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Research on Path Planning Based on Bidirectional A* Algorithm
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AI
DOI：10.1109/ACCESS.2024.3411872.png)
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摘要
En 中文
A refined bidirectional A $<^>{\ast }$ algorithm proposes to address the issue of imprecise path planning for unmanned mining vehicles navigating through complex open-pit mining terrains. To ensure the smooth traversal of these vehicles, a gradient factor incorporates into the cost function to circumvent obstacles along the pathway. Additionally, a weighted coefficient introduces into the heuristic function to fine-tune the combination of Euclidean and Manhattan distances, enhancing the accuracy of path distance measurement and ultimately leading to an optimal path. The enhanced algorithm adeptly simultaneously explores from both the starting and end points, significantly reducing search time and improving path planning efficiency. Utilizing the established map of the mining environment, an experiment for unmanned mining vehicle path planning devises, and a simulation test of the global path planning algorithm conducts using the MATLAB platform. The experimental results demonstrate that the refined bidirectional A $<^>{\ast }$ algorithm exhibits accelerated search speed and superior path planning effectiveness.
Keyword:
Heuristic algorithms
Costs
Prediction algorithms
Trajectory
Safety
Cost function
Collision avoidance
Path planning
Autonomous vehicles
Mining industry
A* algorithm
open-pit mining area
path planning
synchronous bidirectional search
unmanned mining truck
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
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NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
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