汽车工程学报2026，Vol.16Issue(5)：758-769,12.DOI:10.3969/j.issn.2095‒1469.2026.05.04
基于探索参数自学习的无人矿卡路径规划方法
A Path Planning Method for Autonomous Mining Trucks Based on Self-Learning of Exploratory Parameters
摘要
Abstract
To ensure the safety and efficiency of the loading process for autonomous mining trucks,it is essential to plan a global path that satisfies the position and heading constraints of the loading point.Traditional path planning methods are inefficient due to fixed exploration parameters,while learning-based methods have difficulty adapting to complex mining environments because of limited training scenarios.To address these challenges,this paper proposes a Hybrid A* path planning method optimized using deep reinforcement learning,referred to as Deep Q Network-Hybrid A*(DQN-HA*).The proposed method integrates a DQN-based reinforcement learning framework with the Hybrid A* algorithm to adaptively optimize the exploration parameters,thereby improving both planning efficiency and generalization capability.Finally,the proposed algorithm was tested and verified using data from mining scenarios.The results show that,compared with traditional and learning-based algorithms,the proposed DQN-HA*significantly improves the computational efficiency and optimizes the path quality.关键词
自动驾驶/路径规划/深度强化学习/混合A*Key words
autonomous driving/path planning/deep reinforcement learning/hybrid A*分类
信息技术与安全科学引用本文复制引用
梁紫涵,王亚飞,章翼辰,秦涛,田磊,王学鹏..基于探索参数自学习的无人矿卡路径规划方法[J].汽车工程学报,2026,16(5):758-769,12.基金项目
国家自然科学基金项目(52372417) （52372417）
