用深度学习建模飞行人形机器人的气动特性，提升空中控制精度。
Learning Aerodynamics for the Control of Flying Humanoid Robots
- 结合物理模拟与神经网络，学习气动特性。
- 在风洞实验中验证了模型，实现稳定飞行控制。
- 适合机器人控制与气动建模研究者参考。
具备多模态移动能力的机器人因在复杂环境中的适应性而受到关注。本文从技术和科学两个角度解决飞行人形机器人在建模与控制方面的挑战，尤其聚焦气动力建模。技术贡献包括设计了集成喷气发动机的iRonCub-Mk1人形机器人原型机，并针对风洞实验进行了硬件改造，以实现对气动力和表面压力的精确测量。科学贡献提出了一套结合经典方法与学习技术的综合气动建模与控制框架。通过计算流体动力学（CFD）模拟获取气动力数据，并在iRonCub-Mk1上通过风洞实验进行验证。构建自动化CFD流程以扩展数据集，训练深度神经网络与线性回归模型。这些模型被集成至仿真器中，用于设计气动感知控制器，并在飞行模拟与实际原型机平衡实验中验证其有效性。
原文摘要 · Abstract (English)
Robots with multi-modal locomotion are an active research field due to their versatility in diverse environments. In this context, additional actuation can provide humanoid robots with aerial capabilities. Flying humanoid robots face challenges in modeling and control, particularly with aerodynamic forces. This paper addresses these challenges from a technological and scientific standpoint. The technological contribution includes the mechanical design of iRonCub-Mk1, a jet-powered humanoid robot, optimized for jet engine integration, and hardware modifications for wind tunnel experiments on humanoid robots for precise aerodynamic forces and surface pressure measurements. The scientific contribution offers a comprehensive approach to model and control aerodynamic forces using classical and learning techniques. Computational Fluid Dynamics (CFD) simulations calculate aerodynamic forces, validated through wind tunnel experiments on iRonCub-Mk1. An automated CFD framework expands the aerodynamic dataset, enabling the training of a Deep Neural Network and a linear regression model. These models are integrated into a simulator for designing aerodynamic-aware controllers, validated through flight simulations and balancing experiments on the iRonCub-Mk1 physical prototype.
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