农机化研究2025，Vol.47Issue(5)：15-21,7.DOI:10.13427/j.issn.1003-188X.2025.05.003
基于深度强化学习的除草机器臂路径规划研究
Path Planning of Weeding Robot Arm Based on Deep Reinforcement Learning
摘要
Abstract
The current state of research on active seedling avoidance path planning in the field of intelligent weeding ro-bots is inadequate.To address this issue,a new path planning algorithm was developed for gradient weeding robot manip-ulators,utilizing an improved depth deterministic strategy.The algorithm was enhanced through the incorporation of re-ward equipotential surfaces,which improved the performance of the DDPG algorithm.To validate the algorithm,a simu-lation training environment was constructed using CoppeliaSim software,where the algorithm was trained and verified.The results showed 93.36％success rate for weeding and 2.79％rate of seed injury.A test platform was built to conduct weeding tests in an actual environment,where the algorithm achieved 91.50％success rate for weeding and 2.82％rate of seed injury.These experimental findings demonstrated the efficacy of the proposed algorithm in reducing the damage to crop.关键词
除草机器人/深度强化学习/路径规划/人工势场法Key words
weeding robot/deep reinforcement learning/path planning/artificial potential field method分类
农业工程引用本文复制引用
杨卜,邬鑫,张梦磊,冯松科..基于深度强化学习的除草机器臂路径规划研究[J].农机化研究,2025,47(5):15-21,7.基金项目
国家青年自然科学基金项目(51804260) （51804260）
