The dynamic wave expansion neural network model for robot motion planning in time-varying environments

  • Authors:

  • Dmitry V. Lebedev;Jochen J. Steil;Helge J. Ritter

  • Affiliations:

  • Neuroinformatics Group, Faculty of Technology, University of Bielefeld, P.O. Box 10 01 31, 33501 Bielefeld, Germany;Neuroinformatics Group, Faculty of Technology, University of Bielefeld, P.O. Box 10 01 31, 33501 Bielefeld, Germany;Neuroinformatics Group, Faculty of Technology, University of Bielefeld, P.O. Box 10 01 31, 33501 Bielefeld, Germany

  • Venue:
  • Neural Networks
  • Year:
  • 2005

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Abstract

We introduce a new type of neural network-the dynamic wave expansion neural network (DWENN)-for path generation in a dynamic environment for both mobile robots and robotic manipulators. Our model is parameter-free, computationally efficient, and its complexity does not explicitly depend on the dimensionality of the configuration space. We give a review of existing neural networks for trajectory generation in a time-varying domain, which are compared to the presented model. We demonstrate several representative simulative comparisons as well as the results of long-run comparisons in a number of randomly-generated scenes, which reveal that the proposed model yields dominantly shorter paths, especially in highly-dynamic environments.