Robot path planning based on obstacle avoidance optimization and improved ant colony algorithm
Aimed at the problems of slow convergence speed and redundant planning paths in the processing of path planning by ant colony algorithm, an improved ant colony algorithm based on obstacle avoidance information and fast optimization search strategy was proposed. In order to improve the first search e...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of XPU
2024-06-01
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| Series: | Xi'an Gongcheng Daxue xuebao |
| Subjects: | |
| Online Access: | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1474 |
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| Summary: | Aimed at the problems of slow convergence speed and redundant planning paths in the processing of path planning by ant colony algorithm, an improved ant colony algorithm based on obstacle avoidance information and fast optimization search strategy was proposed. In order to improve the first search efficiency and accuracy of the ant colony, the Chebyshev distance was introduced to improve the distance heuristic function, and the guidance of the target point to the robot was enhanced in the transfer probability. The adaptive transfer probability was used to adjust the selection method of nodes during path planning and the setting of initial pheromones based on the distribution of obstacles around the nodes, and the percentage that the ants generate effective paths for the first time increased from 60% to 92%. The garbage information of the generated paths was removed, increasing the pheromone concentration of the optimal path nodes, balancing the local and global searching ability of the ant colony, and speeding up the optimal path. By smoothing the generated paths, the number of robot turns was reduced and the path distance was shortened. The algorithms SSA, ACO, IACO, and I-ACO were selected for performance testing on three grid environments. The results show that the improved ACO algorithm outperforms the other algorithms on path optimization. |
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| ISSN: | 1674-649X |