Nonholonomic Vehicle Motion Planning by Generating a Mapping from Configuration to Control Output with Reinforcement Learning
Stelson, K. and Oba, F.
This paper presents the investigation of applying reinforcement learning to nonholonomic robot motion planning in an uncertain workspace. We propose a planning system for the nonholonomic robot, in which the continuous configuration and control output of the robot are discretized, and the reinforcement learning algorithm generates a mapping from the configuration to the output. The application results are presented with computational simulations.
K. Naruse and M. Leu, "Nonholonomic Vehicle Motion Planning by Generating a Mapping from Configuration to Control Output with Reinforcement Learning," Proceedings of the Japan/USA Symposium on Flexible Automation, American Society of Mechanical Engineers (ASME), Jan 1996.
1996 Japan-USA Symposium on Flexible Automation Part 2
Mechanical and Aerospace Engineering
Keywords and Phrases
Computer Simulation; Learning Algorithms; Motion Planning; Obot Learning
Article - Conference proceedings
© 1996 American Society of Mechanical Engineers (ASME), All rights reserved.