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Fast probabilistic collision checking for sampling-based motion planning using locality-sensitive hashing
2016
The international journal of robotics research
We present a novel approach to perform fast probabilistic collision checking in high-dimensional configuration spaces to accelerate the performance of sampling-based motion planning. Our formulation stores the results of prior collision queries, and then uses such information to predict the collision probability for a new configuration sample. In particular, we perform an approximate k-NN (k-nearest neighbor) search to find prior query samples that are closest to the new query configuration.
doi:10.1177/0278364916640908
fatcat:ltztd6csirhkxdropspw3w2rfa