Motion Optimization Strategy for Passive Acoustic Monitoring With a Team of AUVs Considering Intermittent Communication

This article addresses the challenges of multiagent motion planning in underwater mobile sensor networks, driven by the growing use of autonomous underwater vehicles (AUVs) as mobile nodes. These systems are becoming essential for passive acoustic monitoring, which consists of cooperative pursuit and tracking of an underwater moving acoustic source. A fundamental constraint in underwater network systems is the reliance on acoustic communication, which suffers from high latency, packet loss, and severely limited bandwidth. In cooperative target tracking scenarios—used as a case study in this work—agents must exchange various types of data, such as measurements, target state estimates, and motion plans. Given the bandwidth limitations of underwater communication, prioritizing what information to transmit is critical. We embed event-triggered mechanisms within a distributed model predictive control (DMPC) framework to manage this heterogeneous communication traffic efficiently, thus improving cooperation without overloading the network. The proposed framework extends classical guidance, navigation, and control paradigms to distributed and cooperative settings, enabling more effective multi-AUV operations under strict communication constraints. The proposal is validated by reproducing complex underwater cooperative target tracking scenarios in simulation and then by comparing the performance to that obtained with classical DMPC applied in the same scenario. A statistical analysis is performed by varying both the simulation parameters and a key tuning coefficient of the methodology to assess the robustness and performance of the proposed framework under different operating conditions.