Dual Purpose WING

Research on "dual-purpose wings" spans diverse applications, focusing on optimizing wing design for enhanced performance in multiple contexts, from aerial robotics to aircraft aerodynamics. Current efforts leverage machine learning, particularly deep reinforcement learning and neural networks, alongside advanced modeling techniques like Riemannian geometry and spline-based manifold constraints, to improve efficiency, stability, and control in various flight scenarios. This research significantly impacts fields like autonomous navigation, micro-aerial vehicle design, and aerodynamic modeling, offering improvements in energy efficiency, robustness, and control precision for both fixed-wing and flapping-wing systems.