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Path planning for intelligent vehicles based on improved D* Lite

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Abstract

Typical path planning algorithms are good for static obstacles avoidance, but not for dynamic obstacles, so path planning of intelligent vehicles in uncharted regions is a fundamental and critical problem. This study suggests an improved D* Lite algorithm to address the issues of large corner, node redundancy and close to obstacles in the path planned by D* Lite algorithm. Firstly, in order to increase the safety of the path, the D* Lite algorithm sets the safety distance between the intelligent vehicle and obstacles. Then, the kinematic constraints of intelligent vehicles are introduced to increase the path search direction and avoid path corners exceeding the steering maneuverability of intelligent vehicles. Next, the path is optimized, and the optimization process of removing redundant points is employed to tackle the problem of curved search path and redundant nodes, and the path is smoothed by using third-order Bezier curve to generate a path with continuous curvature. Finally, the enhanced D* Lite algorithm is fused with the improved dynamic window approach to achieve real-time obstacle avoidance based on the global optimal path for moving obstacles. Simulation studies in static and dynamic contexts are used to demonstrate the usefulness of the revised D* Lite algorithm. The results show that compared with other path planning methods, the path generated by the proposed method has more safety and smoothness features, and improves the path quality. Therefore, the proposed algorithm has certain effectiveness and superiority in path planning problems in static and dynamic environments.

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Data availability

The data presented in this study are available on request from the corresponding author.

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Funding

This work was supported by the Fundamental Research Funds for the Central Universities [Project No. 2018CDXYJX0019] and project supported by graduate scientific research and innovation foundation of Chongqing, China [Grant No. CYB19009].

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Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmissions, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing City, 400044, China

    Xiaomei Li, Ye Lu, Xiaoyu Zhao, Xiong Deng & Zhijiang Xie

  2. College of Mechanical and Vehicle Engineering, Chongqing University, No. 174 Shazheng Street, Shapingba District, Chongqing City, 400044, China

    Xiaomei Li, Ye Lu, Xiaoyu Zhao, Xiong Deng & Zhijiang Xie

  3. School of Mechanical and Electrical Engineering, Wuhan Institute of Technology, Wuhan City, 430200, China

    Xiong Deng

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  1. Xiaomei Li
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Contributions

Conceptualization and formal analysis, LXM and XZJ; methodology, LXM; supervision, DX and XZJ; writing—original draft, LXM; writing—review and editing, LXM, LY, ZXY, and DX; Funding acquisition, DX All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zhijiang Xie.

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Li, X., Lu, Y., Zhao, X. et al. Path planning for intelligent vehicles based on improved D* Lite. J Supercomput 80, 1294–1330 (2024). https://doi.org/10.1007/s11227-023-05528-1

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