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An opportunistic global path planner

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Abstract

In this paper we describe a robot path-planning algorithm that constructs a global skeleton of free-space by incremental local methods. The curves of the skeleton are the loci of maxima of an artificial potential field that is directly proportional to distance of the robot from obstacles. Our method has the advantage of fast convergence of local methods in uncluttered environments, but it also has a deterministic and efficient method of escaping local extremal points of the potential function. We first describe a general roadmap algorithm, for configuration spaces of any dimension, and then describe specific applications of the algorithm for robots with two and three degrees of freedom.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of California, 543 Evans Hall, 94720, Berkeley, CA, USA

    John F. Canny

  2. Department of Electrical Engineering and Computer Science, University of California, 211 Cory Hall, Box #79, 94720, Berkeley, CA, USA

    Ming C. Lin

Authors
  1. John F. Canny
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  2. Ming C. Lin
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Additional information

Communicated by Bruce Randall Donald.

This research was supported by a David and Lucile Packard Foundation Fellowship and by NSF Presidential Young Investigator Grant number IRI-8958577.

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Canny, J.F., Lin, M.C. An opportunistic global path planner. Algorithmica 10, 102–120 (1993). https://doi.org/10.1007/BF01891836

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