Wang Ping, Zhang Hao, Tang Youming
School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China.
Key Laboratory of Advanced Design and Manufacturing of Passenger Vehicles of Fujian Province, Xiamen 361024, China.
Sensors (Basel). 2025 Apr 28;25(9):2786. doi: 10.3390/s25092786.
To address the issues of insufficient safety distance and unsmooth paths in AGV path planning for narrow road scenarios, this paper proposes a method that integrates Voronoi-skeleton-based custom layers with traditional cost maps. First, key nodes of the Voronoi skeleton are extracted to generate a custom layer, which is then combined with static, obstacle, and expansion layers to form a new four-layer network cost map. This approach accurately distinguishes obstacle influences and enhances algorithm robustness. The A* algorithm based on this new map guides the automated guided vehicle (AGV) to travel safely along the road center. Second, an improved A* algorithm is employed for global planning to ensure safe navigation. Finally, B-spline smoothing is applied to the global path to enhance the AGV's efficiency and stability in complex environments. The experimental results show that in narrow road scenarios, the proposed algorithm improves AGV path planning safety by 82%, reduces the number of spatial turning points by 55.85%, and shortens planning time by 48.98%. Overall, this algorithm significantly enhances the robustness and real-time performance of path planning in narrow roads, ensuring the AGV moves safely in an optimal manner.
为解决窄路场景下AGV路径规划中安全距离不足和路径不顺畅的问题，本文提出一种将基于Voronoi骨架的自定义层与传统代价地图相结合的方法。首先，提取Voronoi骨架的关键节点以生成自定义层，然后将其与静态层、障碍物层和扩展层相结合，形成新的四层网络代价地图。该方法能准确区分障碍物影响并增强算法鲁棒性。基于此新地图的A算法引导自动导引车（AGV）沿道路中心安全行驶。其次，采用改进的A算法进行全局规划以确保安全导航。最后，对全局路径应用B样条曲线平滑处理，以提高AGV在复杂环境中的效率和稳定性。实验结果表明，在窄路场景中，所提算法将AGV路径规划安全性提高了82%，将空间转折点数量减少了55.85%，并将规划时间缩短了48.98%。总体而言，该算法显著增强了窄路路径规划的鲁棒性和实时性能，确保AGV以最优方式安全移动。
