Resolution Independent Density Estimation for Motion Planning in High-Dimensional Spaces

B. Gipson, M. Moll, and L. E. Kavraki, “Resolution Independent Density Estimation for Motion Planning in High-Dimensional Spaces,” in IEEE International Conference on Robotics and Automation, 2013, pp. 2429–2435.


This paper presents a new motion planner, Search Tree with Resolution Independent Density Estimation (STRIDE), designed for rapid exploration and path planning in high-dimensional systems (greater than 10). A Geometric Near- neighbor Access Tree (GNAT) is maintained to estimate the sampling density of the configuration space, allowing an implicit, resolution-independent, Voronoi partitioning to provide sampling density estimates, naturally guiding the planner towards unexplored regions of the configuration space. This planner is capable of rapid exploration in the full dimension of the configuration space and, given that a GNAT requires only a valid distance metric, STRIDE is largely parameter-free. Extensive experimental results demonstrate significant dimension- dependent performance improvements over alternative state-of-the-art planners. In particular, high-dimensional systems where the free space is mostly defined by narrow passages were found to yield the greatest performance improvements. Experimental results are shown for both a classical 6-dimensional problem and those for which the dimension incrementally varies from 3 to 27.


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