Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control

Jiacheng Li; Wenhui Ma; YangWang Fang; Dengxiu Yu; C. L. Philip Chen

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > In Press, Accepted Manuscript

J. Li, W. Ma, Y. W. Fang, D. Yu, and C. Chen, “Collision-free maneuvering for a UAV swarm based on parallel control,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 4, pp. 1–15, Apr. 2025.

Citation:

J. Li, W. Ma, Y. W. Fang, D. Yu, and C. Chen, “Collision-free maneuvering for a UAV swarm based on parallel control,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 4, pp. 1–15, Apr. 2025.

J. Li, W. Ma, Y. W. Fang, D. Yu, and C. Chen, “Collision-free maneuvering for a UAV swarm based on parallel control,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 4, pp. 1–15, Apr. 2025.

Citation:

J. Li, W. Ma, Y. W. Fang, D. Yu, and C. Chen, “Collision-free maneuvering for a UAV swarm based on parallel control,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 4, pp. 1–15, Apr. 2025.

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Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control

Funds: This work was supported in part by the National Natural Science Foundation of China (62373302, 62333009, 61973253, 62273283)

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Author Bio:
Jiacheng Li recieved the B.S. degree in automation from Nanjing Agricultural University in 2020 and the M.S. degree in electronic information from Northwestern Polytechnical University in 2023. He is currently a Ph.D. candidate in electromechanical engineering with University of Macau, Macau, China. His current research interests include swarm security, swarm intelligent, and unmanned systems control

Wenhui Ma received the B.E., M.E., and Ph.D. degrees in control science and engineering from Northwestern Polytechnical University, in 2016, 2019, and 2024, respectively. Her current research interests include control theory and multi-agent consensus theory with their applications to aerospace systems

YangWang Fang received the Ph.D. degree in control science and engineering from Xi’an Jiaotong University in 1998. He is currently a Professor with Northwestern Polytechnical University and an Adjunct Professor with Xian Jiaotong University and the Xi’an University of Electronic Science and Technology. His research interests include aircraft guidance and control, stochastic optimal control, nonlinear control, and intelligent information processing. He is also the Executive Director of the Shaanxi Automation Society and the Shanxi Signal Processing Society

Dengxiu Yu (Member, IEEE) received the B.S. degree in mechanical design manufacture and automation and the M.S. degree in mechatronic engineering from Northwestern Polytechnical University in 2013 and 2016, respectively, and the Ph.D. degree in computer science from the University of Macau, Macau, China in 2019. He is currently an Associate Professor with the School of Artificial Intelligence, Optics and Electronics, Northwestern Polytechnical University. His current research interests include swarm intelligent and swarm control

C. L. Philip Chen (Fellow, IEEE) received the M.S. degree from the University of Michigan at Ann Arbor, USA in 1985, and the Ph.D. degree from Purdue University, USA in 1988, all in electrical and computer science. He is the Chair Professor and the Dean of the College of Computer Science and Engineering, South China University of Technology. He is the former Dean of the Faculty of Science and Technology, University of Macau, Macau, China. He is also a highly cited Researcher by Clarivate Analytics in 2018, 2019, 2020, and 2021. His research interests include cybernetics, computational intelligence, and systems. Dr. Chen is a Fellow of AAAS, IAPR, CAA, and HKIE; and a Member of Academia Europaea (AE) and European Academy of Sciences and Arts (EASA). He received the IEEE Norbert Wiener Award in 2018 for his contribution in systems and cybernetics, and machine learning. He was the Editor-in-Chief of IEEE Transactions on Cybernetics (2020–2021) after he completed his term as the Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics: Systems (2014–2019), followed by serving as the President of the IEEE Systems, Man, and Cybernetics Society from 2012 to 2013. Currently, he serves as the Deputy Director of CAAI Transactions on AI, an Associate Editor of IEEE Transactions on AI, IEEE Transactions On Systems, Man, and Cybernetics: Systems, and IEEE Transactions On Fuzzy Systems, and an Associate Editor of China Sciences: Information Sciences. He received Macau FDCT Natural Science Award three times and a first-rank Guangdong Province Scientific and Technology Advancement Award in 2019
Corresponding author: Dengxiu Yu, e-mail: yudengxiu@nwpu.edu.cn
Received Date: 2024-04-22
Revised Date: 2024-07-03
Accepted Date: 2024-07-17

Available Online: 2025-02-21

Abstract

Abstract

The maneuvering of a large-scale unmanned aerial vehicle (UAV) swarm, notable for flexible flight with collision-free, is still challenging due to the significant number of UAVs and the compact configuration of the swarm. In light of this problem, a novel parallel control method that utilizes space and time transformation is proposed. First, the swarm is decomposed based on a grouping-hierarchical strategy, while the distinct flight roles are assigned to each UAV. Then, to achieve the desired configuration (DCF) in the real world, a bijection transformation is conducted in the space domain, converting an arbitrarily general configuration (GCF) into a standard configuration (SCF) in the virtual space. Further, to improve the flexibility of the swarm, the time scaling transformation is adopted in the time domain, which ensures the desired prescribed-time convergence of the swarm independent of initial conditions. Finally, simulation results demonstrate that collision-free maneuvering, including formation changes and turning, can be effectively and rapidly achieved by the proposed parallel control method. Overall, this research contributes a viable solution for enhancing cooperation among large-scale UAV swarms.
- Bijection transform,
- large-scale UAV swarm,
- maneuvering,
- parallel control,
- prescribed-time

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References(43)

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Collision-Free Maneuvering for a UAV Swarm Based on Parallel Control

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