基于参数自整定神经ODE的微型机器人飞艇数据驱动动力学建模
Data-Driven Dynamics Modeling of Miniature Robotic Blimps Using Neural ODEs With Parameter Auto-Tuning
AI总结：
针对微型机器人飞艇因复杂气动特性难以精确建模的问题，本文提出融合第一性原理与神经网络的ABNODE方法，并通过螺旋运动实验验证其优于传统及数据驱动基准模型。
摘要：
微型机器人飞艇作为一种轻于空气的飞行器，因其相较于四旋翼具有更高的安全性、更长的续航能力和更安静的运行特性，在科学与工程界引起了越来越多的关注。由于大型升力体带来的复杂空气动力学特性，精确建模这些机器人飞艇的动力学面临重大挑战。传统的第一性原理模型难以获得准确的空气动力学参数，并且常常忽略高阶非线性，因此在建模微型机器人飞艇的运动动力学方面已达到其极限。为应对这一挑战，本文提出了面向飞艇的自整定神经常微分方程方法（ABNODE），这是一种融合第一性原理与神经网络建模的数据驱动方法。开展了机器人飞艇的螺旋运动实验，将ABNODE与第一性原理模型及其他数据驱动基准模型进行比较，结果表明了所提方法的有效性。
英文摘要：
Miniature robotic blimps, as one type of lighter-than-air aerial vehicles, have attracted increasing attention in the science and engineering community for their enhanced safety, extended endurance, and quieter operation compared to quadrotors. Accurately modeling the dynamics of these robotic blimps poses a significant challenge due to the complex aerodynamics stemming from their large lifting bodies. Traditional first-principle models have difficulty obtaining accurate aerodynamic parameters and often overlook high-order nonlinearities, thus coming to its limit in modeling the motion dynamics of miniature robotic blimps. To tackle this challenge, this letter proposes the Auto-tuning Blimp-oriented Neural Ordinary Differential Equation method (ABNODE), a data-driven approach that integrates first-principle and neural network modeling. Spiraling motion experiments of robotic blimps are conducted, comparing the ABNODE with first-principle and other data-driven benchmark models, the results of which demonstrate the effectiveness of the proposed method.
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