煤田地质与勘探2024，Vol.52Issue(4)：152-163,12.DOI:10.12363/issn.1001-1986.23.11.0748
煤矿井下掘进机器人路径规划方法研究
Research on path planning methods for underground roadheader robots
摘要
Abstract
In order to solve the problems of difficulty and low efficiency in the movement of robotic roadheaders under conditions of non-full-section roadways in coal mines,the characteristics of unstructured environments in coal mines and the motion characteristics of robotic roadheaders were analyzed,and a path planning method for robotic roadheaders based on deep reinforcement learning was proposed.The tunnel environment was constructed in real time using depth cameras,a virtual model for detecting roadheader-tunnel collisions was established,collision detection was performed in a virtual environment using the hierarchical bounding box method,and an obstacle avoidance strategy under the restric-tions of tunnel boundary was developed.Considering the size of the roadheader robot and the single goal in the path planning process,the HER-SAC algorithm was proposed based on the traditional SAC algorithm by introducing the ret-rospective experience playback technology.The algorithm expands the target subset through the trajectory obtained by the initial target in the environment to increase training samples and training speed.On this basis,an agent was estab-lished based on the reward and punishment mechanism,and its state space and action space were defined according to the motion characteristics of the roadheader robot.The agent was trained using three algorithms under the same scenario,and the performances of these algorithms were comparatively analyzed using four indicators,namely,the average re-ward value,the maximum reward value,the number of steps to reach the maximum reward value,and robustness.In or-der to further verify the reliability of the proposed method,a virtual-real combination method was adopted,roadheader path planning was performed in two experimental scenarios set by adjusting the target position,and the results produced by the traditional SAC algorithm and the HER-SAC algorithm were compared.The results show that the HER-SAC al-gorithm converges faster and generally performs better than the PPO and SAC algorithms;in the two experimental scen-arios,the path planned by the HER-SAC algorithm is smoother and shorter than that planned by the traditional SAC al-gorithm,and the error between the end point of the path planned by the HER-SAC algorithm and the target position is less than 3.53 cm,indicating that the HER-SAC algorithm can effectively execute and complete path planning tasks.This study lays a theoretical foundation for autonomous transfer control of roadheader robots and provides a new ap-proach to the automation of coal mining equipment.关键词
掘进机器人/路径规划/深度强化学习/智能体/虚实结合/改进SAC算法/煤矿Key words
roadheader robot/path planning/deep reinforcement learning/agent/combination of virtual and real/im-proved SAC algorithm/coal mine分类
矿业与冶金引用本文复制引用
张旭辉,郑西利,杨文娟,李语阳,麻兵,董征,陈鑫..煤矿井下掘进机器人路径规划方法研究[J].煤田地质与勘探,2024,52(4):152-163,12.基金项目
国家自然科学基金项目(52104166) （52104166）
陕煤联合基金项目(2021JLM-03) （2021JLM-03）
中国博士后科学基金面上项目(2022MD723826) （2022MD723826）
陕西省重点研发计划项目(2023-YBGY-063) （2023-YBGY-063）
