A traversal multi-target path planning method for multi-unmanned surface vessels in space-varying ocean current

Yu, Jiabin; Chen, Zhihao; Zhao, Zhiyao; Yao, Ping; Xu, Jun

doi:10.1016/j.oceaneng.2023.114423

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…Considering the effect of ocean currents on the path of USVs, Yu et al [52] proposed a traversal multi-target path-planning method for multiple USVs in space-varying ocean currents, which combined the Hungary algorithm and the virtual field RRT* (VF-RRT*) algorithm. Mahmoud Zadeh et al [53] presented an uninterrupted collision-free pathplanning system combined with a novel B-spline data frame and PSO, which facilitates the operational performance of USVs in an ocean-sampling mission.…”

Section: Path Planning In the Current Environmentmentioning

confidence: 99%

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Yang,

Pan,

Wei

et al. 2024

JMSE

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Ocean currents make it difficult for unmanned surface vehicles (USVs) to keep a safe distance from obstacles. Effective path planning should adequately consider the effect of ocean currents on USVs. This paper proposes an improved A* algorithm based on an artificial potential field (APF) for USV path planning in a current environment. There are three main improvements to the A* algorithm. Firstly, the proposed algorithm ignores unnecessary perilous nodes to decrease calculation. Secondly, an adaptive guidance angle is developed to guide the search in the most appropriate direction to reduce the computing time. Thirdly, the potential field force function is introduced into the cost function to ensure that the path designed for the USV always maintains a safe distance from obstacles under the influence of ocean currents. Furthermore, the Bezier curve is adapted to smooth the path. The experimental results show that the USV path-planning algorithm proposed in this paper, which synthesizes the APF and A* algorithms, runs 22.5% faster on average than the traditional A* algorithm. Additionally, the path developed by the proposed A* algorithm effectively keeps appropriate and different distances from obstacles by considering different ocean currents.

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Section: Path Planning In the Current Environmentmentioning

confidence: 99%

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Yang,

Pan,

Wei

et al. 2024

JMSE

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show abstract

“…In Ref. [ 72 ], double sampling points comparison and selection strategies are used to reduce the randomness of sampling points [ 127 ]. introduces a virtual field sampling algorithm and a current constraint function for multi-unmanned surface vehicle path planning under spatially varying currents.…”

Section: Overview Of Rrt-based Algorithm Improvementsmentioning

confidence: 99%

“…Multi-robot collaboration without human intervention is described in other sections, such as multi-robot systems with line-of-sight constrained communication [ 205 ], decentralized multi-UAV cooperative exploration [ 204 ], online cooperative path planning for multi-quadrotor maneuvering in unknown dynamic environments [ 147 ], formation shape generation for multi-UAV [ 40 ], multi-unmanned surface vehicle path planning under spatially varying currents [ 127 ], cooperative path planning for multiple shipboard aircraft [ 181 ]. However, in some tasks, the lack of guidance from human experience reduces the reliability of path planning.…”

Section: Recent Advances In the Application Of Rrt To Roboticsmentioning

confidence: 99%

Recent advances in Rapidly-exploring random tree: A review

2024

Heliyon

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“…The Six degrees of freedom mathematical model of the USV is simplified to the three degrees of freedom mathematical model of the USV. The three degrees of freedom mathematical model of the USV is represented as:

where η = [ x , y , ψ ] T is the pose vector, which defines the position and heading angle; υ = [ u , v , r ] T is the speed vector, which includes the forward, sway, and steering speeds; J ( η ) is the transformation matrix from the Earth coordinate system to the USV motion coordinate system [ 34 ].…”

Section: Modelingmentioning

confidence: 99%

“…where η = [x, y, ψ] T is the pose vector, which defines the position and heading angle; υ = [u, v, r] T is the speed vector, which includes the forward, sway, and steering speeds; J(η) is the transformation matrix from the Earth coordinate system to the USV motion coordinate system [34]. Modeling a real USV using the Unity3D 2020.3.19f1c2 platform to obtain a 3D model of the USV.…”

Section: Usv Modelingmentioning

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

Self Cite

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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.

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A traversal multi-target path planning method for multi-unmanned surface vessels in space-varying ocean current

Cited by 11 publications

References 22 publications

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Recent advances in Rapidly-exploring random tree: A review

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

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