Butterfly and drones in confined flying conditions : using aerodynamics, biomimetism and AI to improve control and stability – BUCOLYC

Whilst the technology of Unmanned Aerial Vehicles (UAVs) is highly advanced for outdoor flight, the realm of confined flight poses a significant challenge due to the complex interplay of aerodynamic couplings and interferences between the UAV and surrounding walls. These disturbances can wreak havoc on the UAV's stabilisation controls and hinders also its manœuverability. Our project endeavors to overcome this challenge by combining aerodynamic approaches, biomimetism and machine learning to enhance the control and stability of UAVs in confined and near-wall environments.
Our research will focus on both rotor and flapping-wing drones (ornithopters), and we plan to augment the traditional control strategies, which primarily rely on closed-loop response from embedded kinematic data. To achieve this, we aim to better account for (i) the induced aerodynamic perturbations, (ii) the drone's proximity to the walls, and (iii) bio-inspired solutions derived from the study of butterfly flight in confined spaces and near walls.
To achieve this ambitious goal, our multi-disciplinary consortium gathers experts in robotics, biorobotics, fluid mechanics, and entomology, alongside an industrial partner (XTim) renowned for its leadership in the market of biomimetic drones with flapping wings.
Our project is structured around several scientific and technical tasks that aim to finely characterize the dynamics of confined flight and aerodynamic perturbations for both multi-rotor and flapping-wing UAVs, identify the free and confined flight characteristics of butterflies for biomimetic purposes, and develop innovative control protocols (based on reinforcement learning) to achieve stable UAV flight in confined or near-wall environments. These tasks rely on a combination of dedicated experimental facilities, field observation of butterfly flights, state-of-the-art aerodynamic metrology and simulations, and advanced machine learning algorithms, all developed within the consortium.
The culmination of our efforts will be the synthesis and validation of our findings, which we will implement on our industrial partners' commercial UAVs. Our developed solutions and control strategies have the potential to revolutionize the field of confined UAV flight and enable safe, reliable and stable operation in areas that were once considered inaccessible.