The problem of decentralized multi-robot patrol has previously been approached primarily with hand-designed strategies for minimization of 'idlenes' over the vertices of a graph-structured environment. Here we present two lightweight neural network-based strategies to tackle this problem, and show that they significantly outperform existing strategies in both idleness minimization and against an intelligent intruder model, as well as presenting an examination of robustness to communication failure. Our results also indicate important considerations for future strategy design.

翻译：去中心化多机器人巡逻问题以往主要通过手工设计的策略来解决，旨在最小化图结构环境中各顶点的"空闲时间"。本文提出了两种基于轻量级神经网络的策略来处理该问题，并证明它们在空闲时间最小化和对抗智能入侵者模型方面均显著优于现有策略，同时考察了策略在通信故障情况下的鲁棒性。研究结果也为未来策略设计提供了重要参考。