Title: Learning Aerodynamics for the Control of Flying Humanoid Robots Show Chinese title

Title: 学习飞行人形机器人的空气动力学

Abstract: 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. Show Chinese abstract

Abstract: 具有多模式运动能力的机器人是一个活跃的研究领域，因为它们在各种环境中表现出色。 在这种情况下，额外的动力装置可以为人形机器人提供空中能力。 飞行人形机器人在建模和控制方面面临挑战，尤其是在气动载荷方面。 本文从技术和科学的角度解决这些挑战。 技术贡献包括iRonCub-Mk1人形机器人的机械设计，这是一种喷气动力的人形机器人，优化了喷气发动机的集成，并对人形机器人进行了硬件修改，以便在风洞中进行精确的气动载荷和表面压力测量实验。 科学贡献提供了一种全面的方法，使用经典和学习技术来建模和控制气动载荷。 计算流体动力学（CFD）模拟计算气动载荷，并通过iRonCub-Mk1的风洞实验进行验证。 一个自动化的CFD框架扩展了气动数据集，使得可以训练深度神经网络和线性回归模型。 这些模型被集成到模拟器中，用于设计气动感知控制器，通过iRonCub-Mk1物理原型的飞行模拟和平衡实验进行验证。