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.

翻译：小型机器人飞艇作为一种轻于空气的飞行器，因其相比四旋翼飞行器具有更高的安全性、更长的续航能力和更安静的操作，正日益引起科学与工程界的关注。精确建模这些机器人飞艇的动力学面临重大挑战，这源于其大型升力体产生的复杂空气动力学特性。传统第一性原理模型难以获取精确的气动参数，且往往忽略高阶非线性，因此在建模小型机器人飞艇运动动力学方面已达到其极限。为解决这一挑战，本文提出了一种面向飞艇的自动调优神经常微分方程方法（Auto-tuning Blimp-oriented Neural Ordinary Differential Equation method, 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。通过进行机器人飞艇螺旋运动实验，将ABNODE与第一性原理及其他数据驱动基准模型进行比较，实验结果证明了所提方法的有效性。