A path planning approach based on multi-direction A* algorithm for ships navigating within wind farm waters

Xie, Lei; Xue, Shuangfei; Zhang, Jinfen; Zhang, Mingyang; Tian, Wuliu; Haugen, Stein

doi:10.1016/j.oceaneng.2019.04.055

Cited by 90 publications

(32 citation statements)

References 32 publications

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“…The proportion of each component in F(P) can be changed by adjusting the values of β and γ. A feasible discrete trajectory for the eye-in-hand camera Γ = {P k |k = 0,1, � � � n} can be planned in Cartesian space by using F(P) and Eq (15). That is, a corresponding discrete trajectory in the image space X ¼ fs � k jk ¼ 0; 1; � � � ng is obtained.…”

Section: Trajectory Planning Methodsmentioning

confidence: 99%

“…The common methods in the trajectory planning include genetic algorithm [9][10], simulated annealing [11][12], artificial neural network [13][14], A � algorithm [15], vector field method [16], adaptive algorithm [17][18], particle swarm optimization algorithm [19][20], artificial potential field method [21][22], etc. Among these algorithms, the artificial potential field method has a simple structure, is convenient for real-time control on hardware entities, and can usually plan smoother and safer paths.…”

Section: Introductionmentioning

confidence: 99%

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Research on motion planning for an indoor spray arm based on an improved potential field method

et al. 2020

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The target spraying effect of spray robots mainly depends on the control performance of the spraying arm during the processes of aiming and tracking. To further improve the robustness of the endpoint control and positioning accuracy of the spray arm, an improved potential field algorithm for the motion planning and control of the spray arm is proposed based on prophase research. The algorithm introduces a velocity potential field, visual field constraints and joint position limit constraints into the traditional artificial potential field method. The velocity potential field is used to ensure that the target state of the spraying arm is at the same velocity as the target crop (relative velocity) to achieve stable target tracking. The visual field constraints and joint position limit constraints are utilized to ensure the efficiency of the visual servo control and the movement of the spray arm. The algorithm can plan a feasible trajectory for the spraying arm in Cartesian space and image space, and use the speed controller to control the spraying arm movement along the trajectory for aiming and tracking. Simulation analysis shows that the algorithm can plan better motion trajectories than the servo controller based on image moments in previous studies. In addition, the experimental results show that the algorithm can effectively improve the robustness of targeting and tracking control for the spray robot.

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Section: Trajectory Planning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on motion planning for an indoor spray arm based on an improved potential field method

et al. 2020

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“…Path planning is typically divided into global and local path planning based on what type of environmental information is known [9]. A known environment indicates that all information about obstacles and targets is known prior to launching a USV, and the vehicle plans its path navigation based on this prior knowledge, which primarily includes the visual graph method, cell decomposition, the A* algorithm, and the grid method [10][11][12][13]. Conversely, in unknown environments, vehicles know nothing or only some information about their environment prior to launch, and local path planning is primarily performed by artificial potential field methods, genetic algorithms and simulated annealing [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Obstacle Avoiding of the USV Based on Improved ACO-APF Hybrid Algorithm With Adaptive Early-Warning

et al. 2021

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Path planning is important to the efficiency and navigation safety of USV autonomous operation offshore. To improve path planning, this study proposes the improved ant colony optimization-artificial potential field (ACO-APF) algorithm, which is based on a grid map for both local and global path planning of USVs in dynamic environments. The improved ant colony optimization (ACO) mechanism is utilized to search for a globally optimal path from the starting point to the endpoint for a USV in a grid environment, and the improved artificial potential field (APF) algorithm is subsequently employed to avoid unknown obstacles during USV navigation. The primary contributions of this paper are as follows: (1) this paper proposes a new heuristic function, pheromone update rule, and dynamic pheromone volatilization factor to improve convergence and mitigate finding local optima with the traditional ant colony algorithm; (2) we propose an equipotential line outer tangent circle and redefine potential functions to eliminate goals unreachable by nearby obstacles (GNRONs) and local minimum problems, respectively; (3) to adapt the USV to a complex environment, this paper proposes a dynamic early-warning step-size adjustment strategy in which the moving distance and safe obstacle avoidance range in each step are adjusted based on the complexity of the surrounding environment; (4) the improved ant colony optimization algorithm and artificial potential field algorithm are effectively combined to form the algorithm proposed in this paper, which is verified as an effective solution for USV local and global path planning using a series of simulations. Finally, in contrast to most papers, we successfully perform field experiments to verify the feasibility and effectiveness of the proposed algorithm. INDEX TERMS-Unmanned surface vehicles (USVs); Path planning; Improved ant colony optimization-artificial potential filed (ACO-APF) algorithm; Unknown obstacle avoidance; Field experiment

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“…The data compression time could be reduced [13]. Xie et al proposed a modified A-Star algorithm, in which a scalar artificial potential field is introduced to solve the obstacle avoidance and the penalty mode is utilized to realize the crossing time [14]. It is a pity that the factors of wind, current, visibility are not considered.…”

Section: Introductionmentioning

confidence: 99%

Ship Route Planning Based on Double-Cycling Genetic Algorithm Considering Ship Maneuverability Constraint

et al. 2020

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In order to complete automatic route planning in the complex navigation environment, this paper proposes a quadratic optimization genetic algorithm combining the motion characteristics of the ships. Furthermore, the constraints of the maneuvering characteristics of ship are considered to calculate the accurate planning routes fast. First of all, the turning and speed reduction model of ships are established, which is the foundation for the accurate calculation of approach states between the own ship and target ships. Second, in order to check the position relationship between the planned route and the land objects (such as the islands and rocks) on the chart, a detection algorithm between lines and irregular polygon boundary based on quadtree method is acquired. Third, a new genetic algorithm based on double-cycling optimization is proposed by this paper. Aiming at the complex problem of comprehensive collision avoidance planning under the conditions of static and dynamic obstacles, this paper realizes an efficient and feasible solution of dynamic programming for ship route. Finally, the algorithm is verified on a test fireboat, and the experiment data prove the feasibility and effectiveness of the proposed algorithm by this paper.

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A path planning approach based on multi-direction A* algorithm for ships navigating within wind farm waters

Cited by 90 publications

References 32 publications

Research on motion planning for an indoor spray arm based on an improved potential field method

Research on motion planning for an indoor spray arm based on an improved potential field method

Path Planning and Obstacle Avoiding of the USV Based on Improved ACO-APF Hybrid Algorithm With Adaptive Early-Warning

Ship Route Planning Based on Double-Cycling Genetic Algorithm Considering Ship Maneuverability Constraint

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