Data-Driven Dynamics Modeling of Miniature Robotic Blimps Using Neural ODEs With Parameter Auto-Tuning

TL;DR

This paper introduces ABNODE, a neural ODE-based data-driven model with auto-tuning, for accurately capturing the complex dynamics of miniature robotic blimps, outperforming traditional and benchmark models.

Contribution

The paper presents a novel ABNODE method that combines first-principle modeling with neural ODEs and auto-tuning for better dynamics modeling of robotic blimps.

Findings

ABNODE outperforms traditional models in spiraling motion experiments.

The method effectively captures high-order nonlinear aerodynamics.

Experimental results validate the accuracy of the proposed approach.

Abstract

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.