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.
- Design Engineering Division
- Computers and Information in Engineering Division
RRT-HX: RRT With Heuristic Extend Operations for Motion Planning in Robotic Systems
Pareekutty, N, James, F, Ravindran, B, & Shah, SV. "RRT-HX: RRT With Heuristic Extend Operations for Motion Planning in Robotic Systems." Proceedings of the ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 5A: 40th Mechanisms and Robotics Conference. Charlotte, North Carolina, USA. August 21–24, 2016. V05AT07A052. ASME. https://doi.org/10.1115/DETC2016-60547
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