Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm
Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target trac...
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| Vydané v: | Aerospace science and technology Ročník 70; s. 497 - 510 |
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Elsevier Masson SAS
01.11.2017
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| ISSN: | 1270-9638, 1626-3219 |
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| Abstract | Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target tracking problem in urban environment is modeled by formulating the SUAVs kinematic and target models, the urban environment constraints, solar power harvesting and consumption models for SUAV, and sight occlusions by constructions. Especially, the sight occlusions in urban environment for SUAV are taken into consideration for the first time in this paper. A judgment method of sight occlusions for SUAV is proposed to make the calculation of the energy index more precise. Second, based on the precise modeling, the DMPC method is adopted as the framework for trajectory optimization in real time. Third, AGOA, a novel intelligent algorithm to mimic the behaviors of grasshoppers, is proposed to be the DMPC solver. The proposed AGOA has a better searching ability than the traditional GOA and some other intelligent algorithms by introducing some improvement measures e.g. the natural selection strategy, the democratic decision-making mechanism, and the dynamic feedback mechanism based on the 1/5 Principle. Finally, the effectiveness of the proposed method is demonstrated by the simulations. |
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| AbstractList | Aiming at the trajectory optimization of the solar-powered UAVs (SUAVs) cooperative target tracking in urban environment, the distributed model predictive control (DMPC) method based on Adaptive Grasshopper Optimization Algorithm (AGOA) is proposed in this paper. Firstly, the cooperative target tracking problem in urban environment is modeled by formulating the SUAVs kinematic and target models, the urban environment constraints, solar power harvesting and consumption models for SUAV, and sight occlusions by constructions. Especially, the sight occlusions in urban environment for SUAV are taken into consideration for the first time in this paper. A judgment method of sight occlusions for SUAV is proposed to make the calculation of the energy index more precise. Second, based on the precise modeling, the DMPC method is adopted as the framework for trajectory optimization in real time. Third, AGOA, a novel intelligent algorithm to mimic the behaviors of grasshoppers, is proposed to be the DMPC solver. The proposed AGOA has a better searching ability than the traditional GOA and some other intelligent algorithms by introducing some improvement measures e.g. the natural selection strategy, the democratic decision-making mechanism, and the dynamic feedback mechanism based on the 1/5 Principle. Finally, the effectiveness of the proposed method is demonstrated by the simulations. |
| Author | Wang, Honglun Yu, Yue Yao, Peng Huang, Yu Li, Na Wu, Jianfa Su, Zikang |
| Author_xml | – sequence: 1 givenname: Jianfa surname: Wu fullname: Wu, Jianfa organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China – sequence: 2 givenname: Honglun surname: Wang fullname: Wang, Honglun email: wang_hl_12@126.com, hl_wang_2002@126.com organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China – sequence: 3 givenname: Na surname: Li fullname: Li, Na organization: Unmanned System Research Institute, Beihang University, Beijing 100191, China – sequence: 4 givenname: Peng surname: Yao fullname: Yao, Peng organization: College of Engineering, Ocean University of China, Qingdao 266100, China – sequence: 5 givenname: Yu surname: Huang fullname: Huang, Yu organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China – sequence: 6 givenname: Zikang surname: Su fullname: Su, Zikang organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China – sequence: 7 givenname: Yue surname: Yu fullname: Yu, Yue organization: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China |
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| Keywords | Target tracking Solar-powered UAVs (SUAVs) Urban environment Adaptive Grasshopper Optimization Algorithm (AGOA) Sight occlusions Distributed model predictive control (DMPC) |
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| SubjectTerms | Adaptive Grasshopper Optimization Algorithm (AGOA) Distributed model predictive control (DMPC) Sight occlusions Solar-powered UAVs (SUAVs) Target tracking Urban environment |
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