Application of Improved Astar Algorithm in Global Path Planning of Unmanned Vehicles

Xu, Zhe; Liu, Xin; Chen, Qianglong

doi:10.1109/cac48633.2019.8996720

Cited by 27 publications

(11 citation statements)

References 10 publications

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“…If the value of ℎ 𝑛 is low, the algorithm can obtain the shortest path, but the search operation is slow. If the value of ℎ 𝑛 is high, the search speed of A* algorithm will become faster, but the obtained path is not the optimal path [7]. Manhattan distance, Euclidean distance, Chebyshev distance, etc.…”

Section: Principle Of A* Algorithmmentioning

confidence: 99%

Application of A* algorithm in intelligent vehicle path planning

Wang¹,

Lu²,

Jin³

et al. 2022

Math. models, eng.

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Path planning is one of the important directions in the field of intelligent vehicles research. Traditional path planning algorithms generally use Dijkstra algorithm, Breadth-First-Search (BFS) algorithm and A* algorithm. Dijkstra algorithm is a search-based algorithm, which can search to an optimal path, but the disadvantage is too many expansion nodes, which leads to insufficient search efficiency. BFS algorithm is a heuristic search algorithm, which reduces the disadvantage of too many expansion nodes and improves the search efficiency by heuristic function. A* algorithm is a heuristic search algorithm that combines Dijkstra's algorithm and BFS algorithm, which has higher search efficiency and can search to an optimal path at the same time, but it is still lacking in the search mode and smoothness of the planned route. This paper first introduces the general path planning algorithm, then introduces and analyzes the A* algorithm, and proposes improvement measures for its shortcomings; finally, the executability and effectiveness of the improved algorithm are tested using simulation, and compared with the traditional A* algorithm, and the results show that the improved A* algorithm has good effect on path planning of intelligent vehicles.

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Section: Principle Of A* Algorithmmentioning

confidence: 99%

Application of A* algorithm in intelligent vehicle path planning

Wang¹,

Lu²,

Jin³

et al. 2022

Math. models, eng.

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show abstract

“…4 node, taking the No. 4 node to the safety exit, Since the solution of path planning is a string of node numbers from the start point to the end point, the length of the solution is different in different situations, so the firefly cannot move according to formula (3), so the reference 15 in this article is about the movement of the firefly Redesign.…”

Section: Algorithm Designmentioning

confidence: 99%

Application of distributed firefly algorithm in fire escape path planning

Xie

Ren²,

Zhang³

et al. 2022

6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022)

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With the expansion of the scale of high-rise buildings, the internal structure is becoming more and more complex. Fire, earthquake and other emergencies easily lead to people in the building unable to choose the best escape path because of panic. The slow escape speed and untimely treatment greatly increase the casualty rate, so it is very important to plan a reasonable escape path. Aiming at the problems of low accuracy and slow speed of traditional emergency escape path planning methods, the building environment model is built, and the distributed firefly algorithm is designed for the new intelligent building platform, and then the path planning method is studied. The algorithm sets the shortest escape path as the objective function. The simulation results show that the algorithm can converge faster and find the optimal path compared with the traditional firefly algorithm, and the accuracy rate is improved by 8% compared with the standard firefly algorithm, which verifies that the algorithm has a guiding role in emergency evacuation guidance.

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“…Zheng et al 11 combined the A * algorithm with the jump point search principle, and added the angle evaluation function to improve the search speed of the algorithm and make it find the optimal path faster. Xu et al 12 Aiming at the shortcomings of A* algorithm, such as low efficiency, poor real-time performance and no constraints, a constrained BB-A* algorithm is proposed. At the same time, restricted nodes and road sections are added in the search process of the algorithm, which makes it more meet the practical needs.…”

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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Application of Improved Astar Algorithm in Global Path Planning of Unmanned Vehicles

Cited by 27 publications

References 10 publications

Application of A* algorithm in intelligent vehicle path planning

Application of A* algorithm in intelligent vehicle path planning

Application of distributed firefly algorithm in fire escape path planning

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

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