Abstract
Although drone technology has progressed significantly, replicating the dynamic control and wind-sensing abilities of biological flights is still beyond our reach. Biological studies have revealed that insect wings are equipped with mechanoreceptors known as campaniform sensilla, which detect complex aerodynamic loads critical for flight agility. By leveraging robotic experiments designed to mimic these biological systems, we confirmed that wing strain provides crucial information about the drone's attitude, as well as the direction and velocity of the wind. We introduce a novel wing strain-based flight controller, termed 'fly-by-feel'. This methodology employs the aerodynamic forces exerted on a flapping drone's wings to deduce vital flight data, such as attitude and airflow without accelerometers and gyroscopic sensors. Our empirical approach spanned five key experiments: initially validating the wing strain sensor system for state information provision, followed by a single degree of freedom (1 DOF) control in changing winds, a two degrees of freedom (2 DOF) control for gravitational attitude adjustment, a test for position control in windy conditions, and finally, demonstrating precise flight path manipulation in a windless condition using only wing strain sensors. We have successfully demonstrated control of a flapping drone in a various environment using only wing strain sensors, with the aid of reinforcement learning-driven flight controller. The fly-by-feel system holds the potential to revolutionize autonomous drone operations, providing enhanced adaptability to environmental shifts. This will be beneficial across varied applications, from gust resistance to wind-assisted flight, paving the way toward the next generation of resilient and autonomous flying robots.