辽宁石油化工大学学报2026，Vol.46Issue(2)：88-96,9.DOI:10.12422/j.issn.1672-6952.2026.02.010
基于DDPG的自学习PID控制:无人机3D环境避障优化
Self-Learning PID Control Based on DDPG:Optimization of UAV Obstacle Avoidance in 3D Environments
摘要
Abstract
Navigation and obstacle avoidance are critical for the successful completion of UAV tasks.However,traditional autonomous flight systems face limitations in complex environments,prompting researchers to explore alternative frameworks such as deep reinforcement learning(DRL).This paper proposes a novel DRL-based autonomous control algorithm for UAVs,which integrates the Deep Deterministic Policy Gradient(DDPG)algorithm to self-learn an optimal Proportional-Integral-Derivative(PID)controller.The performance of the proposed algorithm is evaluated through simulations in the Gazebo 3D robotic simulator to validate its effectiveness under complex conditions.Results indicate that the proposed method outperforms numerous existing methods in dynamic environments,particularly in terms of improved stability,faster response speed,and higher success rates.关键词
避障/深度强化学习/自学习PID控制/加泽博仿真平台Key words
Obstacle Avoidance/Deep reinforcement learning/Self-learning PID control/Gazebo分类
信息技术与安全科学引用本文复制引用
高心悦,邹瑞元,李金娜..基于DDPG的自学习PID控制:无人机3D环境避障优化[J].辽宁石油化工大学学报,2026,46(2):88-96,9.基金项目
国家自然科学基金项目(62073158) （62073158）
辽宁省教育厅基本科研项目(LJKZ0401). （LJKZ0401）
