Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehicles
ObjectiveTo address the challenges of path planning for unmanned surface vehicles in complex waters, this paper proposes an improved ant colony optimization（ACO）algorithm based on uneven distributed pheromone and multi-objective optimization.MethodsFirst, a probabilistic roadmap method (PRM) is used...
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Editorial Office of Chinese Journal of Ship Research
2025-02-01
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| Series: | Zhongguo Jianchuan Yanjiu |
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| Online Access: | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.04207 |
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| author | Guobing XIE Wei HE Wangwen HU Yixin Su Binghua SHI |
| author_facet | Guobing XIE Wei HE Wangwen HU Yixin Su Binghua SHI |
| author_sort | Guobing XIE |
| collection | DOAJ |
| description | ObjectiveTo address the challenges of path planning for unmanned surface vehicles in complex waters, this paper proposes an improved ant colony optimization（ACO）algorithm based on uneven distributed pheromone and multi-objective optimization.MethodsFirst, a probabilistic roadmap method (PRM) is used to generate an initial path. Based on the orientation information of the initial path and the endpoint, the ACO algorithm is guided to unevenly distribute the initial pheromone, resulting in higher pheromone concentration of the initial path and endpoint while decreasing the pheromone concentration of other grids in mapping according to the initial path-endpoint distance. Therefore, the problem of the ants' blindness in the preliminary path search improved, the calculation time is shortened thereof. Next, an objective function is constructed for solving the multi-objective path planning problem, and the weights are set to balance the relationship among the safety index, the energy consumption, the tortuosity, so as to providing diversified path to meet the requirement for different scenarios, moreover adaptively adjust the increment of pheromone to strengthen the influence of high-quality path in the whole ants colony based on the pros and cons of the planed paths. Meanwhile, to optimize efficiency improvement, an adaptive adjustment strategy of heuristic matrix coefficient is established, incorporating cosine modulation factors pertaining to iteration numbers. To obtain the global optimal path, quadratic optimization is carried out to reduce turns and turning amplitudes. Finally, on the basis of the maps of two real lakes—Lake Xiangdao (Huangshi ) and Lake Qiandao ( Hangzhou)，the experiments are conducted to compare the effects of path planning using the proposed algorithm with that of other algorithms, i.e. traditional ACO, A* algorithm and improved ACO algorithm.ResultsThe results indicate that the proposed algorithm has the shortest planning paths, which is 61.71% shorter than that of the traditional ACO algorithm, the farthest distance from obstacles, and the smallest tortuosity. The running time of the algorithm is also improved. ConclusionThe experimental results show that the proposed algorithm can reduce energy consumption during navigation, as well as the number of turns and turning amplitude, improving the smoothness and safety of the planned path. |
| format | Article |
| id | doaj-art-64b97d33c79a4a68904f497c8fb6f4b7 |
| institution | OA Journals |
| issn | 1673-3185 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Editorial Office of Chinese Journal of Ship Research |
| record_format | Article |
| series | Zhongguo Jianchuan Yanjiu |
| spelling | doaj-art-64b97d33c79a4a68904f497c8fb6f4b72025-08-20T02:00:51ZengEditorial Office of Chinese Journal of Ship ResearchZhongguo Jianchuan Yanjiu1673-31852025-02-0120111512410.19693/j.issn.1673-3185.04207ZG4207Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehiclesGuobing XIE0Wei HE1Wangwen HU2Yixin Su3Binghua SHI4School of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Automation, Wuhan University of Technology, Wuhan 430070, ChinaHubei Key Laboratory of Digital Finance Innovation, Wuhan 430205, ChinaObjectiveTo address the challenges of path planning for unmanned surface vehicles in complex waters, this paper proposes an improved ant colony optimization（ACO）algorithm based on uneven distributed pheromone and multi-objective optimization.MethodsFirst, a probabilistic roadmap method (PRM) is used to generate an initial path. Based on the orientation information of the initial path and the endpoint, the ACO algorithm is guided to unevenly distribute the initial pheromone, resulting in higher pheromone concentration of the initial path and endpoint while decreasing the pheromone concentration of other grids in mapping according to the initial path-endpoint distance. Therefore, the problem of the ants' blindness in the preliminary path search improved, the calculation time is shortened thereof. Next, an objective function is constructed for solving the multi-objective path planning problem, and the weights are set to balance the relationship among the safety index, the energy consumption, the tortuosity, so as to providing diversified path to meet the requirement for different scenarios, moreover adaptively adjust the increment of pheromone to strengthen the influence of high-quality path in the whole ants colony based on the pros and cons of the planed paths. Meanwhile, to optimize efficiency improvement, an adaptive adjustment strategy of heuristic matrix coefficient is established, incorporating cosine modulation factors pertaining to iteration numbers. To obtain the global optimal path, quadratic optimization is carried out to reduce turns and turning amplitudes. Finally, on the basis of the maps of two real lakes—Lake Xiangdao (Huangshi ) and Lake Qiandao ( Hangzhou)，the experiments are conducted to compare the effects of path planning using the proposed algorithm with that of other algorithms, i.e. traditional ACO, A* algorithm and improved ACO algorithm.ResultsThe results indicate that the proposed algorithm has the shortest planning paths, which is 61.71% shorter than that of the traditional ACO algorithm, the farthest distance from obstacles, and the smallest tortuosity. The running time of the algorithm is also improved. ConclusionThe experimental results show that the proposed algorithm can reduce energy consumption during navigation, as well as the number of turns and turning amplitude, improving the smoothness and safety of the planned path.http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.04207unmanned vehiclesmotion planningmultiobjective optimizationant colony optimization algorithmuniformly distributed pheromoneprobabilistic roadmap method |
| spellingShingle | Guobing XIE Wei HE Wangwen HU Yixin Su Binghua SHI Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehicles Zhongguo Jianchuan Yanjiu unmanned vehicles motion planning multiobjective optimization ant colony optimization algorithm uniformly distributed pheromone probabilistic roadmap method |
| title | Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehicles |
| title_full | Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehicles |
| title_fullStr | Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehicles |
| title_full_unstemmed | Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehicles |
| title_short | Application of an improved ant colony algorithm based on unevenly distributed pheromone and multi-objective optimization in path planning for unmanned surface vehicles |
| title_sort | application of an improved ant colony algorithm based on unevenly distributed pheromone and multi objective optimization in path planning for unmanned surface vehicles |
| topic | unmanned vehicles motion planning multiobjective optimization ant colony optimization algorithm uniformly distributed pheromone probabilistic roadmap method |
| url | http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.04207 |
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