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

M. Zeller; K. Schulten; R. Sharma

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

M. Zeller, K. Schulten, R. Sharma

Physics

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
Publisher	IEEE
Pages	48-53
Number of pages	6
State	Published - 1997
Event	Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA - Monterey, CA, USA Duration: Jul 10 1997 → Jul 11 1997

Other

Other	Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
City	Monterey, CA, USA
Period	7/10/97 → 7/11/97

ASJC Scopus subject areas

Computational Mathematics

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Zeller, M, Schulten, K & Sharma, R 1997, Learning the Perceptual Control Manifold for sensor-based robot path planning. in Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA. IEEE, pp. 48-53, Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, Monterey, CA, USA, 7/10/97.

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AB - 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.

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ER -

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

Abstract

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