Although drone technology has advanced rapidly, replicating the dynamic control and wind-sensing abilities of biological flight is still beyond reach. Biological studies reveal 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 confirm that wing strain provides crucial information about the drone’s attitude angle, as well as the direction and velocity of the wind. We introduce a wing-strain-based flight controller that 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. The present work spans five key experiments: initial validation of the wing strain sensor system for state information provision, control in a single degree of freedom movement environment with changing winds, control in a two degrees of freedom movement environment for gravitational attitude adjustment, a test for position control in windy conditions and a demonstration of precise flight path manipulation in a windless condition using only wing strain sensors. We have successfully demonstrated control of a flapping drone in various environments using only wing strain sensors, with the aid of a reinforcement-learning-driven flight controller. The demonstrated adaptability to environmental shifts will be beneficial across varied applications, from gust resistance to wind-assisted flight for autonomous flying robots.

尽管无人机技术发展迅速，但复制生物飞行的动态控制和风感能力仍然遥不可及。生物学研究表明，昆虫翅膀配备有称为钟形感器的机械感受器，可检测对飞行敏捷性至关重要的复杂空气动力载荷。通过利用旨在模仿这些生物系统的机器人实验，我们确认机翼应变提供了有关无人机姿态角以及风向和风速的重要信息。我们介绍了一种基于机翼应变的飞行控制器，它利用施加在扑动无人机机翼上的空气动力来推断重要的飞行数据，例如姿态和气流，而无需加速度计和陀螺仪传感器。目前的工作涵盖五个关键实验：用于状态信息提供的机翼应变传感器系统的初步验证、风向变化的单自由度运动环境中的控制、用于重力姿态调整的二自由度运动环境中的控制、测试用于在有风条件下进行位置控制，并演示仅使用机翼应变传感器在无风条件下进行精确的飞行路径操纵。我们已经成功地演示了在强化学习驱动的飞行控制器的帮助下，仅使用机翼应变传感器即可在各种环境中控制扑动无人机。所展示的对环境变化的适应性将有益于各种应用，从阵风阻力到自主飞行机器人的风辅助飞行。