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CS 326 A: Motion Planning http://robotics.stanford.edu/~latombe/cs326/2003 Probabilistic Roadmaps Basic Techniques
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Motivation Geometric complexity Space dimensionality
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Weaker Completeness Complete planner Too slow Heuristic planner Too unreliable Probabilistic completeness: If there is a solution path, the probability that the planner will find is a (fast growing) function that goes to 1 as running time increases
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Initial idea: Potential Field + Random Walk
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But Pathological Cases …
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Probabilistic Roadmap (PRM) free space mbmbmbmb mgmgmgmg milestone [Kavraki, Svetska, Latombe,Overmars, 95] local path
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Single-Query PRM Planning mbmbmbmb mgmgmgmg
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Two Tenets of PRM Planning Checking sampled configurations and connections between samples for collision can be done efficiently. Hierarchical collision checking A relatively small number of milestones and local paths are sufficient to capture the connectivity of the free space. Exponential convergence in expansive free space (probabilistic completeness)
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Second Tenet of PRM Planning A relatively small number of milestones and local paths are sufficient to capture the connectivity of the free space. Visibility properties of free space Notion of expansive free space (2 nd paper)
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Issues Why random sampling? Convenient incremental scheme Smart sampling strategies Topic for next two classes Final path smoothing
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