UAV-TISP: A simulation platform for collaborative task training and algorithm development in multi-rotor UAV clusters

To fulfill the training requirements for the daily operations of multirotor Unmanned Aerial Vehicles (UAV) clusters, a UAV cluster collaborative task integrated simulation platform (UAV-TISP), was developed. The platform integrates a suite of hardware and software to simulate a range of collaborative UAV cluster operation scenarios. It features modules for collaborative task planning, UAV cluster simulations, and tactical monitoring. The platform significantly reduces training costs by eliminating physical drone dependencies while offering a flexible environment for testing swarm algorithms. UAV-TISP supports both individual UAV and swarm operations, incorporating high-fidelity flight dynamics, real-time communication via user datagram protocol (UDP), and collision avoidance strategies. Utilizing the OSGEarth engine, it enables dynamic 3D environment visualization and scenario customization. Three key task scenarios—route flight, formation reconstruction, and formation transformation—were tested to validate the platform’s efficacy. Results demonstrated robust formation maintenance, adaptive collision avoidance, and seamless task execution. Comparative analysis with Gazebo Sim revealed lower trajectory deviations in UAV-TISP, highlighting its superior accuracy in simulating real-world flight dynamics. Future work will focus on enhancing scalability for diverse UAV models, optimizing swarm networking under communication constraints, and expanding mission scenarios. UAV-TISP serves as a versatile tool for both operational training and advanced algorithm development in UAV cluster applications.

Authors

Publication Details

Journal
Journal of Electronic Science and Technology
Published
2025-07-26
DOI
https://doi.org/10.1016/j.jnlest.2025.100327
Citations
1
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
1.49
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article

UAV-TISP: A simulation platform for collaborative task training and algorithm development in multi-rotor UAV clusters

Zhibin Xie, Kai Xi, Hongying Zhang, Xiaolong Wang et al.
1 citations
Journal of Electronic Science and Technology
Distributed Control Multi-Agent Systems
1.49
article

UAV-TISP: A simulation platform for collaborative task training and algorithm development in multi-rotor UAV clusters

Zhibin Xie, Kai Xi, Hongying Zhang, Xiaolong Wang, Qiang Guo, Zekun Wang, Xiuqin Cheng
article en
1 citations

Abstract

To fulfill the training requirements for the daily operations of multirotor Unmanned Aerial Vehicles (UAV) clusters, a UAV cluster collaborative task integrated simulation platform (UAV-TISP), was developed. The platform integrates a suite of hardware and software to simulate a range of collaborative UAV cluster operation scenarios. It features modules for collaborative task planning, UAV cluster simulations, and tactical monitoring. The platform significantly reduces training costs by eliminating physical drone dependencies while offering a flexible environment for testing swarm algorithms. UAV-TISP supports both individual UAV and swarm operations, incorporating high-fidelity flight dynamics, real-time communication via user datagram protocol (UDP), and collision avoidance strategies. Utilizing the OSGEarth engine, it enables dynamic 3D environment visualization and scenario customization. Three key task scenarios—route flight, formation reconstruction, and formation transformation—were tested to validate the platform’s efficacy. Results demonstrated robust formation maintenance, adaptive collision avoidance, and seamless task execution. Comparative analysis with Gazebo Sim revealed lower trajectory deviations in UAV-TISP, highlighting its superior accuracy in simulating real-world flight dynamics. Future work will focus on enhancing scalability for diverse UAV models, optimizing swarm networking under communication constraints, and expanding mission scenarios. UAV-TISP serves as a versatile tool for both operational training and advanced algorithm development in UAV cluster applications.

Journal of Electronic Science and TechnologyVol. 23(4)
Openalex Percentile: Top 15%
Distributed Control Multi-Agent Systems
1.49
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