Robot Motion Planning: A Distributed Representation Approach

DOI Web Site 被引用文献18件
  • Jérôme Barraquand
    Robotics Laboratory Department of Computer Science Stanford University Stanford, California 94305
  • Jean-Claude Latombe
    Robotics Laboratory Department of Computer Science Stanford University Stanford, California 94305

書誌事項

公開日
1991-12
権利情報
  • https://journals.sagepub.com/page/policies/text-and-data-mining-license
DOI
  • 10.1177/027836499101000604
公開者
SAGE Publications

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説明

<jats:p> We propose a new approach to robot path planning that consists of building and searching a graph connecting the local minima of a potential function defined over the robot's configuration space. A planner based on this approach has been implemented. This planner is consider ably faster than previous path planners and solves prob lems for robots with many more degrees of freedom (DOFs). The power of the planner derives both from the "good" properties of the potential function and from the efficiency of the techniques used to escape the local min ima of this function. The most powerful of these tech niques is a Monte Carlo technique that escapes local min ima by executing Brownian motions. The overall approach is made possible by the systematic use of distributed rep resentations (bitmaps) for the robot's work space and configuration space. We have experimented with the plan ner using several computer-simulated robots, including rigid objects with 3 DOFs (in 2D work space) and 6 DOFs (in 3D work space) and manipulator arms with 8, 10, and 31 DOFs (in 2D and 3D work spaces). Some of the most significant experiments are reported in this article. </jats:p>

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