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Research on Path Planning Algorithm of Autonomous Vehicles Based on Improved RRT Algorithm

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Abstract

Recently, the path planning has become one of the key research hot issues in the field of autonomous vehicles, which has attracted the attention of more and more related researchers. When RRT (Rapidly-exploring Random Tree) algorithm is used for path planning in complex environment with a large number of random obstacles, the obtained path is twist and the algorithm cannot converge quickly, which cannot meet the requirements of autonomous vehicles’ path planning. This paper presents an improved path planning algorithm based on RRT algorithm. Firstly, random points are generated using the circular sampling strategy, which ensures the randomness of the original RRT algorithm and improves the sampling efficiency. Secondly, an extended random point rule based on cost function is designed to filter random points. Then consider the vehicle corner range when choosing the adjacent points, select the appropriate adjacent points. Finally, the B-spline curve is used to simplify and smooth the path. The experimental results show that the quality of the path planned by the improved RRT algorithm in this paper is significantly improved compared with the RRT algorithm and the B-RRT (Bidirectional RRT) algorithm. This can be seen from the four aspects of the time required to plan the path, mean curvature, mean square deviation of curvature and path length. Compared with the RRT algorithm, they are reduced by 55.3 %, 68.78 %, 55.41 % and 19.5 %; compared with the B-RRT algorithm, they are reduced by 29.5 %, 64.02 %, 39.51 % and 11.25 %. The algorithm will make the planned paths more suitable for autonomous vehicles to follow.

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Acknowledgements

This research was funded by National Social Science Fund National Emergency Management System Construction Research special project: Research on the path of improving the quality and upgrading of urban comprehensive emergency management capability under major emergencies, grant number 20VYJ023 and This research was funded by Key project of science and Technology Research Plan of Chongqing Education Commission: Theory and Practice of self-organization optimization of traffic block chain in interlaced area under the environment of intelligent network connection, grant number KJZD-K202000704.

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  1. Chongqing Jiaotong University, No. 66 Xuefu Road, Nanan Distr, Chongqing, 400074, China

    Guanghao Huang & Qinglu Ma

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  1. Guanghao Huang
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  2. Qinglu Ma
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Correspondence to Guanghao Huang or Qinglu Ma.

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Huang, G., Ma, Q. Research on Path Planning Algorithm of Autonomous Vehicles Based on Improved RRT Algorithm. Int. J. ITS Res. 20, 170–180 (2022). https://doi.org/10.1007/s13177-021-00281-2

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