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

Cao, Xiang; Xu, Xinyuan

doi:10.1109/access.2020.3013032

Cited by 23 publications

(7 citation statements)

References 32 publications

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“…In Figure 10, a target exhibiting distinct motion characteristics possesses a connection between its current state and its future state. The output of the action network a t is transformed into the control variable of the AUV and integrated into the dynamic equation to obtain the reward r t and the next state s t+1 ; (12) Convert states to sequences (s t , a t , r t , s t+1 ) and store them in Reply Buffer R; (13) Randomly sampled from the Reply Buffer as training data for the Actor-network and Critic network;…”

Section: Target State Prediction Methods Based On Lstmmentioning

confidence: 99%

“…Liang et al [ 11 ] successfully addressed the challenge of coordinated encircling of targets by AUV formations with varying motion capabilities through the application of a heuristic neural network approach. Cao et al [ 12 ] proposed a multi-AUV search algorithm based on dynamic prediction of the target’s motion trajectory. This allows AUV formation members to swiftly reach the desired hunting points, facilitating efficient pursuit and capture of the target.…”

Section: Introductionmentioning

confidence: 99%

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Research on Multiple-AUVs Collaborative Detection and Surrounding Attack Simulation

Wen,

Wang,

Zhou

et al. 2024

Sensors

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Due to limitations in operational scope and efficiency, a single Autonomous Underwater Vehicle (AUV) falls short of meeting the demands of the contemporary marine working environment. Consequently, there is a growing interest in the coordination of multiple AUVs. To address the requirements of coordinated missions, this paper proposes a comprehensive solution for the coordinated development of multi-AUV formations, encompassing long-range ferrying, coordinated detection, and surrounding attack. In the initial phase, detection devices are deactivated, employing a path planning method based on the Rapidly Exploring Random Tree (RRT) algorithm to ensure collision-free AUV movement. During the coordinated detection phase, an artificial potential field method is applied to maintain AUV formation integrity and avoid obstacles, dynamically updating environmental probability based on formation movement. In the coordinated surroundings attack stage, predictive capabilities are enhanced using Long Short-Term Memory (LSTM) networks and reinforcement learning. Specifically, LSTM forecasts the target’s position, while the Deep Deterministic Policy Gradient (DDPG) method controls AUV formation. The effectiveness of this coordinated solution is validated through an integrated simulation trajectory.

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Section: Target State Prediction Methods Based On Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Multiple-AUVs Collaborative Detection and Surrounding Attack Simulation

Wen,

Wang,

Zhou

et al. 2024

Sensors

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“…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.…”

Section: Algorithm 1 Follower Hfcmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Hamed

Hamlich

2021

mendel

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The cooperation between mobile robots is one of the most important topics of interest to researchers, especially in the many areas in which it can be applied. Hunting a moving target with random behavior is an application that requires robust cooperation between several robots in the multi-robot system. This paper proposed a hybrid formation control for hunting a dynamic target which is based on wolves’ hunting behavior in order to search and capture the prey quickly and avoid its escape and Multi Agent Deep Deterministic Policy Gradient (MADDPG) to plan an optimal accessible path to the desired position. The validity and the effectiveness of the proposed formation control are demonstrated with simulation results.

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“…Chen et al [17] were is no longer limited to homogeneous agents for hunting, further considering the collaborative hunting of heterogeneous underwater robots, proposing a new time-competitive mechanism to build an efficient dynamic hunting coalition. In order to improve the efficiency of target hunting for multi-AUVs, a hunting algorithm based on dynamic prediction of moving target trajectory was proposed [18] A negotiation method was used to allocate appropriate ideal hunting points for each underwater vehicle. Finally, the desired hunting points were quickly reached through the deep reinforcement learning (DRL) algorithm to achieve the hunting of moving targets.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

He,

Wang,

Liu

et al. 2024

Applied Sciences

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In this paper, we present a novel coordinated method tailored to address the dynamic multi-target hunting control problem in multi-agent systems, offering significant practical value. Our approach encompasses several key components: initially, we introduce a task allocation model that integrates a fuzzy inference system with a particle swarm optimization algorithm. This hybrid model efficiently allocates hunting tasks for scattered evading targets, effectively transforming the dynamic multi-target hunting problem into multiple dynamic single-target-hunting problems. This transformation enhances the speed and efficacy of task allocation. Subsequently, we propose an attraction/repulsive model grounded in potential field theory. This model facilitates the coordinated hunting of each target by organizing agents into subgroups. Relying solely on relative position and velocity information between agents and targets, our model simplifies computation, while maintaining effectiveness. Furthermore, the coordination of hunting activities for each target is achieved through a series of agent subgroups, guided by our proposed motion model. This systematic approach ensures a cohesive and efficient hunting strategy. Finally, we validate the effectiveness and feasibility of our proposed method through simulation results. These results provide empirical evidence of the method’s efficacy and potential applicability in real-world scenarios.

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

Cited by 23 publications

References 32 publications

Research on Multiple-AUVs Collaborative Detection and Surrounding Attack Simulation

Research on Multiple-AUVs Collaborative Detection and Surrounding Attack Simulation

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

Dynamic Multi-Target Self-Organization Hunting Control of Multi-Agent Systems

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