International Journal of Big Data Intelligent Technology, 2024, 5(1); doi: 10.38007/IJBDIT.2024.050113.
Tengsheng Yang, Huiheng Suo, Rui Rao, Jian Wu, Tao Sun, Xie Ma, Bibo Yu, Yingping Bai, Xiaoqin Li, Yuhan Sun, Xiushui Ma
Nanchang Hangkong University, Nanchang, China
In this paper, an improved A* algorithm based on fused Theta* is proposed. Firstly, the traditional search strategy of the A* algorithm is changed to a jumping search strategy, which intelligently determines which nodes need to be expanded and which nodes do not need to be expanded at each step of the expansion, based on the direction that the parent node is expanding from, and whether there is any obstacle and its location information around it, The idea of Theta* algorithm is also introduced to check whether the nodes in the final path can be directly connected, if so, the intermediate nodes are skipped and the intermediate path is pruned. The improved A* algorithm is then combined with the Bessel curve optimization algorithm to eliminate redundant inflection points in the robot's path, resulting in a smoother and near-optimal path. As analyzed by simulation experiments, compared to the traditional A* algorithm, the improved A* algorithm shortens the path length by 4.34%, reduces the computation time by 76.9%, and reduces the number of search nodes by 67.4%. The experimental results show that the fusion algorithm improves the efficiency of path planning, increases the stability and path smoothness, and is easier to apply in practice.
Improved A*, Bezier curves, Jump point search, Theta*, Path planning
Tengsheng Yang, Huiheng Suo, Rui Rao, Jian Wu, Tao Sun, Xie Ma, Bibo Yu, Yingping Bai, Xiaoqin Li, Yuhan Sun, Xiushui Ma. Improved A* Algorithm Based on Fused Theta Optimization. International Journal of Big Data Intelligent Technology (2024), Vol. 5, Issue 1: 112-127. https://doi.org/10.38007/IJBDIT.2024.050113.
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