Finding paths in high-dimensional gemetric spaces is a provably hard problem. Recently, a general randomized planning scheme has emerged as an effective approach to solve this problem. In this scheme, the planner samples the space at random and build a network of simple paths, called a probabilistic roadmap. This paper describes a basic probabilistic roadmap planner, which is easily parallelizable, and provides a formal analysis that explains its empirical success when the space satisfies two geometric properties called e-goodness and expansiveness.
CITATION STYLE
Hsu, D., Kavraki, L. E., Latombe, J. C., & Motwani, R. (1998). Capturing the connectivity of high-dimensional geometric spaces by parallelizable random sampling techniques. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1388, pp. 330–340). Springer Verlag. https://doi.org/10.1007/3-540-64359-1_704
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