Abstract
Aiming at the problem of low efficiency of mobile robot path planning in complex environments, based on the traditional A* algorithm and combined with the divide and conquer strategy algorithm, A four-way A* algorithm for a two-dimensional raster map is proposed in this paper. First, use random sorting and preprocessing to optimize the traditional A* algorithm and change the termination condition of the two-way A* algorithm expansion. Finally, use the start and end points to calculate the third node. The original problem is decomposed into a subproblem that simultaneously extends the four search trees from the starting point, intermediate point, and target point. After the pathfinding is successful, the paths of the subproblems are merged to get the optimal path. In addition, termination conditions have been added. While planning the four-way A* algorithm, it will also judge the one-way and two-way path planning so that the algorithm can find a path faster in a complex space. In order to verify the effectiveness of the improved algorithm, the improved algorithm and other algorithms are simulated in Matlab. The simulation results show that the path planning efficiency of the algorithm has been significantly improved, and as the scale of the environment increases, the advantages of the improved algorithm are more prominent.
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Hou, Y., Gao, H., Wang, Z., Du, C. (2022). Path Planning for Mobile Robots Based on Improved A* Algorithm. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_13
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DOI: https://doi.org/10.1007/978-981-19-6142-7_13
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