Learning Aerodynamics for the Control of Flying Humanoid Robots
This work addresses the challenge of precise aerodynamic control for flying humanoid robots, which is incremental in combining existing methods with new hardware and data.
The paper tackled the problem of modeling and controlling aerodynamic forces for flying humanoid robots by developing iRonCub-Mk1, a jet-powered robot, and using CFD simulations and learning techniques to create aerodynamic-aware controllers, validated through simulations and physical experiments.
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.