计算机与现代化Issue(4)：50-55,62,7.DOI:10.3969/j.issn.1006-2475.2025.04.008
基于YOLO和PPO的无人机路径规划
UAV Path Planning Based on YOLO and PPO
摘要
Abstract
This paper proposes an unmanned aerial vehicle path planning method based on deep reinforcement learning for com-plex and ever-changing three-dimensional unknown environments.This method optimizes strategies within a limited observation space to address the challenges posed by high complexity and uncertainty.Firstly,within a limited perceptual range,the YOLO network is used to extract obstacle information from the image information.Secondly,this paper designs hazard levels to address the issue of varying amounts of obstacle information at different times,and combines the extracted information from hazard levels with radar information as input to the intelligent agent.Finally,based on the proximal strategy optimization algorithm,an action selection strategy under state decomposition is designed to improve the effectiveness of drone actions.Through simulation evalua-tion in Gazebo,the experimental results show that compared to the proximal strategy optimization algorithm,the average reward per round has increased by 15.6 percentage points,and the average success rate has increased by 2.6 percentage points.关键词
无人机/路径规划/深度强化学习/YOLOv4Key words
unmanned aerial vehicle/path planning/deep reinforcement learning/YOLOv4分类
航空航天引用本文复制引用
张慧玉,刘磊,闫冬梅,梁成庆..基于YOLO和PPO的无人机路径规划[J].计算机与现代化,2025,(4):50-55,62,7.基金项目
河北省自然科学基金资助项目(A2023209002) （A2023209002）
安徽省重点实验室基金资助项目(KLAHEI18018) （KLAHEI18018）
教育部重点实验室开放基金资助项目(Scip20240111) （Scip20240111）
