Guofeng Peng
ABSTRACT
In addressing the path planning challenges encountered by Automated Guided Vehicles (AGVs) within smart warehousing transportation and storage, as well as the inherent issues of traditional A* algorithm, we propose an optimized and enhanced A* algorithm for path optimization. Building upon the conventional algorithm, the enhanced A* algorithm selects appropriate heuristic functions and weighting coefficients, integrating Bezier curves to achieve the final improved A* algorithm. The heuristic function is used to optimize the problem of long paths. Combined with Bezier curve, the path tortuous problem that occurs in the process of path planning is solved, and the path is smoother. The environmental description is conducted using a grid map constructed in Matlab, enabling simulation analysis of the algorithm within this grid-based environment. The simulation results reduce the path length by approximately 18.7% and the run time by 15.0%. The results analysis demonstrates that the enhanced A* algorithm not only effectively avoids obstacles but also identifies a better path. The enhanced A* algorithm yields shorter paths. Consequently, the enhanced A* algorithm proves to be a more accurate and efficient path planning technique, showcasing superior global search capabilities.
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Digital Library
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