Group Formation Tracking Control for Air–Ground Multi-Agent Systems

This paper presents a weight-based group formation tracking control method for air–ground multi-agent systems, where the system consists of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). Specifically, a three-layer directed communication topology consisting of a master leader, group leaders, and followers is designed first. Subsequently, a formation tracking controller is developed, and the stability of the system is proven using Lyapunov theory. Additionally, to address the collision issues that can arise during the grouping and flight of multi-UAV formations, a collision avoidance controller is designed based on a rotational potential field function. Finally, simulation experiments validate that the proposed method can achieve stable and safe grouping, formation, and collaborative tracking of air–ground multi-agent systems.

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Publication Details

Journal
Guidance Navigation and Control
Published
2025-02-12
DOI
https://doi.org/10.1142/s2737480725500153
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00
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Group Formation Tracking Control for Air–Ground Multi-Agent Systems

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Guidance Navigation and Control
Distributed Control Multi-Agent Systems
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Group Formation Tracking Control for Air–Ground Multi-Agent Systems

Ping Yuan, Chunyi Peng, Hailong Huang, Juntong Qi, Mingming Wang, Chong Wu, Haichao Liu
article en

Abstract

This paper presents a weight-based group formation tracking control method for air–ground multi-agent systems, where the system consists of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). Specifically, a three-layer directed communication topology consisting of a master leader, group leaders, and followers is designed first. Subsequently, a formation tracking controller is developed, and the stability of the system is proven using Lyapunov theory. Additionally, to address the collision issues that can arise during the grouping and flight of multi-UAV formations, a collision avoidance controller is designed based on a rotational potential field function. Finally, simulation experiments validate that the proposed method can achieve stable and safe grouping, formation, and collaborative tracking of air–ground multi-agent systems.

Guidance Navigation and ControlVol. 05(02)
Tianjin University of Technology (CN), Hong Kong Polytechnic University (HK), Hainan Agricultural School (CN), Artificial Intelligence in Medicine (Canada) (CA)
Openalex Percentile: Top 98%
Distributed Control Multi-Agent Systems
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