用轻量神经网络提升多机器人巡逻效率，抗干扰更强。
Lightweight Decentralized Neural Network-Based Strategies for Multi-Robot Patrolling
- 设计轻量神经网络策略，实现分布式自主巡逻
- 相比传统方法，空闲时间减少超30%，对抗智能入侵者更优
- 适合资源受限的实时多机协同场景
以往的去中心化多机器人巡逻问题主要依赖人工设计策略以最小化图结构环境中的顶点空闲时间。本文提出两种基于轻量神经网络的策略，实验表明其在空闲时间最小化及应对智能入侵者方面显著优于现有方法，并对通信故障下的鲁棒性进行了评估。结果揭示了未来策略设计的关键考量因素。
原文摘要 · Abstract (English)
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.
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