A Cooperative Hunting Method for Multi-AUV Swarm in Underwater Weak Information Environment with Obstacles

Cooperative hunting is a typical task that reflects the intelligence level of a swarm. For the complex underwater weak information environment with obstacles, a problem description of the multi-autonomous underwater vehicle (AUV) cooperative hunting task is given, considering the influencing factors, including underwater obstacles, AUV sensing interaction range, and target escape strategy. A hybrid adaptive preference method based on improved artificial potential fields (HAP-IAPF) is proposed. Then the strategies of obstacle avoidance and hunting are designed separately according to the task requirements. The adaptive weight control unit is used to adjust the preference strategy. The multi-AUV cooperative hunting in dynamic obstacle underwater environments under weakly connected conditions are achieved. In order to prove the effectiveness of the proposed algorithm, simulation results compared with the traditional artificial potential field method and the optimized artificial potential field method are given in this paper. The results show that the proposed method is robust and effective in different environments.

Automated summary

In this study, a hybrid adaptive preference method based on improved artificial potential fields (HAP-IAPF) is proposed to achieve cooperative hunting tasks of multiple autonomous underwater vehicles (AUVs) in complex underwater environments with obstacles. Strategies for obstacle avoidance and hunting are separately designed, and an adaptive weight control unit is used to adjust the preference strategy. The proposed method is demonstrated to be robust and effective in different environments through simulation results comparing with traditional and optimized artificial potential field methods.