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

Zhao, Zhenyi; Hu, Qiao; Feng, Haobo; Feng, Xinglong; Su, Wenbin

doi:10.3390/jmse10091266

Cited by 17 publications

(4 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Song proposed an improved artificial potential field method by improving parameters such as angle limit to solve the problem that the artificial potential field algorithm can easily fall into a particular terrain and improve the algorithm's stability [15]. Zhao, ZY proposed a hybrid adaptive preference method based on the improved artificial potential field (HAP-IAPF) [16]. Adaptive weight control units are used to adjust the preference strategy.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Obstacle Avoidance of Multi-AUV Based on Improved Artificial Potential Field Method and Consistency Protocol

Ning

2023

JMSE

View full text Add to dashboard Cite

Formation avoidance is one of the critical technologies for autonomous underwater vehicle (AUV) formations. To this end, a cooperative obstacle avoidance algorithm based on an improved artificial potential field method and a consistency protocol is proposed in this paper for the local obstacle avoidance problem of AUV formation. Firstly, for the disadvantage that the traditional artificial potential field method can easily fall into local minima, an auxiliary potential field perpendicular to the AUV moving direction is designed to solve the problem that AUVs can easily have zero combined force in the potential field and local minima. Secondly, the control law of AUV formation that keeps the speed and position consistent is designed for the problem that the formation will change during the local obstacle avoidance of the formation system. The control conflict problem of the combined algorithm of the artificial potential field law and the consistency protocol is solved by adjusting the desired formation of the consistency protocol through the potential field force. Finally, the bounded energy function demonstrates system convergence stability. The simulation verification confirmed that the AUV formation could achieve the convergence of the formation state under local obstacle avoidance.

show abstract

Section: Introductionmentioning

confidence: 99%

Coordinated Obstacle Avoidance of Multi-AUV Based on Improved Artificial Potential Field Method and Consistency Protocol

Ning

2023

JMSE

View full text Add to dashboard Cite

show abstract

“…This method has the advantages of low computational complexity, a simple environment, and high efficiency. Representative algorithms include a direct search based on potential field forces [ 22 ] and a formation search based on virtual structures [ 23 ]. Heuristic hunting by imitating hunting behavior in nature is a method that has a large number of assumptions and can effectively track targets in environments.…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Hunting Method for Multi-USV Based on the A* Algorithm in an Environment with Obstacles

Chen

Zhao

et al. 2023

Sensors

View full text Add to dashboard Cite

A single unmanned surface combatant (USV) has poor mission execution capability, so the cooperation of multiple unmanned surface ships is widely used. Cooperative hunting is an important aspect of multi USV collaborative research. Therefore, this paper proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles. First, based on the traditional A* algorithm, a path smoothing method based on USV minimum turning radius is proposed. At the same time, the post order traversal recursive algorithm in the binary tree method is used to replace the enumeration algorithm to obtain the optimal path, which improves the efficiency of the A* algorithm. Second, a biomimetic multi USV swarm collaborative hunting method is proposed. Multiple USV clusters simulate the hunting strategy of lions to pre-form on the target’s path, so multiple USV clusters do not require manual formation. During the hunting process, the formation of multiple USV groups is adjusted to limit the movement and turning of the target, thereby reducing the range of activity of the target and improving the effectiveness of the algorithm. To verify the effectiveness of the algorithm, two sets of simulation experiments were conducted. The results show that the algorithm has good performance in path planning and target search.

show abstract

“…However, due to the constraints of energy, AUVs are beginning to be overwhelmed when facing some demanding tasks. As a result, multi-AUV collaboration, information sharing, and joint task completion have become the new direction of AUV development [3,4]. For example, in marine-environment monitoring, if only a single AUV is used to complete marineenvironment-monitoring work, the efficiency will be low; if multiple AUVs are used to complete marine-environment-monitoring work, the efficiency will be greatly improved.…”

Section: Introductionmentioning

confidence: 99%

Robust Finite-Time Control of a Multi-AUV Formation Based on Prescribed Performance

Tian

Zhang

et al. 2023

JMSE

View full text Add to dashboard Cite

This paper focuses on the finite-time formation-control problem of a multi-AUV formation under unknown perturbations with prescribed performance. First, the nonlinear AUV model is transformed into a second-order integral model using feedback linearization. Suitable prescribed performance functions are selected to constrain the control errors of AUVs within a preset range and convert AUV tracking errors into unconstrained tracking errors using an error-conversion function to facilitate controller design. Finite-time sliding-mode disturbance observers are designed for unknown disturbances in the ocean so that they can accurately estimate the unknown disturbances in finite time. Based on the unconstrained tracking error and the unknown disturbance observer, the fast terminal sliding-mode formation controller is designed so that the multi-AUV formation can converge in finite time. Finally, the simulation experimental results show that the finite-time formation-control method with prescribed performance proposed in this paper can better cancel the unknown disturbance in the ocean in finite time and improve the robustness of the multi-AUV formation control.

show abstract

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

Cited by 17 publications

References 32 publications

Coordinated Obstacle Avoidance of Multi-AUV Based on Improved Artificial Potential Field Method and Consistency Protocol

Coordinated Obstacle Avoidance of Multi-AUV Based on Improved Artificial Potential Field Method and Consistency Protocol

A Cooperative Hunting Method for Multi-USV Based on the A* Algorithm in an Environment with Obstacles

Robust Finite-Time Control of a Multi-AUV Formation Based on Prescribed Performance

Contact Info

Product

Resources

About