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arXiv · 2512.14450

Nonlinear System Identification Nano-drone Benchmark

Riccardo Busetto·Elia Cereda·Marco Forgione·Gabriele Maroni·Dario Piga·Daniele Palossi
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Abstract

We introduce a benchmark for system identification based on 75k real-world samples from the Crazyflie 2.1 Brushless nano-quadrotor, a sub-50g aerial vehicle widely adopted in robotics research. The platform presents a challenging testbed due to its multi-input, multi-output nature, open-loop instability, and nonlinear dynamics under agile maneuvers. The dataset comprises four aggressive trajectories with synchronized 4-dimensional motor inputs and 13-dimensional output measurements. To enable fair comparison of identification methods, the benchmark includes a suite of multi-horizon prediction metrics for evaluating both one-step and multi-step error propagation. In addition to the data, we provide a detailed description of the platform and experimental setup, as well as baseline models highlighting the challenge of accurate prediction under real-world noise and actuation nonlinearities. All data, scripts, and reference implementations are released as open-source at https://github.com/idsia-robotics/nanodrone-sysid-benchmark to facilitate transparent comparison of algorithms and support research on agile, miniaturized aerial robotics.

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Riccardo Busetto, Elia Cereda, Marco Forgione, Gabriele Maroni, Dario Piga, Daniele Palossi. 2025-12-16. Nonlinear System Identification Nano-drone Benchmark. https://doi.org/10.1016/j.conengprac.2026.106871

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