndrewsScotland

Personalised recommendations