3-dimensional affine formation control for a heterogeneous UAVs-UGVs system

Purpose This work studies the air–ground coordinated formation control problem for a unmanned aerial vehicle (UAVs)-unmanned ground vehicle (UGVs) system. This study aims to drive the agents to generate adaptive geometric shapes in a complex environment. Design/methodology/approach The method of affine formation is adopted. The authors use 4 basic affine transformations in a three-dimensional space for the formation maneuver in different conditions. The system framework is constructed using a leader–follower strategy and network localization theory such that the formation can be controlled merely through driving a part of the agents over a directed graph. For the heterogeneous system under disturbance, the authors propose a control design integrated with disturbance estimation and compensation for the agents to track the desired configuration. Finally, a simulation example and a real-world experiment are shown to illustrate the effectiveness of the designed strategy. Findings With the proposed strategy, the formation could reconfigure and generate time-varying shapes to adapt to the complex environment. Using the proposed controller, the negative effects of external disturbances are handled such that the UAVs and UGVs could track the desired trajectories precisely. Originality/value Appropriate design of maneuver modes based on affine transformation in R3 is given on how to make the formation configuration adapt to the constrained environment. A system framework is constructed for the affine formation over a directed graph. Even though the formation parameters are unknown to the followers, they can track the desired trajectories through distributed estimation.

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Publication Details

Journal
Robotic Intelligence and Automation
Published
2025-05-23
DOI
https://doi.org/10.1108/ria-08-2024-0176
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

3-dimensional affine formation control for a heterogeneous UAVs-UGVs system

Mou Chen, Xia Zheng
Robotic Intelligence and Automation
Distributed Control Multi-Agent Systems
article

3-dimensional affine formation control for a heterogeneous UAVs-UGVs system

Mou Chen, Xia Zheng
article en

Abstract

Purpose This work studies the air–ground coordinated formation control problem for a unmanned aerial vehicle (UAVs)-unmanned ground vehicle (UGVs) system. This study aims to drive the agents to generate adaptive geometric shapes in a complex environment. Design/methodology/approach The method of affine formation is adopted. The authors use 4 basic affine transformations in a three-dimensional space for the formation maneuver in different conditions. The system framework is constructed using a leader–follower strategy and network localization theory such that the formation can be controlled merely through driving a part of the agents over a directed graph. For the heterogeneous system under disturbance, the authors propose a control design integrated with disturbance estimation and compensation for the agents to track the desired configuration. Finally, a simulation example and a real-world experiment are shown to illustrate the effectiveness of the designed strategy. Findings With the proposed strategy, the formation could reconfigure and generate time-varying shapes to adapt to the complex environment. Using the proposed controller, the negative effects of external disturbances are handled such that the UAVs and UGVs could track the desired trajectories precisely. Originality/value Appropriate design of maneuver modes based on affine transformation in R3 is given on how to make the formation configuration adapt to the constrained environment. A system framework is constructed for the affine formation over a directed graph. Even though the formation parameters are unknown to the followers, they can track the desired trajectories through distributed estimation.

Robotic Intelligence and AutomationVol. 45(4)
Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 88%
Distributed Control Multi-Agent Systems
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