Reliable Maintenance Policy for Distributed Affine Formation Control of UAVs

Conference Paper (2026)
Author(s)

H. Zhou (Student TU Delft)

Z. Li (TU Delft - Electrical Engineering, Mathematics and Computer Science)

R. T. Rajan (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Research Group
Signal Processing Systems
DOI related publication
https://doi.org/10.1109/AERO66936.2026.11519911 Final published version
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Publication Year
2026
Language
English
Research Group
Signal Processing Systems
Publisher
IEEE
ISBN (print)
979-8-3315-7361-4
ISBN (electronic)
979-8-3315-7360-7
Event
2026 IEEE Aerospace Conference (2026-03-07 - 2026-03-14), Big Sky, United States
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Abstract

Distributed affine formation control (AFC) enables unmanned aerial vehicle (UAV) swarms to achieve coordinated motion while maintaining a desired geometric configuration, thereby enhancing their maneuverability. However, conventional leader-follower-based distributed AFC remains vulnerable to dynamic changes in the network topology, where UAVs may be temporarily unavailable due to maintenance in real-world applications. In this work, we propose a reliable maintenance policy that enables individual UAVs to detach from the swarm without compromising the formation stability. Our policy introduces an agent homogenization strategy that replaces the conventional leader UAVs with virtual leaders, thereby ensuring all operational UAVs are followers and thus eligible for maintenance. A relative affine localization (RAL) technique is employed, which allows the remaining UAVs to estimate the relative positions of missing neighbors by leveraging the formation geometry. Our proposed framework is validated through a series of experiments with a swarm of Crazyflie quadrotors in an indoor environment, which demonstrate the effectiveness of our policy that allows individual UAVs to be removed and returned in sequence while the rest of the swarm maintains its target configuration with high accuracy. Our proposed maintenance policy enables robust and long-duration deployments of UAV swarms in inaccessible and harsh environments.

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