An opportunistic global path planner
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.
Key wordsObstacle avoidance Global path planner Roadmap algorithm Robot motion planning Artificial potential field
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