A review on fission-fusion behavior in unmanned aerial vehicle swarm systems

doi:10.23919/JSEE.2025.000024

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (5): 1216-1234.doi: 10.23919/JSEE.2025.000024

• SYSTEMS ENGINEERING • Previous Articles

A review on fission-fusion behavior in unmanned aerial vehicle swarm systems

Wenrui DING¹(), Xiaorong ZHANG²^,³(), Yufeng WANG¹^,*(), Qingyi LIU¹(), Fuyuan MA²()

¹ Institute of Unmanned System, Beihang University, Beijing 100191, China
² School of Electronic Information Engineering, Beihang University, Beijing 100191, China
³ Shen Yuan Honors College, Beihang University, Beijing 100191, China

Received:2024-06-04 Online:2025-10-18 Published:2025-10-24
Contact: Yufeng WANG E-mail:ding@buaa.edu.cn;zhangxiaorong@buaa.edu.cn;wyfeng@buaa.edu.cn;liuqy671@buaa.edu.cn;fy_ma@buaa.edu.cn
About author:
DING Wenrui was born in 1979. She received her B.S. degree in computer science and technology, M.S. and Ph.D. degrees in information and communication engineering from Beihang University, China, in 1994, 2001, and 2006, respectively. She is a researcher at the Institute of Unmanned System, Beijing, Beihang University. Her research interests are intelligent perception technology for unmanned aerial vehicles (UAVs) and UAV image processing. E-mail: ding@buaa.edu.cn

ZHANG Xiaorong was born in 1995. He received his B.S. degree in transportation engineering with the School of Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2020. He is pursuing his Ph.D. degree in Beihang University, Beijing, China. His research interests include methodologies for unmanned aerial vehicle swarm subset synthesis and integration, and advanced task planning strategies. E-mail: zhangxiaorong@buaa.edu.cn

WANG Yufeng was born in 1994. He received his B.S. degree in information and communication engineering in Northwestern Polytechnical University, Xi’an, China, in 2016, and Ph.D. degree from Beihang University. He is an associate professor in Beihang University, China, in 2021. His research interests include computer vision, machine learning, and swarm algorithms for unmanned aerial vehicles. E-mail: wyfeng@buaa.edu.cn

LIU Qingyi was born in 1987. She received her Ph.D. degree in solid mechanics from Beihang University, Nanjing, China, in 2014. She is an associate professor in Beihang University. Her research interests include architecture-based systems engineering, model-based systems engineering, and cyber-physical systems. E-mail: liuqy671@buaa.edu.cn

MA Fuyuan was born in 1997. He received his B.S. degree in mechatronic engineering with the North University of China, Taiyuan, China, in 2022. He is pursuing his Ph.D. degree in Beihang University, Beijing, China. His research interests include resource allocation for unmanned aerial vehicle communication, motion planning, and reinforcement learning. E-mail: fy_ma@buaa.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (U20B2042).

Abstract

Abstract:

The exploration of unmanned aerial vehicle (UAV) swarm systems represents a focal point in the research of multi-agent systems, with the investigation of their fission-fusion behavior holding significant theoretical and practical value. This review systematically examines the methods for fission-fusion of UAV swarms from the perspective of multi-agent systems, encompassing the composition of UAV swarm systems and fission-fusion conditions, information interaction mechanisms, and existing fission-fusion approaches. Firstly, considering the constituent units of UAV swarms and the conditions influencing fission-fusion, this paper categorizes and introduces the UAV swarm systems. It further examines the effects and limitations of fission-fusion methods across various categories and conditions. Secondly, a comprehensive analysis of the prevalent information interaction mechanisms within UAV swarms is conducted from the perspective of information interaction structures. The advantages and limitations of various mechanisms in the context of fission-fusion behaviors are summarized and synthesized. Thirdly, this paper consolidates the existing implementation research findings related to the fission-fusion behavior of UAV swarms, identifies unresolved issues in fission-fusion research, and discusses potential solutions.Finally, the paper concludes with a comprehensive summary and systematically outlines future research opportunities.

Key words: unmanned aerial vehicle (UAV), multi-swarm system, fission-fusion behavior, interaction mechanism, swarm control

Wenrui DING, Xiaorong ZHANG, Yufeng WANG, Qingyi LIU, Fuyuan MA. A review on fission-fusion behavior in unmanned aerial vehicle swarm systems[J]. Journal of Systems Engineering and Electronics, 2025, 36(5): 1216-1234.

Figures/Tables 7

Table 1

Homogeneous UAV swarm fission-fusion methods"

Factor	Fission-fusion method	Disadvantage	Advantage	Applicable scenario	Representative literature
Leader/virtual leader	Individuals follow different real or virtual leaders to fission-fusion by broadcasting or interaction	High requirement for perception, planning, and interaction ability of a selected leader	Leaders can be generated through designation or simple election to generate leaders	Small-scale swarms with advanced control center	[47,48]
Internal interaction force	Fission-fusion with internal rules and initial conditions	The global control parameter is needed to preset the forces in the known environment based on the control framework	Can be designed according to different scene requirements	Small-scale swarms with advanced control centers or any swarm without a center	[29,49−51]
Task assignment or negotiation mechanism	Fission-fusion by setting different tasks or goals for different individuals through pre-storage or real-time	Centralized coordination or distributed communication negotiation between UAVs is required	Fission-fusion campaigns can be carried out directly according to task requirements	Small-scale swarms with advanced control center	[28,52−54]
Interactive structure	Fission-fusion by controlling the interaction network between individual units in the swarm	The global network structure of the swarm is required to introduce centralized planning	Higher scene adaptability and control robustness	Global advanced control center for centralized planning and control	[27,42,55]
Self- organized excitability	Self-organized fission-fusion in external stimulation based on self-control structure	Requiring a high level of perception ability for UAV individuals, and recognition mechanism is needed	Fission-fusion can be performed for different stimuli and can be done directly through behaviour of the stimulus	Requires specific stimulation from the external environment, without restrictions on swarm size	[56−59]

Table 1

Table 2

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Category	Factor	Fission-fusion method and effect	Disadvantage	Advantage	Applicable scenario	Representative literature
Controlled	Self-performance of UAVs	Implement task-level fission-fusion based on the heterogeneity of UAVs	Require obvious heterogeneity between UAVs, global control and singularity grouping	Direct fission-fusion based on physical differences is possible with less computational effort	Small-scale swarms with a significant difference	[60,61]
Controlled	Coordination mechanisms such as task assignment or negotiation	Fission-fusion by setting different tasks or goals for different individuals through pre-storage or real-time	Centralized coordination is required to achieve multi-level task presetting	Fast implementation of swarms fission-fusion planning in smaller tasks	Small-scale swarm with global or advanced control centers	[62,63]
Self-organized	Heuristic algorithm	Self-organized fission-fusion and control based on intelligent learning networks with partial experience	Partial pre-learning is required, and requires high perceptual and computational abilities for heterogeneous UAVs	High efficiency of fission-fusion swarms with ground arithmetic guarantees	High-performance UAV swarm with network pre-learning	[64−69]
Self-organized	Control protocol and interaction structure	Self-organized fission-fusion by controlling the interaction topology between heterogeneous individuals and designing control protocols	The global network structure of the swarm is required to introduce centralized planning	Overall planning efficiency is higher in higher order controllers	Small-scale swarms with high-order controllers	[70−73]

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A review on fission-fusion behavior in unmanned aerial vehicle swarm systems

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