Read Sandström, Andrew Bregger, Ben Smith, Shawna Thomas, Nancy M. Amato,
IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, May 2018.
Abstract: Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms. The cost of finding nearest neighbors grows with the size of the roadmap, leading to significant slowdowns for problems which require many configurations to find a solution. Prior work has investigated relieving this pressure with quicker computational techniques, such as kd-trees or locality-sensitive hashing. In this work, we investigate an alternative direction for expediting this process based on workspace connectivity. We present an algorithm called Topological Nearest-Neighbor Filtering, which employs a workspace decomposition to select a topologically relevant set of candidate neighbor configurations as a pre-processing step for a nearest-neighbor algorithm. We investigate the application of this filter to several varieties of RRT and demonstrate that the filter improves both nearest-neighbor time and overall planning performance.
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