Onboard Ranging-Based Relative Localization and Stability for Lightweight Aerial Swarms
Shushuai Li, Feng Shan, Jiangpeng Liu, Mario Coppola, Christophe De Wagter, Guido de Croon
AI summary
Problem
Lightweight aerial swarms lack efficient, infrastructure-free relative localization due to extreme computational and memory constraints, existing ranging protocols that fail at scale, and theoretical unobservability in estimation filters.
Approach
Fuses ultra-wideband distance measurements with shared neighbor state data using a lightweight Extended Kalman Filter and a novel broadcast-based ranging protocol to enable fully onboard, infrastructure-free relative localization.
Key results
- First fully autonomous relative localization implemented on 13 lightweight (33g) drones with 168 MHz MCUs
- Scalable broadcast ranging protocol enabling 16 Hz communication for large swarms
- Sub-0.2 meter position error achieved at 16 Hz update rate
- Theoretical proof that unobservable states naturally self-correct via control-induced drift
Why it matters
Enables reliable, infrastructure-free swarm coordination for tiny, resource-limited robots in GPS-denied or confined environments, advancing practical applications in rescue, mapping, and exploration.
Abstract
Lightweight aerial swarms have potential applica- tions in scenarios where larger drones fail to operate efficiently. The primary foundation for lightweight aerial swarms is efficient rela- tive localization, 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 infras- tructure 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.