Departmental Papers (ESE)

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Abstract

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

2017

Publication Source

IEEE/RSJ International Conference on Intelligent Robots and Systems

Start Page

3708

Last Page

3715

DOI

10.1109/IROS.2017.8206218

Copyright/Permission Statement

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Bib Tex

@InProceedings{Arslan_pacelli_koditschek,
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},
year={2017},
pages={3708--3715}
}

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Date Posted: 02 August 2018

This document has been peer reviewed.