Distributed Cooperative Multi-Target Search for an Autonomous Underwater Vehicle Swarm in Unknown 3D Underwater Environments

This paper investigates the problem of multi-target search by an Autonomous Underwater Vehicle (AUV) swarm in unknown three-dimensional (3D) underwater environments with obstacles under limited communication conditions. To address this problem, a distributed cooperative search framework is proposed. Within this framework, an adaptive dual-state search mechanism driven by a target response function is designed. This mechanism enables the swarm to transition between independent large-scale roaming search and precise cooperative search. On this basis, a multi-target search method is developed by integrating a virtual force model, motion-constrained 3D Particle Swarm Optimization (PSO), and a sectional 3D tangent-plane obstacle-avoidance method. Simulation results demonstrate the effectiveness and engineering feasibility of the proposed framework. Under the conditions of unknown terrains and communication limits, the AUV swarm can adaptively execute state transitions, safely avoid 3D obstacles, and complete multi-target search tasks. Specifically, as the swarm size increases from 30 to 60 AUVs, the mean number of iterations drops from 432.97 to 269.73, while the total energy consumption expectedly rises from 11.79 × 104 to 15.51 × 104, reflecting a well-balanced trade-off between efficiency and cost. This study provides a practical distributed control reference for AUV swarms in complex communication-constrained underwater scenarios.