Learning Aerodynamics for the Control of Flying Humanoid Robots
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866915354466320384 |
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| author | Paolino, Antonello Nava, Gabriele Di Natale, Fabio Bergonti, Fabio Vanteddu, Punith Reddy Grassi, Donato Riccobene, Luca Zanotti, Alex Tognaccini, Renato Iaccarino, Gianluca Pucci, Daniele |
| author_facet | Paolino, Antonello Nava, Gabriele Di Natale, Fabio Bergonti, Fabio Vanteddu, Punith Reddy Grassi, Donato Riccobene, Luca Zanotti, Alex Tognaccini, Renato Iaccarino, Gianluca Pucci, Daniele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00305 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Aerodynamics for the Control of Flying Humanoid Robots Paolino, Antonello Nava, Gabriele Di Natale, Fabio Bergonti, Fabio Vanteddu, Punith Reddy Grassi, Donato Riccobene, Luca Zanotti, Alex Tognaccini, Renato Iaccarino, Gianluca Pucci, Daniele Robotics Machine Learning 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. |
| title | Learning Aerodynamics for the Control of Flying Humanoid Robots |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2506.00305 |