Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known a priori. The problem is challenging in decentralized settings with robots that have limited communication and sensing capabilities. We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem, wherein a convolution neural network (CNN) processes localized perception; a graph neural network (GNN) facilitates robot communications; finally, a shallow multi-layer perceptron (MLP) computes robot actions. The GNN enables collaboration in the robot swarm by computing what information to communicate with nearby robots and how to incorporate received information. Evaluations show that the LPAC models -- trained using imitation learning -- outperform standard decentralized and centralized coverage control algorithms. The learned policy generalizes to environments different from the training dataset, transfers to larger environments with more robots, and is robust to noisy position estimates. The results indicate the suitability of LPAC architectures for decentralized navigation in robot swarms to achieve collaborative behavior.

翻译：覆盖控制问题是指引导机器人集群协同监测未知先验特征或感兴趣现象。该问题在机器人通信与感知能力有限的分散式场景中极具挑战性。本文提出一种面向该问题的可学习感知-动作-通信（LPAC）架构：其中卷积神经网络（CNN）处理局部感知信息，图神经网络（GNN）实现机器人间通信，浅层多层感知机（MLP）计算机器人动作。GNN通过计算机器人需与相邻机器人通信的信息类型以及如何融合接收信息，促进集群协作。评估表明，采用模仿学习训练的LPAC模型性能优于标准分散式与集中式覆盖控制算法。该学习策略可泛化至与训练数据不同的环境，可迁移至具有更多机器人的更大规模场景，并对位置估计噪声具有鲁棒性。研究结果表明LPAC架构适用于实现机器人集群分散式导航中的协作行为。