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A neural network-based method for time-optimal trajectory planning

Published online by Cambridge University Press:  05 April 2001

Gu Fang  and
M. W. M. G. Dissanayake
Gu Fang
Affiliation:
School of Mechanical Automation Engineering, University of Western Sydney, Nepan, PO Box 10, Kingswood, NSW 2747, Australia [email protected].
M. W. M. G. Dissanayake
Affiliation:
Department of Mechanical and Mechatronic Engineering, The University of Sydney, NSW 2006, Australia [email protected].
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Abstract

Planning appropriate trajectories can significantly increase the productivity of robot systems. To plan realistic time-optimal trajectories, the robot dynamics have to be described precisely. In this paper, a neural network based algorithm for tim e-optimal trajectory planning is introduced. This method utilises neural networks for representing the inverse dynamics of the robot. As the proposed neural networks can be trained with data obtained from exciting the robot with given torque inputs, they will capture the complete dynamics of the robot system. Threfore, the trajectories generated will be mo re realistic than those obtained by using nominal dynamic equations based on nominal parameters. Time-optimal trajectories are generated for a PUMA robot to demonstrate the proposed method.

Keywords

Neural networkTrajectory planningInverse dynamicsPUMA robot
Type
Research Article
Information
Robotica , Volume 16 , Issue 2 , March 1998 , pp. 143 - 158
Copyright
1998 Cambridge University Press

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