Optimization of Flying Ad Hoc Network Topology and Collaborative Path Planning for Multiple UAVs

Ming He; Peizhao Wang; Haihua Chen; Bin Sun; Hongpeng Wang

doi:10.1109/JAS.2025.125846

Volume 13 Issue 6

Jun. 2026

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 18.3, Top 1 (SCI Q1)

CiteScore: 28.2, Top 1% (Q1)
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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(6): 1339-1352

M. He, P. Wang, H. Chen, B. Sun, and H. Wang, “Optimization of flying ad hoc network topology and collaborative path planning for multiple UAVs,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1339–1352, Jun. 2026. doi: 10.1109/JAS.2025.125846

Citation:

M. He, P. Wang, H. Chen, B. Sun, and H. Wang, “Optimization of flying ad hoc network topology and collaborative path planning for multiple UAVs,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1339–1352, Jun. 2026. doi: 10.1109/JAS.2025.125846

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Optimization of Flying Ad Hoc Network Topology and Collaborative Path Planning for Multiple UAVs

doi: 10.1109/JAS.2025.125846

Funds: This work was supported by the National Natural Science Foundation of China (62373201, 61973173), the Technology Researchand Development Program of Tianjin (20YFZCSY00830, 18ZXZNGX00340)

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Author Bio:
Ming He received the M.Eng. and Ph.D. degrees in electrical engineering from Nankai University in 2002 and 2008, respectively. Since 1997, he has been with the College of Electronic Information and Optical Engineering, Nankai University, where he became a Professor in 2016. From 2004 to 2009, he held several visiting research appointments in Juelich Research Centre and Karlsruhe University, Germany. His research interests include the applications of high temperature superconductors, the generation and detection of THz signals, and microwave communications

Peizhao Wang received the M.Eng. degree in information and communication engineering from Nankai University in 2022. She is currently a Ph.D. degree candidate in electronic science and technology at the Department of Electronic Information Engineering, the College of Electronic Information and Optical Engineering, Nankai University. Her current research interests include topology optimization of heterogeneous moving node networks, digital signal processing, and wireless communication technology

Haihua Chen (Member, IEEE) received the Ph.D. degree in electrical and electronic engineering from the University of Hong Kong, China in 2006. From 2007 to 2010, she was a Postdoctoral Fellow in the Communication Systems Group, Darmstadt University of Technology, Germany. Beginning from September 2010, she has been part of the College of Electronic Information and Optical Engineering, Nankai University, where she is currently an Associate Professor. Her research interests include wireless networking and task allocation for unmanned intelligent systems, distributed beamforming, resource allocation in wireless networks, massive multiple-input multiple-output systems, broadband sensor arrays, and statistical array signal processing

Bin Sun received the M.Eng. degree in communication engineering from the College of Electronic Information and Optical Engineering, Nankai University in 2025. Since July 2025, he has been working at China Everbright Bank, China. His current research interests include wireless communication technology and multi-agent path planning

Hongpeng Wang (Member, IEEE) received the B.Sc. degree in physics and the Ph.D. degree in control theory and engineering from Nankai University in 2000 and 2009, respectively. He is currently a Professor with Nankai University. From Sep. 2007−Sep. 2008, Mar. 2016−Mar. 2017, he was a Visiting Research Fellow with the Department of Computer Science and Engineering, College Station, Texas A&M University, USA. His primary research interests include artificial intelligence and robotics, virtual reality and intelligent simulation. Dr. Wang is a Member of the IEEE Robotics and Automation Society, and also a Member of the IEEE Signal Processing Society
Corresponding author: Haihua Chen, e-mail: hhchen@nankai.edu.cn
Received Date: 2024-12-05
Accepted Date: 2025-08-06

Abstract

Abstract

Multiple unmanned aerial vehicles (UAVs) play a vital role in monitoring and data collection in wide area environments with harsh conditions. In most scenarios, issues such as real-time data retrieval and real-time UAV positioning are often disregarded, essentially neglecting the communication constraints. In this paper, we comprehensively address both the coverage of the target area and the data transmission capabilities of the flying ad hoc network (FANET). The data throughput of the network is therefore maximized by optimizing the network topology and UAV trajectories. The resultant optimization problem is effectively solved by the proposed reinforcement learning-based trajectory planning (RL-TP) algorithm and the convex-based topology optimization (C-TOP) algorithm sequentially. The RL-TP optimizes the UAV paths while considering the constraints of FANET. The C-TOP maximizes the data throughput of the network while simultaneously constraining the neighbors and transmit powers of the UAVs, which is shown to be a convex problem that can be efficiently solved in polynomial time. Simulations and field experimental results show that the proposed optimization strategy can effectively plan the UAV trajectories and significantly improve the data throughput of the FANET over the adaptive local minimum spanning tree (A-LMST) and cyclic pruning-assisted power optimization (CPAPO) methods.
- Convex optimization,
- flying ad hoc network (FANET),
- path planning,
- reinforcement learning (RL),
- topology control

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References(41)

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