DOI QR코드

DOI QR Code

Heuristics for Motion Planning Based on Learning in Similar Environments

  • Ogay, Dmitriy (Department of Computer Science & Engineering, Graduate School, Korea University of Technology and Education) ;
  • Kim, Eun-Gyung (School of Computer Science & Engineering, Korea University of Technology and Education)
  • Received : 2013.12.03
  • Accepted : 2014.02.17
  • Published : 2014.06.30

Abstract

This paper discusses computer-generated heuristics for motion planning. Planning with many degrees of freedom is a challenging task, because the complexity of most planning algorithms grows exponentially with the number of dimensions of the problem. A well-designed heuristic may greatly improve the performance of a planning algorithm in terms of the computation time. However, in recent years, with increasingly challenging high-dimensional planning problems, the design of good heuristics has itself become a complicated task. In this paper, we present an approach to algorithmically develop a heuristic for motion planning, which increases the efficiency of a planner in similar environments. To implement the idea, we generalize modern motion planning algorithms to an extent, where a heuristic is represented as a set of random variables. Distributions of the variables are then analyzed with computer learning methods. The analysis results are then utilized to generate a heuristic. During the experiments, the proposed approach is applied to several planning tasks with different algorithms and is shown to improve performance.

Keywords

References

  1. S. M. LaValle, Planning Algorithms. Cambridge: Cambridge University Press, 2006.
  2. D. Berenson, P. Abbeel, and K. Goldberg, "A robot path planning framework that learns from experience," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), St. Paul, MN, pp. 3671-3678, 2012.
  3. S. M. LaValle and J. J. Kuffner, "Randomized kinodynamic planning," International Journal of Robotics Research, vol. 20, no. 5, pp. 378-400, 2001. https://doi.org/10.1177/02783640122067453
  4. S. Dalibard and J. P. Laumond, "Control of probabilistic diffusion in motion planning," in Algorithmic Foundation of Robotics VIII. Heidelberg: Springer, pp. 467-481, 2009.
  5. J. M. Lien and Y. Lu, "Planning motion in environments with similar obstacles," in Proceedings of Robotics: Science and Systems, Seattle, WA, pp. 1-7, 2009.
  6. M. Zucker, J. Kuffner, and M. Branicky, "Multipartite RRTs for rapid replanning in dynamic environments," in Proceedings of the IEEE International Conference on Robotics and Automation, Rome, Italy, pp. 1603-1609, 2007.
  7. M. Zucker, J. Kuffner, and J. A. Bagnell, "Adaptive workspace biasing for sampling-based planners," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, pp. 3757-3762, 2008.
  8. S. Martin, S. Wright, and J. Sheppard, "Offline and online evolutionary bi-directional RRT algorithms for efficient replanning in environments with moving obstacles," in Proceedings of the IEEE International Conference on Automation Science and Engineering, Scottsdale, AZ, pp. 1131-1136, 2007.
  9. C. G. Atkeson and J. Morimoto, "Nonparametric representation of policies and value functions: a trajectory-based approach," in Proceedings of the Neural Information Processing Systems Conference (Advances in Neural Information Processing Systems), Vancouver, Canada, pp. 1611-1618, 2003.
  10. M. Stolle and C. G. Atkeson, "Policies based on trajectory libraries," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Orlando, FL, pp. 3344-3349, 2006.
  11. C. E. Shannon, "A mathematical theory of communication," Bell System Technical Journal, vol. 27, pp. 379-423, 623-656, 1948. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

Cited by

  1. Applying Clinical Judgment Rubric for Evaluation of Simulation Practice for Nursing Students : A Non-Randomized Controlled Trial vol.14, pp.2, 2018, https://doi.org/10.5392/ijoc.2018.14.2.035