Dynamic behavior recognition in aerial deployment of multi-segmented foldable-wing drones using variational autoencoders

The aerial deployment method enables Unmanned Aerial Vehicles (UAVs) to be directly positioned at the required altitude for their mission. This method typically employs folding technology to improve loading efficiency, with applications such as the gravity-only aerial deployment of high-aspect-ratio solar-powered UAVs, and aerial takeoff of fixed-wing drones in Mars research. However, the significant morphological changes during deployment are accompanied by strong nonlinear dynamic aerodynamic forces, which result in multiple degrees of freedom and an unstable character. This hinders the description and analysis of unknown dynamic behaviors, further leading to difficulties in the design of deployment strategies and flight control. To address this issue, this paper proposes an analysis method for dynamic behaviors during aerial deployment based on the Variational Autoencoder (VAE). Focusing on the gravity-only deployment problem of high-aspect-ratio foldable-wing UAVs, the method encodes the multi-degree-of-freedom unstable motion signals into a low-dimensional feature space through a data-driven approach. By clustering in the feature space, this paper identifies and studies several dynamic behaviors during aerial deployment. The research presented in this paper offers a new method and perspective for feature extraction and analysis of complex and difficult-to-describe extreme flight dynamics, guiding the research on aerial deployment drones’ design and control strategies.