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Research on Robot Motion Planning Based on RRT Algorithm with Nonholonomic Constraints

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Abstract

A 1–0 Bg-RRT algorithm is proposed to reduce computational time and complexity, even in complex environments. Different from Rapidly-exploring Random Tree (RRT) and Bias-goal Rapidly-exploring Random Tree (Bg-RRT), using 1–0 Bg-RRT with 1 and 0 change probability biased to the target to construct the tree is faster and can jump out of the local minimum in time. Although unknown space path planning problem based on RRT is difficult to obtain satisfactory performance, but the improved algorithm provides a more superior compared with the basic RRT algorithm and Bg-RRT algorithm. The simulation results show that the 1–0 Bg-RRT algorithm has shorter computational time and shorter path than the traditional RRT algorithm.

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Acknowledgements

The research was funded by the National Natural Science Foundation of China (No. 51375314).

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Authors and Affiliations

  1. College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China

    Yi Gan, Bin Zhang, Chao Ke & Weiming He

  2. Department of Precision Mechanics, Faculty of Science and Engineering, Chuo University, Tokyo, Japan

    Yi Gan, Weiming He & Tohru Ihara

  3. Advanced Vocational Technical College of Shanghai University of Engineering Science, Shanghai, China

    Xiaofeng Zhu

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  1. Yi Gan
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  2. Bin Zhang
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Correspondence to Bin Zhang.

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Gan, Y., Zhang, B., Ke, C. et al. Research on Robot Motion Planning Based on RRT Algorithm with Nonholonomic Constraints. Neural Process Lett 53, 3011–3029 (2021). https://doi.org/10.1007/s11063-021-10536-4

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