Switching-Based Cooperative Avoidance Control for Multi-Agent Quadrotor Dynamic Systems in Dense Environments

This paper presents a control framework for multi-unmanned aerial vehicle systems that achieves safe and cooperative navigation in complex environments through a unified collision avoidance and trajectory guidance strategy. The principal innovation lies in the incorporation of velocity information into the design of a switching function, enabling more accurate assessment of collision risk and effectively reducing system conservativeness. Building upon this, an adaptive trajectory guidance mechanism is developed using collision avoidance information to ensure safe motion coordination among the vehicles. In addition, a closed-form solution for the dynamic system is derived, and its safety and stability are rigorously established through Lyapunov-based analysis. The effectiveness of the proposed framework is validated through simulation studies conducted on the MATLAB/Simulink platform (version R2020b), confirming reliable cooperative navigation in densely cluttered environments and guaranteeing dynamic safety.

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Publication Details

Journal
Applied Sciences
Published
2025-12-18
DOI
https://doi.org/10.3390/app152413283
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

Switching-Based Cooperative Avoidance Control for Multi-Agent Quadrotor Dynamic Systems in Dense Environments

Chunlei Zhao, Dušan M. Stipanović, Dongliang Yang, Wenxue Zhang
Applied Sciences
Robotic Path Planning Algorithms
article

Switching-Based Cooperative Avoidance Control for Multi-Agent Quadrotor Dynamic Systems in Dense Environments

Chunlei Zhao, Dušan M. Stipanović, Dongliang Yang, Wenxue Zhang
article en

Abstract

This paper presents a control framework for multi-unmanned aerial vehicle systems that achieves safe and cooperative navigation in complex environments through a unified collision avoidance and trajectory guidance strategy. The principal innovation lies in the incorporation of velocity information into the design of a switching function, enabling more accurate assessment of collision risk and effectively reducing system conservativeness. Building upon this, an adaptive trajectory guidance mechanism is developed using collision avoidance information to ensure safe motion coordination among the vehicles. In addition, a closed-form solution for the dynamic system is derived, and its safety and stability are rigorously established through Lyapunov-based analysis. The effectiveness of the proposed framework is validated through simulation studies conducted on the MATLAB/Simulink platform (version R2020b), confirming reliable cooperative navigation in densely cluttered environments and guaranteeing dynamic safety.

Applied SciencesVol. 15(24)
University of Illinois Urbana-Champaign (US), Hebei University of Technology (CN)
Openalex Percentile: Top 58%
Robotic Path Planning Algorithms
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