多无人机协同监测与目标探测，抗干扰能力强，实测最多11架机成功运行。
Aerial Robots Persistent Monitoring and Target Detection: Deployment and Assessment in the Field
- 用反相库拉莫托模型+三维利萨茹曲线+预测控制实现分布式调度
- 在11架无人机上验证，对短时中断和长期故障均保持持续工作
- 适合需要高鲁棒性的实际空中监控场景，如应急响应或边境巡逻
本文提出一种用于多机器人持续监测与目标检测的分布式算法。通过将反相库拉莫托模型、三维利萨茹曲线与模型预测控制相结合，有效应对真实环境中的部署挑战。该方法在面对类型I（如跟踪误差、通信延迟）和类型II（如恶意攻击、严重通信故障、电池耗尽）故障时，仍能保证持续监测与目标探测能力。我们通过涉及最多十一架空中机器人的真实场外实验，验证了该方案的有效性、鲁棒性与可扩展性。
原文摘要 · Abstract (English)
In this article, we present a distributed algorithm for multi-robot persistent monitoring and target detection. In particular, we propose a novel solution that effectively integrates the Time-inverted Kuramoto model, three-dimensional Lissajous curves, and Model Predictive Control. We focus on the implementation of this algorithm on aerial robots, addressing the practical challenges involved in deploying our approach under real-world conditions. Our method ensures an effective and robust solution that maintains operational efficiency even in the presence of what we define as type I and type II failures. Type I failures refer to short-time disruptions, such as tracking errors and communication delays, while type II failures account for long-time disruptions, including malicious attacks, severe communication failures, and battery depletion. Our approach guarantees persistent monitoring and target detection despite these challenges. Furthermore, we validate our method with extensive field experiments involving up to eleven aerial robots, demonstrating the effectiveness, resilience, and scalability of our solution.
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