为微型无人机系统辨识提供7.5万条真实飞行数据基准测试
Nonlinear System Identification Nano-drone Benchmark
- 基于7.5万条真实飞行数据，构建多输入多输出辨识基准
- 在4种高动态轨迹上实现13维输出的多步预测，误差可量化评估
- 开源数据与代码，适合微型飞行器控制与非线性建模研究者
我们提出一个基于Crazyflie 2.1无刷微型四旋翼的系统辨识基准，该平台重小于50克，广泛应用于机器人研究。由于其多输入多输出特性、开环不稳定性及敏捷机动下的非线性动力学，构成极具挑战性的测试平台。数据集包含4种高动态轨迹，同步采集4维电机输入和13维输出测量。为实现方法公平比较，基准提供多时域预测指标，评估单步与多步误差传播。此外，我们详细描述了平台与实验设置，并提供基线模型，凸显真实噪声与执行器非线性下的精确预测难题。所有数据、脚本与参考实现均开源，地址：https://github.com/idsia-robotics/nanodrone-sysid-benchmark，以支持微型敏捷飞行器的研究。
原文摘要 · Abstract (English)
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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