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Biologically Inspired Neural Network Approaches to Real-time Collision-free Robot Motion Planning

  • Simon X. Yang
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 109)

Abstract

In this chapter, a framework, based on biologically inspired neural networks, is proposed for real-time collision-free robot motion planning in a nonstationary environment. Each neuron in the topologically organized neural network is characterized by a shunting equation. The developed algorithms can be applied to point mobile robots, manipulation robots, car-like mobile robots, and multi-robot systems. The real-time optimal robot motion is planned through the dynamic neural activity landscape without explicitly searching over the free workspace or the collision paths, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of robot movement. Therefore the proposed algorithms are computationally efficient. The computational complexity linearly depends on the neural network size. The system stability is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approaches are demonstrated by simulation and comparison studies.

Keywords

Mobile Robot Neural Network Model Motion Planning Path Planning Obstacle Avoidance 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Simon X. Yang
    • 1
  1. 1.School of EngineeringUniversity of GuelphGuelphCanada

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