基于YOLO和PPO的无人机路径规划  

UAV Path Planning Based on YOLO and PPO

作　　者：张慧玉 刘磊[1] 闫冬梅 梁成庆 ZHANG Huiyu;LIU Lei;YAN Dongmei;LIANG Chengqing(School of Mathematics,Hohai University,Nanjing 211100,China;School of Modern Posts,Nanjing University Of Posts And Telecommunications,Nanjing 210003,China;School of Artificial Intelligence and Automation,Hohai University,Changzhou 213200,China)

机构地区：[1]河海大学数学学院,江苏南京211100 [2]南京邮电大学现代邮政学院,江苏南京210003 [3]河海大学人工智能与自动化学院,江苏常州213200

出　　处：《计算机与现代化》2025年第4期50-55,62,共7页Computer and Modernization

基　　金：河北省自然科学基金资助项目(A2023209002);安徽省重点实验室基金资助项目(KLAHEI18018);教育部重点实验室开放基金资助项目(Scip20240111)。

摘　　要：针对复杂多变的三维未知环境,设计一种基于深度强化学习的无人机路径规划方法,该方法在有限的观测状态下作出决策,解决高复杂度和不确定性带来的挑战。首先,在有限的感知范围内,利用YOLO网络提取图像中的障碍物信息;其次,提出危险度来解决不同时刻障碍物信息数量差异的问题,并将危险度提炼出的信息结合雷达信息作为智能体的输入;最后,在近端策略优化算法基础上,设计状态分解下的动作选择策略,以提升无人机动作的有效性。通过在Gazebo环境中进行仿真评估,实验结果表明,相较于近端策略优化算法每回合平均奖励提升了15.6个百分点,平均成功率提升了2.6个百分点。This paper proposes an unmanned aerial vehicle path planning method based on deep reinforcement learning for complex 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 evaluation 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 YOLOv4 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TP18[自动化与计算机技术—控制理论与控制工程]