LF-ACO: an effective formation path planning for multi-mobile robot
Multi-robot path planning is a hot problem in the field of robotics. Compared with single-robot path planning, complex problems such as obstacle avoidance and mutual collaboration need to be considered. This paper proposes an efficient leader follower-ant colony optimization (LF-ACO) to solve the co...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-01-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022012?viewType=HTML |
| _version_ | 1798025134708097024 |
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| author | Liwei Yang Lixia Fu Ping Li Jianlin Mao Ning Guo Linghao Du |
| author_facet | Liwei Yang Lixia Fu Ping Li Jianlin Mao Ning Guo Linghao Du |
| author_sort | Liwei Yang |
| collection | DOAJ |
| description | Multi-robot path planning is a hot problem in the field of robotics. Compared with single-robot path planning, complex problems such as obstacle avoidance and mutual collaboration need to be considered. This paper proposes an efficient leader follower-ant colony optimization (LF-ACO) to solve the collaborative path planning problem. Firstly, a new Multi-factor heuristic functor is proposed, the distance factor heuristic function and the smoothing factor heuristic function. This improves the convergence speed of the algorithm and enhances the smoothness of the initial path. The leader-follower structure is reconstructed for the position constraint problem of multi-robots in a grid environment. Then, the pheromone of the leader ant and the follower ants are used in the pheromone update rule of the ACO to improve the search quality of the formation path. To improve the global search capability, a max-min ant strategy is used. Finally, the path is optimized by the turning point optimization algorithm and dynamic cut-point method to improve path quality further. The simulation and experimental results based on MATLAB and ROS show that the proposed method can successfully solve the path planning and formation problem. |
| first_indexed | 2024-04-11T18:13:47Z |
| format | Article |
| id | doaj.art-12c25f9030954673bed81c3fa9be11ff |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-11T18:13:47Z |
| publishDate | 2022-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-12c25f9030954673bed81c3fa9be11ff2022-12-22T04:10:01ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-01-0119122525210.3934/mbe.2022012LF-ACO: an effective formation path planning for multi-mobile robotLiwei Yang0Lixia Fu 1Ping Li 2Jianlin Mao3Ning Guo4Linghao Du5School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, ChinaSchool of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, ChinaMulti-robot path planning is a hot problem in the field of robotics. Compared with single-robot path planning, complex problems such as obstacle avoidance and mutual collaboration need to be considered. This paper proposes an efficient leader follower-ant colony optimization (LF-ACO) to solve the collaborative path planning problem. Firstly, a new Multi-factor heuristic functor is proposed, the distance factor heuristic function and the smoothing factor heuristic function. This improves the convergence speed of the algorithm and enhances the smoothness of the initial path. The leader-follower structure is reconstructed for the position constraint problem of multi-robots in a grid environment. Then, the pheromone of the leader ant and the follower ants are used in the pheromone update rule of the ACO to improve the search quality of the formation path. To improve the global search capability, a max-min ant strategy is used. Finally, the path is optimized by the turning point optimization algorithm and dynamic cut-point method to improve path quality further. The simulation and experimental results based on MATLAB and ROS show that the proposed method can successfully solve the path planning and formation problem.https://www.aimspress.com/article/doi/10.3934/mbe.2022012?viewType=HTMLmulti-robotleader follower-ant colony algorithm (lf-aco)formation path planningdynamic tangent point method |
| spellingShingle | Liwei Yang Lixia Fu Ping Li Jianlin Mao Ning Guo Linghao Du LF-ACO: an effective formation path planning for multi-mobile robot Mathematical Biosciences and Engineering multi-robot leader follower-ant colony algorithm (lf-aco) formation path planning dynamic tangent point method |
| title | LF-ACO: an effective formation path planning for multi-mobile robot |
| title_full | LF-ACO: an effective formation path planning for multi-mobile robot |
| title_fullStr | LF-ACO: an effective formation path planning for multi-mobile robot |
| title_full_unstemmed | LF-ACO: an effective formation path planning for multi-mobile robot |
| title_short | LF-ACO: an effective formation path planning for multi-mobile robot |
| title_sort | lf aco an effective formation path planning for multi mobile robot |
| topic | multi-robot leader follower-ant colony algorithm (lf-aco) formation path planning dynamic tangent point method |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022012?viewType=HTML |
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