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RRT-HX: RRT with heuristic extend operations for motion planning in robotic systems
N. Pareekutty, F. James, B. Ravindran,
Published in American Society of Mechanical Engineers (ASME)
Volume: 5A-2016
This paper presents a sampling-based method for path planning in robotic systems without known cost-to-go information. It uses trajectories generated from random search to heuristically learn the cost-to-go of regions within the configuration space. Gradually, the search is increasingly directed towards lower cost regions of the configuration space, thereby producing paths that converge towards the optimal path. The proposed framework builds on Rapidly-exploring Random Trees for random sampling-based search and Reinforcement Learning is used as the learning method. A series of experiments were performed to evaluate and demonstrate the performance of the proposed method. Copyright © 2016 by ASME.
About the journal
JournalProceedings of the ASME Design Engineering Technical Conference
PublisherAmerican Society of Mechanical Engineers (ASME)