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Faster Sample-Based Motion Planning Using Instance-Based Learning
[chapter]
2013
Springer Tracts in Advanced Robotics
We present a novel approach to improve the performance of sample-based motion planners by learning from prior instances. Our formulation stores the results of prior collision and local planning queries. This information is used to accelerate the performance of planners based on probabilistic collision checking, select new local paths in free space, and compute an efficient order to perform queries along a search path in a graph. We present fast and novel algorithms to perform k-NN (k-nearest
doi:10.1007/978-3-642-36279-8_23
fatcat:a37amgfnivagleoas7mnl5pl7u