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— Randomly expanding trees are very effective in exploring high-dimensional spaces. Consequently, they are a powerful algorithmic approach to sampling-based single-query motion p...
A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementall...
In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabi...
During the last decade, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), have been shown to work well in practice and to po...
— In this paper we develop an RRT-based motion planner that achieved bounding in simulation with the LittleDog robot over extremely rough terrain. LittleDog is a quadruped robot ...
Alexander C. Shkolnik, Michael Levashov, Ian R. Ma...