Departmental Papers (ESE)


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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.

For more information: Kod*lab

Sponsor Acknowledgements

This work was supported in part by AFRL grant FA865015D1845 (subcontract 669737–1).

Document Type

Conference Paper

Subject Area

GRASP, Kodlab

Date of this Version


Publication Source

IEEE/RSJ International Conference on Intelligent Robots and Systems

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Bib Tex

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},



Date Posted: 02 August 2018

This document has been peer reviewed.