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Method evidence record

Probabilistic Roadmap

The Probabilistic Roadmap (PRM) method is a motion planning algorithm that builds a pre-computed graph (roadmap) of feasible paths through the configuration space by sampling random configurations and connecting them if collision-free. Introduced by Kavraki et al. in 1996, PRM is powerful for multi-query planning scenarios where many path queries are answered, amortizing roadmap construction cost across many queries.

Evidence statusSources recorded, not reviewedBibliographic sources are present. Claim-level evidence review has not been performed.

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Probabilistic Roadmap

Taxonomic method recordml-modelcontrol-theory

  1. Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566-580. · DOI 10.1109/70.508439

  2. Overmars, M. H., & Svestka, P. (1992). A probabilistic learning approach to motion planning. Proceedings of the Fourth Workshop on Algorithmic Foundations of Robotics, 19-37. · URL

  3. LaValle, S. M. (2006). Planning Algorithms. Cambridge University Press. · URL

Open full method3 recorded citations, copied from the method source record.

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