Collision Detection and Avoidance for Multi-UAV based on Deep Reinforcement Learning
作者单位：College of Intelligence and Technology National University of Defense Technology Beijing Institute of Aerospace Systems Engineering
会议名称：《第40届中国控制会议》
会议日期：2021年
学科分类：08[工学] 081105[工学-导航、制导与控制] 082503[工学-航空宇航制造工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程]
关 键 词：Multi-UAV Collision Detection and Avoidance Fully-distributed Deep Reinforcement Learning
摘 要：In recent years, the demand for improving the autonomy of UAVs has continued to increase in the civilian and military fields. Collision detection and avoidance is one of the key technologies to this end. In this paper, we propose a fully-distributed collision detection and avoidance method for multi-UAV based on deep reinforcement learning. Different from traditional methods, we implement an end-to-end control, which takes the information of sensor, UAV status and destination as inputs, and directly outputs the control references. Besides, based on the PPO algorithm and the paradigm of centralized training and decentralized execution, we provide the design of the deep reinforcement leaning network and the policy update *** addition, we build a series of training and verification environments, including 2D Stage scenes and 3D Gazebo scenes. The experiment results show that our method can successfully avoid the obstacles and achieve no collision between UAVs.