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 article. 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.
Highlights
Multi-AUV target hunting is an ideal platform for the research of multi-agent cooperation and coordination, which is the theme that investigates the optimal cooperation hunting algorithms through multi-AUV cooperation and coordination in the dynamic process of many predators to capture many evaders [1]–[3]
The AUVs arrive at desired hunting points rapidly through deep reinforcement learning (DRL) algorithm to achieve hunting the moving target
In this article, a multi-AUV hunting algorithm based on the dynamic prediction of the target trajectory is proposed
Summary
Multi-AUV target hunting is an ideal platform for the research of multi-agent cooperation and coordination, which is the theme that investigates the optimal cooperation hunting algorithms through multi-AUV cooperation and coordination in the dynamic process of many predators to capture many evaders [1]–[3]. The research covers many disciplines and domain knowledge, such as real-time path planning, multi-AUV distributed coordination and control, planning and learning, competition, and cooperation within AUV groups, and so on [4]–[6]. The rectangular game model is a simple discretization scheme, which only allows evader and hunters to move in the horizontal or vertical direction. Korf [8] proposed a right-angle approximation scheme of the diagonal game model that allowed the robot to move in the diagonal direction.
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