Wing-strain-based flight control of flapping-wing drones through reinforcement learning

Although drone technology has advanced rapidly, replicating the dynamic control and wind-sensing abilities of biological flight remains challenging. Insects use wing-mounted mechanoreceptors (campaniform sensilla) to detect complex aeroelastic loads essential for agile flight. Using robotic experiments that mimic these biological systems, we show that wing strain encodes critical information about a drone’s attitude, as well as wind direction and speed. We introduce a wing-strain-based flight controller that infers attitude and airflow from aerodynamic forces on flapping wings, eliminating the need for accelerometers and gyroscopes. Across five experiments—sensor validation, single-DOF control under changing winds, two-DOF attitude control via gravity-based adjustment, position control in windy conditions, and precise flight path control in calm air—we demonstrate that a flapping drone can be controlled using only wing strain sensors coupled with a reinforcement-learning-based flight controller. The system adapts to environmental changes, supporting applications from gust resistance to wind-assisted autonomous flight.