火力与指挥控制2023，Vol.48Issue(12)：133-141,9.DOI:10.3969/j.issn.1002-0640.2023.12.020
基于深度强化学习的无人机实时航迹规划
Deep Reinforcement Learning-based UAV Real-time Trajectory Planning
舒健生 1周于翔 1郑晓龙 1赖晓昌 1陶大甜2
作者信息
- 1. 火箭军工程大学,西安 710025
- 2. 武汉理工大学信息工程学院,武汉 430070
- 折叠
摘要
Abstract
With the application and development of UAV technology,the flight environment of UAV is becoming more and more complex and changeable,the higher requirements for the obstacle avoidance ability and real-time path planning of UAVs are proposed.Based on the deep reinforcement learning algorithm with good generalization and weak dependence on the environment,the real-time path planning is carried out based on the obstacle map information obtained by radar in real time.The continuous reward function is designed according to the characteristics of the two-dimensional path planning problem,the problem of sparse reward in the two-dimensional plane path planning of the reinforcement learning algorithm is solved.Based on the idea of transfer learning,multiple training environments are designed and trained step by step according to the difficulty of the task,which reduces the training difficulty of the algorithm,improves the training effects and makes the convergence effects of the algorithm more stable.Finally,the SAC algorithm is compared with the current mainstream PPO and TD3 algorithms in the experiment.The experimental results show that the SAC algorithm has fast convergence speed,good real-time performance and better track smoothness.关键词
无人机/SAC算法/二维平面规划/实时航迹规划Key words
UAV/SAC algorithm/2D plane planning/real-time route planning分类
信息技术与安全科学引用本文复制引用
舒健生,周于翔,郑晓龙,赖晓昌,陶大甜..基于深度强化学习的无人机实时航迹规划[J].火力与指挥控制,2023,48(12):133-141,9.
