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A Novel Path Planning Approach Based on AppART and Particle Swarm Optimization

  • Jian Tang
  • Jihong Zhu
  • Zengqi Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

Due to the NP-hard complexity, the path planning problem may perhaps best be resolved by stochastically searching for an acceptable solution rather than using a complete search to find the guaranteed best solution. Most other evolutionary path planners tend to produce jagged paths consisting of a set of nodes connected by line segments. This paper presents a novel path planning approach based on AppART and Particle Swarm Optimization (PSO). AppART is a neural model multidimensional function approximator, while PSO is a promising evolutionary algorithm. This path planning approach combines neural and evolutionary computing in order to evolve smooth motion paths quickly. In our simulation experiments, some complicated path-planning environments were tested, the result show that the hybrid approach is an effective path planner which outperforms many existing methods.

Keywords

Particle Swarm Optimization Mobile Robot Path Planning Particle Swarm Optimization Algorithm Evolutionary Computing 
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.

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References

  1. 1.
    Hwang, Y.K., Ahuja, N.: Gross Motion Planning - A Survey. ACM Computing Survey 24, 219–291 (1992)CrossRefGoogle Scholar
  2. 2.
    Latombe, J.C.: Robot Motion Planning. Kulwer Academic publishers, Boston (1991)Google Scholar
  3. 3.
    Shibata, T., Fukuda, T., Kosuge, K., Arai, F.: Selfish and Coordinative Planning for Multiple Mobile Robots by Genetic Algorithm. In: Proc. of the 31st Conf. on Decision and Control, vol. 3, pp. 2686–2691 (1992)Google Scholar
  4. 4.
    Leung, C.H., Zalzala, A.M.S.: A Genetic Solution for the Motion of Wheeled Robotic Systems in Dynamic Environments. In: Int. Conf. on Control, vol. 1, pp. 760–764 (1994)Google Scholar
  5. 5.
    Trojanowski, K., Michalewicz, Z., Xiao, J.: Adding Memory to the Evolutionary Planner/Navigator. In: Proc. of the 4th IEEE ICEC, pp. 483–487 (1997)Google Scholar
  6. 6.
    Gerke, M.: Genetic Path Planning for Mobile Robots. In: Proc. of the American Control Conference, vol. 4, pp. 2424–2429 (1999)Google Scholar
  7. 7.
    Martí, L., Policriti, A., García, L.: AppART: An ART Hybrid Stable Learning Neural Network for Universal Function Approximation. In: Hybrid Information Systems, pp. 93–120. Springer, Heidelberg (2002)Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc IEEE Int. Conf. Neural Networks, Perth, Australia, pp. 1942–1948 (1995)Google Scholar
  9. 9.
    Mendes, R.C., Rocha, P.M., Neves, J.: Particle Swarms for Feedforward Neural Network Training. In: Proc. Int. Joint Conf. Neural Networks, pp. 1895–1899 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jian Tang
    • 1
  • Jihong Zhu
    • 1
  • Zengqi Sun
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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