A new coverage path planning algorithm for unmanned surface mapping vehicle based on A-star based searching

Ma, Yong; Zhao, Yujiao; Li, Zhixiong; Yan, Xinping; Bi, Huaxiong; Królczyk, Grzegorz M.

doi:10.1016/j.apor.2022.103163

Cited by 22 publications

(6 citation statements)

References 20 publications

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“…It is a widely used method for global path planning in grid maps for its computational simplicity and ease of implementation [31]. To meet the diverse requirements of various applications, numerous enhancements have been made to the original A* algorithm [32][33][34]. Daniel et al [35] proposed a novel Theta* path-planning algorithm based on A* by smoothing the initial path generated.…”

Section: Global Path-planning Methodsmentioning

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: Global Path-planning Methodsmentioning

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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“…The algorithm was optimized for multiple constraint conditions, creating favorable conditions for scientific research and investigation. Yong Ma et al [16] proposed an IBA* algorithm for the coverage path planning of unmanned ground surveying vehicles, which overcomes the problem of insufficient continuity in the BA* algorithm through task decomposition and dynamic mapping updates. Experiments have shown that this algorithm can effectively ensure the safety of UGV during map mapping.…”

Section: Related Workmentioning

confidence: 99%

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Meng,

Fang

2024

Electronics

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Aiming at the problem of difficult obstacle avoidance for unmanned ground vehicles (UGVs) in complex dynamic environments, an improved A*-APF algorithm (BA*-MAPF algorithm) is proposed in this paper. Addressing the A* algorithm’s challenges of lengthy paths, excess nodes, and lack of smoothness, the BA*-MAPF algorithm integrates a bidirectional search strategy, applies interpolation to remove redundant nodes, and uses cubic B-spline curves for path smoothing. To rectify the traditional APF algorithm’s issues with local optimization and ineffective dynamic obstacle avoidance, the BA*-MAPF algorithm revises the gravitational field function by incorporating a distance factor, and fine-tunes the repulsive field function to vary with distance. This adjustment ensures a reduction in gravitational force as distance increases and moderates the repulsive force near obstacles, facilitating more effective local path planning and dynamic obstacle navigation. Through our experimental analysis, the BA*-MAPF algorithm has been validated to significantly outperform existing methods in achieving optimal path planning and dynamic obstacle avoidance, thereby markedly boosting path planning efficiency in varied scenarios.

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“…The defining feature of the A * algorithm is to establish a "Closed List" to record the evaluated areas, an "Edge List" to record those areas that have already been evaluated, and to calculate the distance from the "Starting Point" to the estimated distance to the "Target Point" [42][43][44]. This article applies this method to the iteration and position update process of GWO algorithm, partially solving the problem of falling into local optima.…”

Section: The Schematic Diagram Of the Mobile Charging Robot Modulementioning

confidence: 99%

Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots

Liu,

Xiao

2023

Robotica

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With the popularization of electric vehicles, early built parking lots cannot solve the charging problem of a large number of electric vehicles. Mobile charging robots have autonomous navigation and complete charging functions, which make up for this deficiency. However, there are static obstacles in the parking lot that are random and constantly changing their position, which requires a stable and fast iterative path planning method. The gray wolf optimization (GWO) algorithm is one of the optimization algorithms, which has the advantages of fast iteration speed and stability, but it has the drawback of easily falling into local optimization problems. This article first addresses this issue by improving the fitness function and position update of the GWO algorithm and then optimizing the convergence factor. Subsequently, the fitness function of the improved gray wolf optimization (IGWO) algorithm was further improved based on the minimum cost equation of the A* algorithm. The key coefficients AC1 and AC2 of two different fitness functions, Fitness1 and Fitness2, were discussed. The improved gray wolf optimization algorithm integrating A* algorithm (A*-IGWO) has improved the number of iterations and path length compared to the GWO algorithm in parking lots path planning problems.

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A new coverage path planning algorithm for unmanned surface mapping vehicle based on A-star based searching

Cited by 22 publications

References 20 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

A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field

Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots

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