Learning the Perceptual Control Manifold for sensor-based robot path planning

M. Zeller, K. Schulten, R. Sharma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Perceptual Control Manifold is a recently introduced concept that extends the notion of the robot configuration space to include sensor feedback for robot motion planning. In this paper, we propose a framework for sensor-based robot motion planning using the Topology Representing Network algorithm to develop a learned representation of the Perceptual Control Manifold. The topology preserving features of the neural network lend themselves to yield, after learning, a diffusion-based path planning strategy for flexible obstacle avoidance. Simulations on path control and flexible obstacle avoidance demonstrate the feasibility of this approach for motion planning and illustrate the potential for further robotic applications.

Original languageEnglish (US)
Title of host publicationProceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
PublisherIEEE
Pages48-53
Number of pages6
StatePublished - Jan 1 1997
EventProceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA - Monterey, CA, USA
Duration: Jul 10 1997Jul 11 1997

Other

OtherProceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
CityMonterey, CA, USA
Period7/10/977/11/97

ASJC Scopus subject areas

  • Computational Mathematics

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