Path planning for oversized cargo vehicles considering three-dimensional collision risks

HAN Yanling; ZHANG Like; ZHOU Guofeng; CAO Shouqi; SONG Ge; WANG Jing; ZHANG Yun

doi:10.11959/j.issn.2096-6652.202539

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Path planning for oversized cargo vehicles considering three-dimensional collision risks

Papers and Reports | 更新时间：2026-03-18

- Path planning for oversized cargo vehicles considering three-dimensional collision risks
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 4, Pages: 454-467(2025)
- 作者机构：
  
  1.上海海洋大学信息学院，上海 201306
  2.上海海洋大学工程学院，上海 201306
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(42176175;42271335)
- DOI：10.11959/j.issn.2096-6652.202539
  CLC： TP39
- Received：23 July 2025，
  
  Revised：2025-09-29，
  
  Accepted：21 October 2025，
  
  Published：15 December 2025
- 稿件说明：
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韩彦岭,张力珂,周国峰等.考虑三维碰撞风险的大件物流车辆路径规划[J].智能科学与技术学报,2025,07(04):454-467.

HAN Yanling,ZHANG Like,ZHOU Guofeng,et al.Path planning for oversized cargo vehicles considering three-dimensional collision risks[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):454-467.
韩彦岭,张力珂,周国峰等.考虑三维碰撞风险的大件物流车辆路径规划[J].智能科学与技术学报,2025,07(04):454-467. DOI： 10.11959/j.issn.2096-6652.202539.

HAN Yanling,ZHANG Like,ZHOU Guofeng,et al.Path planning for oversized cargo vehicles considering three-dimensional collision risks[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):454-467. DOI： 10.11959/j.issn.2096-6652.202539.

摘要

考虑大件物流车辆三维碰撞风险，为提高车辆行驶路径平滑度及路径跟踪精度，提出了一种大件物流车辆闭环迭代路径规划方法。首先，在混合A*算法中引入大件物流车辆运动学约束规划基础路径；其次，结合控制器和车辆运动学模型进行基础路径优化，提高路径平滑度，降低车辆路径跟踪误差；同时，在路径优化过程中，利用Delaunay三角剖分得到大件物流货物四面体网格，进行精细化三维碰撞风险识别；基于风险识别结果，重新定义二维障碍物地图，进行闭环迭代规划，确定大件物流车辆最优路径。在3种典型场景中进行仿真测试，结果显示，相对于4种对比方法，所提方法能有效识别三维碰撞风险，规划出大件物流车辆最优安全路径，规划路径平均曲率为0.013 7 m

-1

，铰接角均低于24.59 °，均方根误差降幅达49.51%以上。

Abstract

To address three-dimensional collision risks associated with oversized cargo vehicles and enhance path smoothness and tracking accuracy

a closed-loop iterative path planning method is proposed for oversized cargo vehicles. First

a basic path is planned by incorporating kinematic constraints for oversized cargo vehicles into the hybrid A* algorithm. Second

the basic path is optimized by integrating the controller and vehicle kinematic model to improve path smoothness and reduce path tracking error. Concurrently

during path optimization

a tetrahedral mesh of the ov

ersized cargo obtained via Delaunay triangulation is utilized for refined 3D collision risk identification. Based on the risk identification results

the 2D obstacle map is redefined to perform closed-loop iterative planning

determining the optimal path for the oversized cargo vehicle. Through simulation testing in three typical scenarios

the proposed method effectively identifies 3D collision risks and plans safe paths compared with four alternative approaches. The average curvature of the planned paths is 0.013 7 m

-1

with articulation angles consistently below 24.59 °

and the root mean square error reduction exceeds 49.51 %

thereby planning optimal safe paths for large-item logistics vehicles.

关键词

Keywords

references

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