Genetic-Algorithm-Based Global Path Planning for AUV

Cao, Jian; Li, Ye; Zhao, Shiqi; Bi, Xiaosheng

doi:10.1109/iscid.2016.2027

Cited by 22 publications

(8 citation statements)

References 3 publications

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“…For polygonal obstacles, they are generally divided into convex polygonal and concave polygonal obstacles. In literature [29], the concave polygonal obstacles are expanded by the expansion method of convex polygonal obstacles, as shown in figure 2 (a), it can be seen that this way of inflation may lose the optimal path in the planning of collision avoidance, so this paper will design a applies to both the expansion method of the convex polygonal and concave polygonal obstacle, as shown in figure 2 (b).…”

Section: A: Viewable Inflation Methodsmentioning

confidence: 99%

Collision Avoidance Planning Method of USV Based on Improved Ant Colony Optimization Algorithm

Wang

Guo²,

Yao

et al. 2019

IEEE Access

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In order to solve the problem of insufficient search ability of the unmanned surface vehicle (USV) collision avoidance planning algorithm, this paper proposes an improved ant colony optimization algorithm (ACO). First, aiming at the static unknown environment, in order to improve the real-time performance of USV online planning, and considering the environmental characteristics of USV operation for improving ACO to search for the optimal path, a dynamic viewable method is proposed for the local environment model. Second, according to the known dynamic environment, based on the motion velocity model and International Regulations for Preventing Collisions at Sea (COLREGS), a reverse eccentric expansion method is designed to deal with the dynamic obstacles. Then, aiming at the problem that ACO has a slow convergence speed, an improved pseudo-random proportional rule is proposed to select the ant state transition. And the wolf pack allocation principle and the maximum-minimum ant system are used to update the global pheromone to avoid the search falling into local optimum. Finally, the convergence, real-time performance, and stability of the improved ACO are verified through the simulation experiment of USV collision avoidance in the static unknown and dynamic known environment.INDEX TERMS Unmanned surface vehicle (USV), ant colony optimization algorithm (ACO), viewable method, reverse eccentric expansion method, collision avoidance planning.

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Section: A: Viewable Inflation Methodsmentioning

confidence: 99%

Collision Avoidance Planning Method of USV Based on Improved Ant Colony Optimization Algorithm

Wang

Guo²,

Yao

et al. 2019

IEEE Access

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“…e algorithm improved the initial population generation method by detecting whether the connection between two adjacent points passed through obstacles and introduced a chamfer operator based on the traditional genetic operator to smooth the angle. e simulation results show that the algorithm accelerates the convergence speed while converging to the optimal solution [69]. However, the improved genetic algorithm uses a 2D plane figure to represent AUV's working space and does not consider the motion characteristics of AUV and the 3D underwater environment.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model search methods, random sampling methods, and intelligent bionic methods. In this review, we summarize in detail the development and application of various path planning algorithms in recent years. Meanwhile, we analyse the advantages and disadvantages of various algorithms and their improvement methods. Obstacles, ocean currents, and undersea terrain have an impact on AUV path planning. Therefore, how to deal with the complex underwater environment adds some limits to AUV path planning algorithms. In addition to the external environment, path planning algorithms also need to consider AUV’s physical constraints, such as energy constraints and motion constraints. Then, we analyse the motion constraints in AUV path planning. Finally, we discuss the development direction of AUV path planning algorithm. Time-varying ocean currents, special obstacles, multiobjective constraints, and practicability will be the problems that AUV path planning algorithms need to solve.

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“…In terms of mobile robot technology, path planning is a fundamental problem urgent to be solved in the application of robots [1]. The path planning problem can be generally divided into global path planning and local path planning in accordance with the robot's knowledge of the map [2,3]. Among the global path planning algorithms, the intelligent algorithms represented by ant colony algorithm [4][5][6], genetic algorithm [7,8], artificial neural network [9][10][11] and article swarm optimisation algorithm [12][13][14] possess high computational efficiency, but they tend to be trapped in predicament with local optimum and slow convergence rate.…”

Section: Introductionmentioning

confidence: 99%

A global path planning algorithm based on the feature map

Ren

Liu

2022

IET Cyber-Syst and Robotics

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The feature map is a characteristic of high computational efficiency, but it is seldom used in path planning due to its lack of expression of environmental details. To solve this problem, a global path planning algorithm based on the feature map is proposed based on the directionality of line segment features. First, the robot searches the path along the direction of the target position but turns to search in the direction parallel to the obstacle, which it approaches until the line between the robot and the target position does not intersect with obstacles. Then it turns to the target position, keep searching the path. Meanwhile, the problems of the direction selection of turning point, corner point and obstacle circumvention in the searching process are analysed and corresponding solutions are put forth. Finally, a path optimisation algorithm with variable parameters is proposed, making the optimised path shorter and smoother. Simulation experiments demonstrate that the proposed algorithm is superior to A* algorithm in terms of computation time and path length, especially of the computation efficiency. The cover image is based on the Research Article A global path planning algorithm based on the feature map by Peng Liu et al.,

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Genetic-Algorithm-Based Global Path Planning for AUV

Cited by 22 publications

References 3 publications

Collision Avoidance Planning Method of USV Based on Improved Ant Colony Optimization Algorithm

Collision Avoidance Planning Method of USV Based on Improved Ant Colony Optimization Algorithm

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

A global path planning algorithm based on the feature map

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