Age of Information-Aware Multi-Objective Optimization for Heterogeneous UAV-USV-UUV Networks in Underwater Target Hunting

Underwater target hunting (UTH) is a critical and complex mission involving the search, tracking, and hunting of targets in an underwater environment. However, the unpredictable trajectories and flexibility of these targets, along with complex underwater environments, significantly impede the efficiency and success of traditional methods that depend solely on unmanned underwater vehicles (UUVs). Consequently, this paper presents the “3U network”, a novel heterogeneous framework integrating unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs), and UUVs for UTH. Within this framework, a UAV identifies the target, a USV acts as a communication relay, and a swarm of UUVs hunts the target. Moreover, to improve the timeliness of target detection, we incorporate the age of information (AoI) concept into the UAV’s search strategy. Additionally, we develop a constrained multi-objective optimization problem to minimize energy consumption and mission duration by optimizing vehicles’ trajectories, considering mobility limitations, safety, and connectivity constraints. Furthermore, to tackle this problem, we design an AoI- and energy-aware multi-vehicle twin-delayed deep deterministic policy gradient algorithm (AE-MVTD3) to optimize control policies for heterogeneous vehicles. The experimental results show that the proposed method performs effectively across diverse complex scenarios.