Adaptive Collaborative Formation Control of Unmanned Vehicles in Open Environments

This paper proposes a formation control algorithm to enhance the scalability and stability of the group of unmanned vehicles, while accommodating the dynamic arrival and departure of vehicles. Each agent maintains an auxiliary variable for all neighboring individuals to store historical information, effectively eliminating the legacy information after any departure. Additionally, we improve traditional velocity obstacle method to ensure safe operation during group movements by incorporating a safety radius to define a safety action zone, adding road limits during velocity selection, and setting a perception radius to reduce computational load. Numerical simulations are performed to verify the effectiveness of the algorithm.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1109/icus61736.2024.10839776
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
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article

Adaptive Collaborative Formation Control of Unmanned Vehicles in Open Environments

Zihang Xu, Yuezu Lv, Yuangui Bao
Distributed Control Multi-Agent Systems
article

Adaptive Collaborative Formation Control of Unmanned Vehicles in Open Environments

Zihang Xu, Yuezu Lv, Yuangui Bao
article en

Abstract

This paper proposes a formation control algorithm to enhance the scalability and stability of the group of unmanned vehicles, while accommodating the dynamic arrival and departure of vehicles. Each agent maintains an auxiliary variable for all neighboring individuals to store historical information, effectively eliminating the legacy information after any departure. Additionally, we improve traditional velocity obstacle method to ensure safe operation during group movements by incorporating a safety radius to define a safety action zone, adding road limits during velocity selection, and setting a perception radius to reduce computational load. Numerical simulations are performed to verify the effectiveness of the algorithm.

Beijing Institute of Technology (CN)
National Outstanding Youth Science Fund Project of National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 69%
Distributed Control Multi-Agent Systems
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