A fast formation obstacle avoidance algorithm for clustered UAVs based on artificial potential field
The aim of this paper is to improve the rapid obstacle avoidance control of UAVs cluster in a complex obstacle environment, primarily utilizing the finite-time consistent formation control algorithm and the improved artificial potential field algorithm to design the fast obstacle avoidance control s...
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| Published in: | Aerospace science and technology Vol. 147; p. 108974 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Masson SAS
01.04.2024
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| ISSN: | 1270-9638, 1626-3219 |
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| Abstract | The aim of this paper is to improve the rapid obstacle avoidance control of UAVs cluster in a complex obstacle environment, primarily utilizing the finite-time consistent formation control algorithm and the improved artificial potential field algorithm to design the fast obstacle avoidance control strategy. Firstly, a finite-time consistent formation control algorithm is adopted to address the problems of slow formation speed and low control accuracy of UAVs clusters for establishing the formation model and control of UAVs cluster. Then, taking static and dynamic obstacles as obstacle avoidance targets, the improved artificial potential field algorithm is utilized, and the auxiliary potential field and dynamic situation field range of obstacle velocity are also introduced. The algorithm enhances obstacle avoidance speed and efficiency from the two aspects: time optimization and space optimization. Meanwhile, dynamic perturbation is introduced to address the local minimum problem of traditional artificial potential field. Finally, the effectiveness of the algorithm is confirmed through simulation on a verification platform and testing on a physical prototype verification platform. |
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| AbstractList | The aim of this paper is to improve the rapid obstacle avoidance control of UAVs cluster in a complex obstacle environment, primarily utilizing the finite-time consistent formation control algorithm and the improved artificial potential field algorithm to design the fast obstacle avoidance control strategy. Firstly, a finite-time consistent formation control algorithm is adopted to address the problems of slow formation speed and low control accuracy of UAVs clusters for establishing the formation model and control of UAVs cluster. Then, taking static and dynamic obstacles as obstacle avoidance targets, the improved artificial potential field algorithm is utilized, and the auxiliary potential field and dynamic situation field range of obstacle velocity are also introduced. The algorithm enhances obstacle avoidance speed and efficiency from the two aspects: time optimization and space optimization. Meanwhile, dynamic perturbation is introduced to address the local minimum problem of traditional artificial potential field. Finally, the effectiveness of the algorithm is confirmed through simulation on a verification platform and testing on a physical prototype verification platform. |
| ArticleNumber | 108974 |
| Author | Liu, Yunping Zhang, Tingting Gong, Yiguang Chen, Cheng Wang, Yan |
| Author_xml | – sequence: 1 givenname: Yunping surname: Liu fullname: Liu, Yunping organization: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, PR China – sequence: 2 givenname: Cheng surname: Chen fullname: Chen, Cheng email: chencheng_nuist@sina.com organization: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, PR China – sequence: 3 givenname: Yan surname: Wang fullname: Wang, Yan organization: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, PR China – sequence: 4 givenname: Tingting surname: Zhang fullname: Zhang, Tingting organization: School of Command and Control Engineering, Army Engineering University, Nanjing 210017, PR China – sequence: 5 givenname: Yiguang surname: Gong fullname: Gong, Yiguang organization: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, PR China |
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| Cites_doi | 10.1016/j.automatica.2010.09.005 10.1109/TASE.2018.2792327 10.1177/027836498600500106 10.1007/s12555-018-0320-6 10.1109/TRO.2007.900642 10.1109/TNSE.2019.2943023 10.1007/s00498-005-0151-x 10.1002/rnc.5641 10.1109/TIE.2014.2364982 10.1007/s11071-016-3155-9 10.1109/ACCESS.2019.2953946 10.1109/ACCESS.2018.2885003 10.1109/TAC.2005.854620 10.1016/j.ast.2017.07.012 10.1109/ACCESS.2019.2915594 10.1137/S0363012997321358 10.1109/ACCESS.2020.3028865 10.1016/0167-6911(92)90078-7 10.1103/PhysRevLett.96.104302 |
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| Title | A fast formation obstacle avoidance algorithm for clustered UAVs based on artificial potential field |
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