Homotopy based optimal configuration space reduction for anytime robotic motion planning |
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Authors: | Yang LIU Zheng ZHENG Fangyun QIN |
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Institution: | School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191083, China;School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191083, China;School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191083, China |
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Abstract: | Anytime sampling-based motion planning algorithms are widely used in practical applications due to limited real-time computing resources. The algorithm quickly finds feasible paths and incrementally improves them to the optimal ones. However, anytime sampling-based algorithms bring a paradox in convergence speed since finding a better path helps prune useless candidates but also introduces unrecognized useless candidates by sampling. Based on the words of homotopy classes, we propose a Homotopy class Informed Preprocessor (HIP) to break the paradox by providing extra information. By comparing the words of path candidates, HIP can reveal wasteful edges of the sampling-based graph before finding a better path. The experimental results obtained in many test scenarios show that HIP improves the convergence speed of anytime sampling-based algorithms. |
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Keywords: | Collision avoidance Homotopy Motion planning Rapidly-exploring Random Tree (RRT) Robots |
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