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.

翻译：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。具体而言，我们提出一种创新解决方案，该方案有效整合了时间反演仓本模型、三维利萨茹曲线与模型预测控制。我们重点关注该算法在飞行机器人上的实现，解决了在实际环境条件下部署本方法所涉及的技术挑战。我们的方法确保提供高效稳健的解决方案，即使在存在我们定义的I类与II类故障时仍能保持运行效能。I类故障指短时中断，如跟踪误差与通信延迟；II类故障则涉及长时中断，包括恶意攻击、严重通信故障及电池耗尽。本方法能够保证在这些挑战下实现持续监测与目标检测。此外，我们通过多达十一架飞行机器人的大规模现场实验验证了本方法的有效性、鲁棒性与可扩展性。