Many-objective evolutionary algorithm based agricultural mobile robot route planning
•Many-objective optimization algorithm for the route planning of greenhouse robots.•Four up-to-date algorithms comparison based on a real greenhouse route planning problem.•HypE algorithm with best performance for many-objective route planning according to C-Metric. Agricultural robot technology has...
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| Veröffentlicht in: | Computers and electronics in agriculture Jg. 200; S. 107274 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.09.2022
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| ISSN: | 0168-1699, 1872-7107 |
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| Abstract | •Many-objective optimization algorithm for the route planning of greenhouse robots.•Four up-to-date algorithms comparison based on a real greenhouse route planning problem.•HypE algorithm with best performance for many-objective route planning according to C-Metric.
Agricultural robot technology has experienced rapid development in the past ten years, and agricultural robots have been used to implement various complex agricultural tasks. In these processes, route planning is an important guarantee for reducing navigation distance and saving total turning angle. However, minimizing the cost of the entire navigation process on the premise of completing agricultural work is difficult. Many-objective Evolutionary Algorithm is used to solve the route planning problem of agricultural mobile robots under the premise of minimizing navigation cost. By scanning the radar map of the greenhouse, the path between all target points is calculated by using the probabilistic roadmap (PRM), and the route planning of the agricultural robot is carried out according to the sum of the path length and the path angle. To determine the best route for agricultural mobile robots, four algorithms are compared: Hypervolume Estimation Algorithm (HypE), Grid-Based Evolutionary Algorithm (GrEA), Knee Point-Driven Evolutionary Algorithm (KnEA), and Non-dominated sorting genetic algorithm (NSGA-III). The quality of the solutions was compared using C-Metric, and it could verify that HypE offers the best performance among four algorithms. |
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| AbstractList | Agricultural robot technology has experienced rapid development in the past ten years, and agricultural robots have been used to implement various complex agricultural tasks. In these processes, route planning is an important guarantee for reducing navigation distance and saving total turning angle. However, minimizing the cost of the entire navigation process on the premise of completing agricultural work is difficult. Many-objective Evolutionary Algorithm is used to solve the route planning problem of agricultural mobile robots under the premise of minimizing navigation cost. By scanning the radar map of the greenhouse, the path between all target points is calculated by using the probabilistic roadmap (PRM), and the route planning of the agricultural robot is carried out according to the sum of the path length and the path angle. To determine the best route for agricultural mobile robots, four algorithms are compared: Hypervolume Estimation Algorithm (HypE), Grid-Based Evolutionary Algorithm (GrEA), Knee Point-Driven Evolutionary Algorithm (KnEA), and Non-dominated sorting genetic algorithm (NSGA-III). The quality of the solutions was compared using C-Metric, and it could verify that HypE offers the best performance among four algorithms. •Many-objective optimization algorithm for the route planning of greenhouse robots.•Four up-to-date algorithms comparison based on a real greenhouse route planning problem.•HypE algorithm with best performance for many-objective route planning according to C-Metric. Agricultural robot technology has experienced rapid development in the past ten years, and agricultural robots have been used to implement various complex agricultural tasks. In these processes, route planning is an important guarantee for reducing navigation distance and saving total turning angle. However, minimizing the cost of the entire navigation process on the premise of completing agricultural work is difficult. Many-objective Evolutionary Algorithm is used to solve the route planning problem of agricultural mobile robots under the premise of minimizing navigation cost. By scanning the radar map of the greenhouse, the path between all target points is calculated by using the probabilistic roadmap (PRM), and the route planning of the agricultural robot is carried out according to the sum of the path length and the path angle. To determine the best route for agricultural mobile robots, four algorithms are compared: Hypervolume Estimation Algorithm (HypE), Grid-Based Evolutionary Algorithm (GrEA), Knee Point-Driven Evolutionary Algorithm (KnEA), and Non-dominated sorting genetic algorithm (NSGA-III). The quality of the solutions was compared using C-Metric, and it could verify that HypE offers the best performance among four algorithms. |
| ArticleNumber | 107274 |
| Author | Wang, Yang Zhang, Xinhao Yang, Jinqi Guo, Yu Zhao, Ran Li, Daoliang |
| Author_xml | – sequence: 1 givenname: Xinhao surname: Zhang fullname: Zhang, Xinhao organization: National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China – sequence: 2 givenname: Yu surname: Guo fullname: Guo, Yu organization: National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China – sequence: 3 givenname: Jinqi surname: Yang fullname: Yang, Jinqi organization: Department of Chemical and Process Engineering, University of Surrey, Guildford GU2 7XH, UK – sequence: 4 givenname: Daoliang surname: Li fullname: Li, Daoliang organization: National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China – sequence: 5 givenname: Yang surname: Wang fullname: Wang, Yang email: andy_yangwang@cau.edu.cn organization: National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China – sequence: 6 givenname: Ran surname: Zhao fullname: Zhao, Ran email: ran.zhao@cau.edu.cn organization: National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China |
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| Cites_doi | 10.1007/s11831-020-09415-3 10.1023/A:1016639210559 10.1109/TEVC.2003.810758 10.1016/j.jclepro.2020.124843 10.3390/su13147722 10.1016/j.compag.2012.11.015 10.5424/sjar/2013113-3865 10.1002/rob.21525 10.1109/TEVC.2014.2378512 10.1023/A:1020568125418 10.1016/j.aej.2021.01.010 10.1155/2019/5219471 10.1016/j.compag.2019.01.016 10.1109/4235.797969 10.1016/j.robot.2013.09.004 10.1162/EVCO_a_00009 10.1007/s10846-020-01291-0 10.1109/TEVC.2012.2227145 10.1016/j.jclepro.2020.122695 10.1109/TEVC.2013.2281535 10.1007/s11119-017-9522-9 10.1109/TEVC.2007.892759 10.1016/j.biosystemseng.2015.12.004 10.1162/106365600568202 |
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| SubjectTerms | Agricultural mobile robot agriculture algorithms electronics Greenhouse greenhouses Many-objective optimization radar Route planning |
| Title | Many-objective evolutionary algorithm based agricultural mobile robot route planning |
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