基于图卷积模仿学习的分布式群集控制

针对受限通信条件下机器人群集协同控制问题，提出基于图卷积模仿学习的分布式群集控制策略. 该策略旨在实现群集内避障、速度一致性的基础上，提高群集鲁棒性，提升避免群集分裂的成功率. 提出基于熵评价的群集鲁棒性量化评价指标，建立节点和链路重要性的均衡分布与群集鲁棒性的联系. 提出重要度相关图卷积网络，用于实现受限通信条件下非欧氏数据的特征提取和加权聚合. 采用图卷积模仿学习方法，根据提升群集鲁棒性的要求设计集中式专家策略，通过对集中式专家策略的模仿，得到分布式群集协同控制策略. 设计仿真实验，证明所得的分布式策略基于受限通信条件实现了接近集中式的专家策略的控制效果.

Decentralized swarm control based on graph convolutional imitation learning

A distributed swarm control strategy based on graph convolutional imitation learning was proposed to deal with the cooperative control of robot swarms under restricted communication conditions. The strategy aimed to improve swarm robustness and enhance the success rate of avoiding swarm splitting based on achieving intra-swarm obstacle avoidance and velocity consistency. A quantitative evaluation index of swarm robustness based on entropy evaluation was proposed to establish the connection between the balanced distribution of node and link importance and cluster robustness. The importance-correlated graph convolutional networks were proposed to realize feature extraction and weighted aggregation of non-Euclidean data under restricted communication conditions. A centralized expert strategy was designed to improve swarm robustness, and the graph convolutional imitation learning method was adopted. Furthermore, a distributed swarm cooperative control strategy was obtained by imitating the centralized expert strategy. Simulation experiments demonstrate that the resulting distributed strategy achieves control effects close to those of the centralized expert strategy based on restricted communication conditions.