Aerial Robots Persistent Monitoring and Target Detection: Deployment and Assessment in the Field

2025-04-26

AI Summary

This paper addresses the robustness challenges faced by multi-UAV systems performing persistent surveillance and target detection in real-world outdoor environments, where both short-term disturbances (e.g., tracking errors, communication delays) and long-term failures (e.g., cyberattacks, severe link outages, battery depletion) occur. To this end, we propose a distributed cooperative framework that innovatively integrates time-reversed Kuramoto synchronization, 3D Lissajous trajectory planning, and model predictive control (MPC), enabling real-time fault-tolerant scheduling and recovery under dynamic conditions. The framework explicitly distinguishes and handles Type-I (transient) and Type-II (persistent) system failures. Extensive field experiments involving up to 11 UAVs validate the method’s effectiveness, resilience, and scalability, demonstrating significant improvements in sustained surveillance coverage and target detection success rate.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this manuscript, 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.

Problem

• Distributed algorithm for multi-robot persistent monitoring and target detection
• Integration of Time-inverted Kuramoto model, 3D Lissajous curves, and Model Predictive Control
• Ensuring operational efficiency under type I and type II failures

Innovation

• Integrates Time-inverted Kuramoto model for coordination
• Uses 3D Lissajous curves for trajectory planning
• Applies Model Predictive Control for robustness