An Effective Strategy for Distributed Unmanned Underwater Vehicles to Encircle and Capture Intelligent Targets

Encircling and capturing foreign unmanned underwater vehicles (UUVs) by multiple defensive UUVs has recently attracted increasing attention. Previous studies using a self-organized map neural network for target allocation suffer from team imbalance and heavy communication burden problems. In addition, the cooperation of defensive UUVs in a team is insufficient. Aiming at these problems, this article forms balanced encircling teams with the estimated travel time acquired in local communication through distributed auction. The quantum particle swarm optimization algorithm is used to optimize the initial encircling circle for fast encirclement, and then dynamically shrink the encircling circle to improve cooperation. Finally, the encirclement control of the defensive UUV is achieved by a fast marching model predictive velocity planner and a proportional-integral controller. In real multi-island waters, this article adopts the proposed algorithm to simulate the encircling of foreign UUVs, which have the same speed as that of defensive UUVs. The simulation results show that the algorithm has advantages in shortening the chasing distance and improving the encircling efficiency. A pool validation experiment is also conducted to verify the effectiveness of the algorithm.