基于灰狼优化的自适应混合A*和人工势场的无人矿卡路径规划研究

韩彦岭; 吕金科; 周国峰; 张力珂; 张云

doi:10.11959/j.issn.2096-6652.202513

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基于灰狼优化的自适应混合A*和人工势场的无人矿卡路径规划研究

学术论文 | 更新时间：2025-07-21

- 基于灰狼优化的自适应混合A*和人工势场的无人矿卡路径规划研究
- Research on unmanned mining truck path planning based on grey wolf optimization adaptive hybrid A* and artificial potential field
- 智能科学与技术学报 2025年7卷第2期页码：246-256
- 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：
  
  [ "韩彦岭（1975- ），女，博士，上海海洋大学信息学院教授，主要研究方向为路径规划算法、基于深度学习的图像分类与识别。" ]
  [ "吕金科（1999- ），男，上海海洋大学信息学院硕士生，主要研究方向为路径规划算法、智能优化。" ]
  [ "周国峰（1985- ），男，博士，上海海洋大学工程学院讲师，主要研究方向为车辆动力学、建模、仿真和控制。" ]
  [ "张力珂（2001- ），男，上海海洋大学信息学院硕士生，主要研究方向为路径规划算法。" ]
  [ "张云（1974- ），男，博士，上海海洋大学信息学院教授，主要研究方向为导航系统的高精度定位原理、导航系统反射信号的海洋遥感技术。" ]
- 基金信息：
  
  国家自然科学基金项目(42176175;42271335)
- DOI：10.11959/j.issn.2096-6652.202513
  中图分类号： TP39
- 收稿：2024-11-28，
  
  修回：2025-03-04，
  
  纸质出版：2025-06-15
- 稿件说明：
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韩彦岭,吕金科,周国峰等.基于灰狼优化的自适应混合A*和人工势场的无人矿卡路径规划研究[J].智能科学与技术学报,2025,07(02):246-256.

HAN Yanling,LYU Jinke,ZHOU Guofeng,et al.Research on unmanned mining truck path planning based on grey wolf optimization adaptive hybrid A* and artificial potential field[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):246-256.
韩彦岭,吕金科,周国峰等.基于灰狼优化的自适应混合A*和人工势场的无人矿卡路径规划研究[J].智能科学与技术学报,2025,07(02):246-256. DOI： 10.11959/j.issn.2096-6652.202513.

HAN Yanling,LYU Jinke,ZHOU Guofeng,et al.Research on unmanned mining truck path planning based on grey wolf optimization adaptive hybrid A* and artificial potential field[J].Chinese Journal of Intelligent Science and Technology,2025,07(02):246-256. DOI： 10.11959/j.issn.2096-6652.202513.

摘要

为了提升无人驾驶技术在露天矿区应用中的路径规划能力，提出了一种基于灰狼优化的自适应混合A*和人工势场（grey wolf optimization-based adaptive hybrid A* and artificial potential field，GWO-HAPF）方法。通过灰狼优化算法对混合A*的关键参数进行自适应调整，实现路径长度、平滑度和规划时间的多目标平衡，从而克服混合A*（hybrid A*，HA*）算法固定参数适应性不足的问题，大幅提升了全局路径规划的质量与适应性。在局部路径规划中，采用改进的人工势场法，优化斥力函数并增加逃出力，有效提升了实时避障的可行性和路径的平滑性。实验结果表明，相较于HA*算法和改进HA*（improved hybrid A*，IHA*）算法，GWO-HAPF方法在全局规划中的计算效率分别平均提升了约80%和14.9%，路径长度平均减少9.8%，平滑度平均提升超过53%；在局部路径规划中，其规划时间相较于IHA*算法提升了95.8%，平滑度为IHA*算法的10.19%，能够高效完成避障任务。研究结果表明，该方法在规划效率、路径长度、平滑性和实时避障方面表现出优势，体现了其在露天矿区无人矿卡路径规划中的实际应用价值。

Abstract

To enhance the path planning capabilities of unmanned mining trucks in open-pit mining scenarios

a grey wolf optimization-based adaptive hybrid A* and artificial potential field (GWO-HAPF)method was proposed. The proposed method emploied the grey wolf optimization algorithm to adaptively adjust the key parameters of the hybrid A*(HA*) algorithm

achieving a multi-objective balance among path length

smoothness

and planning time. This effectively overcame the HA* algorithm's limited adaptability to fixed parameter settings

significantly improving the quality and adaptability of global path planning. For local path planning

an improved artificial potential field method was adopted

optimizing the repulsive force function and incorporating an escape force mechanism

which effectively enhanced real-time obstacle avoidance feasibility and path smoothness. Experimental results demonstrate that

compared to the standard HA* algorithms and the improved hybrid A* (IHA*) algorithms

GWO-HAPF improves computational efficiency in global planning by an average of 80% and 14.9%

respectively

reduces path length by over 9.8%

and increases smoothness by over 53%. In local path planning

GWO-HAPF achieves a planning time that is 95.8% shorter than IHA*

while its smoothness improves to 10.19% of IHA*. These findings indicate that the proposed method exhibits outstanding advantages in planning efficiency

path length

smoothness

and real-time obstacle avoidance

showcasing its practical application value in path planning for unmanned mining trucks in open-pit mining scenarios.

关键词

Keywords

references

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