Multi-Agent Ergodic Surveillance under Unknown Target Distribution

This paper investigates the problem of multi-agent ergodic surveillance of multiple targets whose spatial distribution was previously unknown. A distributed adaptive control framework is developed, enabling agents to cooperatively estimate the target density and generate ergodic surveillance trajectories through local communication. The proposed method leverages a Fourier-based distributed density estimation approach, which is computationally efficient and robust to measurement noise. The Spectral Multi-scale Coverage (SMC) ergodic control strategy is extended to a decentralized, discrete-time formulation suitable for distributed implementation. Additionally, the Optimal Reciprocal Collision Avoidance (ORCA) algorithm is integrated to ensure collision avoidance among agents. The effectiveness and advantages of the proposed framework are demonstrated through simulations and real-world experiments, showing improved efficiency and accuracy in multi-agent surveillance tasks.