Motion planning for high DOF anthropomorphic hands
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Image-based mapping and navigation with heterogenous robots
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Sampling-based finger gaits planning for multifingered robotic hand
Autonomous Robots
Motion planning with dynamics by a synergistic combination of layers of planning
IEEE Transactions on Robotics
Sampling-based algorithms for optimal motion planning
International Journal of Robotics Research
Randomized path planning on manifolds based on higher-dimensional continuation
International Journal of Robotics Research
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The cost of nearest-neighbor (NN) calls is one of the bottlenecks in the performance of sampling-based motion-planning algorithms. Therefore, it is crucial to develop efficient techniques for NN searching in configuration spaces arising in motion planning. In this paper, we present and implement an algorithm for performing NN queries in Cartesian products of R, S1, and RP3, the most common topological spaces in the context of motion planning. Our approach extends the algorithm based on kd-trees, called ANN, developed by Arya and Mount for Euclidean spaces. We prove the correctness of the algorithm and illustrate substantial performance improvement over the brute-force approach and several existing NN packages developed for general metric spaces. Our experimental results demonstrate a clear advantage of using the proposed method for both probabilistic roadmaps and rapidly exploring random trees