RflySim ToolChain: a rapid development and validation toolchain for intelligent unmanned swarm systems

doi:10.23919/JSEE.2025.000079

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (4): 1077-1093.doi: 10.23919/JSEE.2025.000079

• CONTROL THEORY AND APPLICATION • Previous Articles

RflySim ToolChain: a rapid development and validation toolchain for intelligent unmanned swarm systems

Xunhua DAI¹(), Jinhu TU²(), Quan QUAN³^,*()

¹ School of Computer Science and Engineering, Central South University, Changsha 410083, China
² School of Automation, Control Science and Engineering, Central South University, Changsha 410083, China
³ School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China

Received:2024-08-13 Online:2025-08-18 Published:2025-09-04
Contact: Quan QUAN E-mail:dai.xh@csu.edu.cn;tjhcsu@csu.edu.cn;qq_buaa@buaa.edu.cn
About author:
DAI Xunhua was born in 1990. He received his B.S., M.S., and Ph.D. degrees in control science and engineering at Beihang University, Beijing, China, in 2013, 2016, and 2020, respectively. Since 2020, he has been an associate professor with Central South University in computer science and engineering, where he is currently with the School of Computer Science and Engineering. His main research interests include reliable intelligent control, safety assessment, and design optimization of unmanned aerial vehicle. E-mail: dai.xh@csu.edu.cn

TU Jinhu was born in 1999. He received his B.S. degree in software engineering from Jiangxi University of Science and Technology in 2021 and M.S. degree in software engineering from Central South University in 2024. He is currently pursuing his Ph.D. degree in control science and engineering at Central South University. His main research interests include self-reinforcement safety learning, health management, and low-altitude traffic of unmanned aerial vehicle. E-mail: tjhcsu@csu.edu.cn

QUAN Quan was born in 1981. He received his B.S. and Ph.D. degrees in control science and engineering from Beihang University, Beijing, China, in 2004 and 2010, respectively. Since 2022, he has been a professor with Beihang University in control science and engineering, where he is currently with the School of Automation Science and Electrical Engineering. His research interests include reliable fight control, swarm intelligence, vision-based navigation, and health assessment. E-mail: qq_buaa@buaa.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (62406345).

Abstract

Abstract:

Developing intelligent unmanned swarm systems (IUSSs) is a highly intricate process. Although current simulators and toolchains have made a notable contribution to the development of algorithms for IUSSs, they tend to concentrate on isolated technical elements and are deficient in addressing the full spectrum of critical technologies and development needs in a systematic and integrative manner. Furthermore, the current suite of tools has not adequately addressed the challenge of bridging the gap between simulation and real-world deployment of algorithms. Therefore, a comprehensive solution must be developed that encompasses the entire IUSS development lifecycle. In this study, we present the RflySim ToolChain, which has been developed with the specific aim of facilitating the rapid development and validation of IUSSs. The RflySim ToolChain employs a model-based design (MBD) approach, integrating a modeling and simulation module, a lower reliable control module, and an upper swarm decision-making module. This comprehensive integration encompasses the entire process, from modeling and simulation to testing and deployment, thereby enabling users to rapidly construct and validate IUSSs. The principal advantages of the RflySim ToolChain are as follows: it provides a comprehensive solution that meets the full-stack development needs of IUSSs; the highly modular architecture and comprehensive software development kit (SDK) facilitate the automation of the entire IUSS development process. Furthermore, the high-fidelity model design and reliable architecture solution ensure a seamless transition from simulation to real-world deployment, which is known as the simulation to reality (Sim2Real) process. This paper presents a series of case studies that illustrate the effectiveness of the RflySim ToolChain in supporting the research and application of IUSSs.

Key words: simulation, unmanned aerial vehicle (UAV), swarm, RflySim, hardware-in-the-loop (HIL), model-based design (MBD), simulation to reality (Sim2Real)

Xunhua DAI, Jinhu TU, Quan QUAN. RflySim ToolChain: a rapid development and validation toolchain for intelligent unmanned swarm systems[J]. Journal of Systems Engineering and Electronics, 2025, 36(4): 1077-1093.

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Additional support	Target user	Learning objective
Provide introductory tutorials, video courses, advanced functions and customized solutions. Establish online forums and user communities to encourage communication and mutual assistance among users, and provide technical support and communication.	Academic researchers	Powerful support for complex algorithm development and validation, with detailed case studies, tutorials, extensive documentation, and sample code to get started quickly
	Industry engineer	Highlights methods for system integration, testing and deployment, and provides practical guidance, rich interfaces and technical support services to solve problems encountered in actual applications

Item	Minimum recommended specification
Operating system	Windows 10×64 (version 1 809 or higher)
CPU	Intel the 8th generation Core i7 processor or higher, or equivalent AMD processor
Graphics card	NVIDIA GTX 2060 or higher, or equivalent AMD graphics card
Memory	16 GB RAM or higher, DDR3 1 600 MHz or higher
Storage	At least 40 GB free space on the installation drive (SSD recommended)
Monitor	Resolution of at least 1 080 P (1 920×1080) or higher (dual monitors recommended)
Interface	At least one USB Type A port (expandable via adapters)

Mission scenario	Indicator	Value
Formation mission for a swarm of 100 UAVs	CPU usage/%	10
Formation mission for a swarm of 100 UAVs	Memory usage/GB	1.2
Simulation of a network of four UAVs with signal attenuation based on terrain	Average packet loss rate/%	27.6
	Average delay rate/ms	96.4
Validation of drone stationary flight accuracy (tracking error)	RMSE	$8{{{\mathrm{e}} - }}3 \pm 2{{{\mathrm{e}} - }}3$
Quality of data from an inertial measurement unit (error compared to ground truth)	RMSE	${{2{\mathrm{e}} - 2}} \pm 5{{{\mathrm{e}} - }}3$
Safety test for a fault where a motor stops running	Simulation credibility/%	64.02

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RflySim ToolChain: a rapid development and validation toolchain for intelligent unmanned swarm systems

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