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Fusion Algorithm of the Improved A* Algorithm and Segmented Bézier Curves for the Path Planning of Mobile Robots

Author

Listed:
  • Rongshen Lai

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Zhiyong Wu

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Xiangui Liu

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China)

  • Nianyin Zeng

    (Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361005, China)

Abstract

In terms of mobile robot path planning, the traditional A* algorithm has the following problems: a long searching time, an excessive number of redundant nodes, and too many path-turning points. As a result, the shortest path obtained from planning may not be the optimal movement route of actual robots, and it will accelerate the hardware loss of robots. To address the aforementioned problems, a fusion algorithm for path planning, combining the improved A* algorithm with segmented second-order Bézier curves, is proposed in this paper. On the one hand, the improved A* algorithm is presented to reduce unnecessary expansion nodes and shorten the search time, which was achieved from three aspects: (1) the traditional 8-neighborhood search strategy was adjusted to 5-neighborhood according to the orientation of the target point relative to the current node; (2) the dynamic weighting factor of the heuristic function was introduced into the evaluation function of the traditional A* algorithm; and (3) the key node extraction strategy was designed to reduce the redundant nodes of the optimal path. On the other hand, the optimal path planned by the improved A* algorithm was smoothed using segmented second-order Bézier curves. The simulation results show that the improved A* algorithm can effectively reduce the search time and redundant nodes and the fusion algorithm can reduce the path curvature and path length to a certain extent, improving path safety.

Suggested Citation

  • Rongshen Lai & Zhiyong Wu & Xiangui Liu & Nianyin Zeng, 2023. "Fusion Algorithm of the Improved A* Algorithm and Segmented Bézier Curves for the Path Planning of Mobile Robots," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2483-:d:1051526
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    References listed on IDEAS

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    1. Dechao Chen & Zhixiong Wang & Guanchen Zhou & Shuai Li, 2022. "Path Planning and Energy Efficiency of Heterogeneous Mobile Robots Using Cuckoo–Beetle Swarm Search Algorithms with Applications in UGV Obstacle Avoidance," Sustainability, MDPI, vol. 14(22), pages 1-23, November.
    2. Biao Li & Tao Wang & Chunxiao Li & Zhen Dong & Hua Yang & Yi Sun & Pengfei Wang, 2022. "A Strategy for Determining the Decommissioning Life of Energy Equipment Based on Economic Factors and Operational Stability," Sustainability, MDPI, vol. 14(24), pages 1-24, December.
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    Cited by:

    1. Hui Guo & Zhaoxin Qiu & Guomin Gao & Tianlun Wu & Haiyang Chen & Xiang Wang, 2024. "Safflower Picking Trajectory Planning Strategy Based on an Ant Colony Genetic Fusion Algorithm," Agriculture, MDPI, vol. 14(4), pages 1-17, April.

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