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Sensory Steering for Sampling-Based Motion Planning

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2017

OmurArslan, Vincent Pacelli and D. E. Koditschek
Electrical and Systems Engineering, University of Pennsylvania
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Sensory Steering for Sampling-Based Motion Planning
       Sampling-based algorithms offer computationally efficient, practical solutions to the path finding problem in high-dimensional complex configuration spaces by approximately capturing the connectivity of the underlying space through a (dense) collection of sample configurations joined by simple local planners. In this paper, we address a long-standing bottleneck associated with the difficulty of finding paths through narrow passages. Whereas most prior work considers the narrow passage problem as a sampling issue (and the literature abounds with heuristic sampling strategies) very little attention has been paid to the design of new effective local planners. Here, we propose a novel sensory steering algorithm for sampling- based motion planning that can “feel” a configuration space locally and significantly improve the path planning performance near difficult regions such as narrow passages. We provide computational evidence for the effectiveness of the proposed local planner through a variety of simulations which suggest that our proposed sensory steering algorithm outperforms the standard straight-line planner by significantly increasing the connectivity of random motion planning graphs.
This work was supported in part by AFRL grant FA865015D1845 (subcontract 669737–1).
BibTeX entry
  Title                    = {Sensory Steering for Sampling-Based Motion Planning},
  Author                   = {Omur Arslan and Vincent Pacelli and Daniel E. Koditschek},
  Booktitle                = {IEEE/RSJ International Conference on Intelligent Robots and Systems (in press)},
  Year                     = {2017}

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