Encyclopedia of Robotics

Living Edition
| Editors: Marcelo H. Ang, Oussama Khatib, Bruno Siciliano

Asymptotically Optimal Sampling-Based Planners

Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-41610-1_172-1



An asymptotically optimal sampling-based planner employs sampling to solve robot motion planning problems and returns paths with a cost that converges to the optimal solution cost, as the number of samples approaches infinity.


Sampling-based planners sample feasible robot configurations and connect them with valid paths. They are widely popular due to their simplicity, generality, and elegance in terms of analysis. They scale well to high-dimensional problems and rely only on well-understood primitives, such as collision checking and nearest-neighbor data structures. Roadmap planners, e.g., the probabilistic roadmap method (PRM) (Kavraki et al., 1996), construct a graph, where nodes are configurations and edges are local paths. The roadmap can be preprocessed and used to answer multiple queries. Alternatives, e.g., the rapidly exploring random tree (RRT) (LaValle and Kuffner Jr, 2001), build a tree and aim to quickly...

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Authors and Affiliations

  1. 1.Rutgers UniversityNew BrunswickUSA
  2. 2.Rice UniversityHoustonUSA

Section editors and affiliations

  • Lydia E. Kavraki
    • 1
  1. 1.Department of Computer ScienceRice UniversityHoustonUSA