Hunting Algorithm for Multi-AUV Based on Dynamic Prediction of Target Trajectory in 3D Underwater Environment

Abstract

In the research of multi-robot systems, multi-AUV (multiple autonomous underwater vehicles) cooperative target hunting is a hot issue. In order to improve the target hunting efficiency of multi-AUV, a multi-AUV hunting algorithm based on dynamic prediction for the trajectory of the moving target is proposed in this paper. Firstly, with moving of the target, sample points are updated dynamically to predict the possible position of a target in a short period time by using the fitting of a polynomial, and the safe domain of the moving target, which is a denied area for the hunting AUVs, is built to avoid the target's escape when it detects AUVs. Secondly, the method of negotiation is adopted to allocate appropriate desired hunting points for each AUV. Finally, the AUVs arrive at desired hunting points rapidly through deep reinforcement learning (DRL) algorithm to achieve hunting the moving target. The simulations show that hunting AUVs can surround the moving target of which the trajectory is unknown rapidly and accurately by the algorithm in the 3D environment with complex obstacles and results obtained is satisfactory. INDEX TERMS Multi-AUV hunting, dynamic prediction, deep reinforcement learning, desired hunting point XIANG CAO was born in Sichuan, China. He received the B.Sc. degree in electronic and information engineering from Southwest University, Chongqing, China, in 2004, and the M.Sc. degree in communication and information systems from

Hybrid Formation Control for Multi-Robot Hunters Based on Multi-Agent Deep Deterministic Policy Gradient

“…R f ← Dp In lines (2,5), the behavior of the follower is selected, and according to this behavior the desired position Dp is calculated. There are two cases for a desired destination when it is calculated, either the desired destination can be reached or this destination cannot be reached because of an obstacle.…”

“…Many researches have been done on hunting with multi-robot systems problems. Among them, there are many methods for hunting a target by means of several mobile robots that are based on generative adversarial network [1], dynamic prediction [2] and Deep Reinforcement Learning (DRL) [11,13]. The nature inspired methods [4] are effective in chasing a dynamic target with random behavior in real time in unexpected environments.…”