Onboard Ranging-Based Relative Localization and Stability for Lightweight Aerial Swarms

Lightweight aerial swarms have potential applications in scenarios where larger drones fail to operate efficiently. The primary foundation for lightweight aerial swarms is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficient relative localization</i>, which enables cooperation and collision avoidance. Computing the real-time position is challenging due to extreme resource constraints. This letter presents an autonomous relative localization technique for lightweight aerial swarms without infrastructure by fusing ultra-wideband wireless distance measurements and the shared state information (e.g., velocity, yaw rate, height) from neighbors. This is the first fully autonomous, tiny, fast, and accurate relative localization scheme implemented on a team of 13 lightweight (33 grams) and resource-constrained (168 MHz MCU with 192 KB memory) aerial vehicles. The proposed resource-constrained swarm ranging protocol is scalable, and a surprising theoretical result is discovered: the unobservability poses no issues because the state drift leads to control actions that make the state observable again. By experiment, less than 0.2 m position error is achieved at the frequency of 16 Hz for as many as 13 drones. The code is open-sourced, and the proposed technique is relevant not only for tiny drones but can be readily applied to many other resource-restricted robots.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-08-08
DOI
https://doi.org/10.1109/lra.2025.3597039
Citations
2
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
Field-Weighted Citation Impact
1.46
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article

Onboard Ranging-Based Relative Localization and Stability for Lightweight Aerial Swarms

Christophe De Wagter, Feng Shan, Mario Coppola, Shushuai Li et al.
2 citations
IEEE Robotics and Automation Letters
Indoor and Outdoor Localization Technologies
1.46
article

Onboard Ranging-Based Relative Localization and Stability for Lightweight Aerial Swarms

Christophe De Wagter, Feng Shan, Mario Coppola, Shushuai Li, Jiangpeng Liu, Guido de Croon
article en
2 citations

Abstract

Lightweight aerial swarms have potential applications in scenarios where larger drones fail to operate efficiently. The primary foundation for lightweight aerial swarms is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficient relative localization</i>, which enables cooperation and collision avoidance. Computing the real-time position is challenging due to extreme resource constraints. This letter presents an autonomous relative localization technique for lightweight aerial swarms without infrastructure by fusing ultra-wideband wireless distance measurements and the shared state information (e.g., velocity, yaw rate, height) from neighbors. This is the first fully autonomous, tiny, fast, and accurate relative localization scheme implemented on a team of 13 lightweight (33 grams) and resource-constrained (168 MHz MCU with 192 KB memory) aerial vehicles. The proposed resource-constrained swarm ranging protocol is scalable, and a surprising theoretical result is discovered: the unobservability poses no issues because the state drift leads to control actions that make the state observable again. By experiment, less than 0.2 m position error is achieved at the frequency of 16 Hz for as many as 13 drones. The code is open-sourced, and the proposed technique is relevant not only for tiny drones but can be readily applied to many other resource-restricted robots.

IEEE Robotics and Automation LettersVol. 10(10)
Nanjing University of Science and Technology (CN), Southeast University (CN), Delft University of Technology (NL)
Affordable and clean energy
Openalex Percentile: Top 16%
Indoor and Outdoor Localization Technologies
1.46
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Onboard Ranging-Based Relative Localization and Stability for Lightweight Aerial Swarms — Christophe De Wagter, Feng Shan, et al. · IEEE Robotics and Automation Letters (2025) | TGRS Research Map | TGRS