2021/01/20 by Chao Yan, Xiaojia Xiang, Yan, Chao +5
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #FOS: Electrical engineering #Guidance and Control Systems #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Systems and Control (eess.SY) #UAV Applications and Optimization #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2101.08074
openalex publication_date 2021/01/20 · openalex created_date 2022/08/27 · openalex updated_date 2026/07/28
Developing the flocking behavior for a dynamic squad of fixed-wing UAVs is still a challenge due to kinematic complexity and environmental uncertainty. In this paper, we deal with the decentralized flocking and collision avoidance problem through deep reinforcement learning (DRL). Specifically, we formulate a decentralized DRL-based decision making framework from the perspective of every follower, where a collision avoidance mechanism is integrated into the flocking controller. Then, we propose a novel reinforcement learning algorithm PS-CACER for training a shared control policy for all the followers. Besides, we design a plug-n-play embedding module based on convolutional neural networks and the attention mechanism. As a result, the variable-length system state can be encoded into a fixed-length embedding vector, which makes the learned DRL policy independent with the number and the order of followers. Finally, numerical simulation results demonstrate the effectiveness of the proposed method, and the learned policies can be directly transferred to semi-physical simulation without any parameter finetuning.