Improved Safety-First A-Star Algorithm for Autonomous Vehicles

Yu, Junwei; Hou, Jing; Chen, Guang

doi:10.1109/icarm49381.2020.9195318

Cited by 33 publications

(24 citation statements)

References 16 publications

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“…In order to test the feasibility of this algorithm and the effect of path planning, MATLAB R2022b is used on 12th Gen Intel(R) Core(TM) i7-12700 2.10GHz computer to simulate the traditional A-Star algorithm, the algorithm in literature [10], the algorithm in literature [15] and the algorithm proposed in this paper in the same scene. Scenarios are divided into simple scenarios and complex scenarios.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…7-10 and 11-14 show the test results of each algorithm for path planning in the grid maps of simple and complex environments with sizes of 10×10, 30×30, 50×50 and 80×80, respectively. Where (a) is the path of the traditional A-Star algorithm, (b) is the path of the A-Star algorithm in the literature [10], (c) is the path of the A-Star algorithm in the literature [15], and (d) is the path of the improved A-Star algorithm in this paper. The green circle is the starting point, the yellow square is the goal point, the black grid is the obstacle, the blue region represents the node traversed and saved during the path finding process, the red region is the discarded node, the gray solid line is the final planned path, and the green node is the turning point on the path.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…When the map is large, the number of searched nodes will increase considerably, and the search efficiency is low. By introducing 20 neighborhoods and safe distance, the algorithm in literature [15] makes the planned route smoother, but the path finding time is considerably increased, and the number of searched nodes is also increased, resulting in low search efficiency.…”

Section: Simulation Experimentsmentioning

confidence: 99%

“…Xu et al expanded the 8 search neighborhoods of the traditional A* algorithm to 48 search neighborhoods, increased the search direction, and improved the efficiency of path planning [14] . Yu et al proposed to extend 20 neighborhoods and design a safe distance to make the planned path smoother [15] . Chen et al considered the safe distance of the robot and proposed A path planning method combining A* algorithm and Floyd algorithm to ensure the safety of the path [16] .…”

Section: Introductionmentioning

confidence: 99%

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Global path planning for mobile robots based on improved A-Star algorithm

Zhang,

Dong,

Zhang

et al. 2024

Preprint

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Aiming at the problems of lengthy planning time, easy to fall into local optimization, and insufficient ability to adapt to complex environments in the global path planning process of A-Star algorithm, we propose an improved A-Star algorithm. First, we introduce global obstacle information and local obstacle information into the heuristic function to adapt to different environmental requirements, and propose a U-shaped "trap" region filling strategy to reduce the number of searched nodes and improve the search efficiency. Secondly, we assign a priority to the search neighborhood and use directional gain to enhance the orientation of the search process. Then, we introduce a key node selection strategy to eliminate redundant nodes on the path and improve the smoothness of the path. Simulation results prove that the algorithm in this paper has significant improvement in terms of the number of traversed nodes, path nodes, path inflection nodes, turning angle, pathfinding time and path length. Finally, we experiment with the improved algorithm in a real environment, proving the effectiveness of the improved algorithm and demonstrating its ability to significantly enhance the performance of mobile robot path planning.

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Section: Simulation Experimentsmentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

Section: Simulation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global path planning for mobile robots based on improved A-Star algorithm

Zhang,

Dong,

Zhang

et al. 2024

Preprint

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“…In addition, the improved A* algorithm is combined with Q learning, and a dynamic exploration factor is proposed to solve the exploration-development dilemma of UAV local dynamic path adjustment. Yu et al 14 Aiming at the safety problem in automatic driving, the distance safety factor is added to the A* algorithm, and the path is smoothed, which improves the safety of the planned path. Shang et al 15 proposed an improved A* algorithm based on guide lines, and in order to improve the ability of avoiding obstacles, key nodes were added to the algorithm to guide the path.…”

Section: Introductionmentioning

confidence: 99%

Research on path planning method of curtain wall installation robot based on improved A* algorithm

Yang,

Wang,

Han

et al. 2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

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With the development of construction robot technology, the problem of path planning has received extensive attention. Aiming at the problem that the traditional path planning algorithm cannot plan the orientation, we propose an improved A* algorithm to improve the flexibility of robot movement. Firstly, the mathematical model of the robot system is established in the two-dimensional plane, and the environment is preprocessed. Secondly, the position planning method of A* algorithm is improved to improve the search efficiency of the algorithm. Finally, the optimization method of robot position and orientation is proposed, and the optimization function is added to the A* algorithm to improve the flexibility of the whole robot system. The results show that the improved A* algorithm can shorten the path length by 40.67 % and the turning angle by 10.29 %, which verifies the correctness of the proposed method. The research results of this paper have reference and guiding significance for solving the path planning problem of curtain wall installation robot.

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