Abstract
Path planning is an NP-hard problem in road network environments. Considering that the existing path planning algorithms mainly have the problems of low smoothness and low search efficiency in generating paths in large-scale complex environments, an improved rapidly exploring random tree (RRT) algorithm is proposed in this paper. First, the grid method is applied to model the road network environment, and the RRT algorithm based on adjacency expansion is proposed to search the initial path. Then, the strategies of identifying paths and eliminating redundant paths are adopted, respectively, to further optimize the selected paths. Experimental results show that, compared with other path planning algorithms, our algorithm can achieve faster convergence speed, shorter search path, and better smoothness in a complex map of the environment.
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Funding
This work was supported by National Natural Science Foundation of China (No. 61972439 and No. 61702010), Key Program in the Youth Elite Support Plan in Universities of Anhui Province (No. gxyqZD2020004 and No. gxyqZD2019010), the University Synergy Innovation Program of Anhui Province (No. GXXT-2021-007), and Natural Science Foundation of Anhui Province (No. 2108085MF214).
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LS conceived, developed and tested the proposed algorithm. XD analyzed the data and wrote this manuscript. YL verified the analytical methods and supervised the findings of this work. PX, XZ and QY visualized the experimental results. KZ revised and improved the manuscript according to the comments of reviewers. All authors read and approved the final manuscript.
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Sun, L., Duan, X., Zhang, K. et al. Improved path planning algorithm for mobile robots. Soft Comput 27, 15057–15073 (2023). https://doi.org/10.1007/s00500-023-08674-z
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DOI: https://doi.org/10.1007/s00500-023-08674-z