Advertisement

Adaptive Genetic Algorithms for Multi-Point Path Finding in Artificial Potential Fields

  • R. M. Rylatt
  • C. A. Czarnecki
  • T. W. Routen
Conference paper

Abstract

We present research work in progress into the use of adaptive genetic algorithms (AGAs) to search for collision-free paths in an artificial potential field (APF) representation of a cluttered robotic work-cell. We argue that the AGA approach promises to avoid the drawback of other APF approaches which are vulnerable to entrapment by local minima.

Keywords

Genetic Algorithm Path Planning Collision Avoidance Simple Genetic Algorithm Adaptive Genetic Algorithm 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahuactzin, J-M., Talbi E-G., Bessiere, P and Mazer, E. (1993) Using Genetic Algorithms for Robot Motion Planning. IEEE-IROS’93. Yokohama, Japan.Google Scholar
  2. Craig, J.J. (1989) Introduction to Robotics: Mechanics and Control. Reading, MA: Addison Wesley.MATHGoogle Scholar
  3. Davidor, Y. (1989) Analogous crossover. Proceedings of the Third Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann, 42–50.Google Scholar
  4. Davidor, Y. (1991) Genetic Algorithms: A Heuristic Strategy for Optimization. Singapore: World Scientific.MATHGoogle Scholar
  5. Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison Wesley.MATHGoogle Scholar
  6. Khatib, O. (1986) Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research. 5: 1, 90–98.MathSciNetCrossRefGoogle Scholar
  7. Lozano-Perez, T (1987) A Simple Motion-Planning Algorithm for General Robot Manipulators. IEEE Journal of Robotics and Automation, RA-3: 3, 225–237.CrossRefGoogle Scholar
  8. Rylatt, R.M. (1994) M. Sc. Dissertation. De Montfort University, Leicester, U.K.Google Scholar
  9. Srinivas, M. and Patnaik, L.M. (1994) Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics. 24: 4, 656–667.CrossRefGoogle Scholar
  10. Warren, C.W., Danos, J.C. and Mooring, B.W. (1989) An Approach to Manipulator Path Planning. The International Journal of Robotics Research. 8: 5, 87–95.CrossRefGoogle Scholar
  11. Zelinsky, A. (1994) Using Path Transforms to Guide the Search for Findpath in 2D. International Journal of Robotics Research, 13: 4, 315–325.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag/Wien 1995

Authors and Affiliations

  • R. M. Rylatt
    • 1
  • C. A. Czarnecki
    • 1
  • T. W. Routen
    • 1
  1. 1.Department of Computer ScienceDe Montfort UniversityLeicesterUK

Personalised recommendations