Navigation & SLAM
Probabilistic Roadmap (PRM)
A probabilistic roadmap (PRM) is a sampling-based motion planner, introduced by Kavraki, Svestka, Latombe, and Overmars in 1996, that randomly samples collision-free configurations, connects nearby samples with feasible local paths to form a graph, and answers queries by graph search. As a multi-query method it amortizes roadmap construction across many planning problems, and the PRM* variant achieves asymptotic optimality.
Why it matters for physical AI
Sampling-based planners like PRM made motion planning tractable in high-dimensional configuration spaces, and precomputed roadmaps still power fast repeated planning in static industrial workcells.
Related terms
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