Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method
An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, th...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/11/1/4 |
| _version_ | 1797496914239815680 |
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| author | Haoting Liu Jianyue Ge Yuan Wang Jiacheng Li Kai Ding Zhiqiang Zhang Zhenhui Guo Wei Li Jinhui Lan |
| author_facet | Haoting Liu Jianyue Ge Yuan Wang Jiacheng Li Kai Ding Zhiqiang Zhang Zhenhui Guo Wei Li Jinhui Lan |
| author_sort | Haoting Liu |
| collection | DOAJ |
| description | An optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method. |
| first_indexed | 2024-03-10T03:11:08Z |
| format | Article |
| id | doaj.art-1725cf4909574ff69650eb056ca9414b |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-10T03:11:08Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-1725cf4909574ff69650eb056ca9414b2023-11-23T12:33:27ZengMDPI AGActuators2076-08252021-12-01111410.3390/act11010004Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony MethodHaoting Liu0Jianyue Ge1Yuan Wang2Jiacheng Li3Kai Ding4Zhiqiang Zhang5Zhenhui Guo6Wei Li7Jinhui Lan8Beijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology, Beijing 100083, ChinaScience and Technology on Near-Surface Detection Laboratory, Wuxi 214035, ChinaSchool of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UKJiuquan Satellite Launch Center, Jiuquan 732750, ChinaJiuquan Satellite Launch Center, Jiuquan 732750, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology, Beijing 100083, ChinaAn optimal mission assignment and path planning method of multiple unmanned aerial vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery UAV, image collection UAV, and communication relay UAV. When implementing the modeling and simulation, first, three threat sources are built: the weather threat source, transmission tower threat source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance, oil consumption, function descriptions of UAV, and threat source factors above are considered. The analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function. Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness function which considers the current and maximum iteration numbers is proposed to improve the AGA convergence performance. Finally, an optimal path plan between the neighboring mission points is computed by an improved artificial bee colony (IABC) method. A balanced searching strategy is developed to modify the IABC computational effect. Extensive simulation experiments have shown the effectiveness of our method.https://www.mdpi.com/2076-0825/11/1/4multiple UAVsmission assignmentpath planninggenetic algorithmartificial bee colonydisaster rescue |
| spellingShingle | Haoting Liu Jianyue Ge Yuan Wang Jiacheng Li Kai Ding Zhiqiang Zhang Zhenhui Guo Wei Li Jinhui Lan Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method Actuators multiple UAVs mission assignment path planning genetic algorithm artificial bee colony disaster rescue |
| title | Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method |
| title_full | Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method |
| title_fullStr | Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method |
| title_full_unstemmed | Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method |
| title_short | Multi-UAV Optimal Mission Assignment and Path Planning for Disaster Rescue Using Adaptive Genetic Algorithm and Improved Artificial Bee Colony Method |
| title_sort | multi uav optimal mission assignment and path planning for disaster rescue using adaptive genetic algorithm and improved artificial bee colony method |
| topic | multiple UAVs mission assignment path planning genetic algorithm artificial bee colony disaster rescue |
| url | https://www.mdpi.com/2076-0825/11/1/4 |
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