Flapping wing micro aerial vehicles face challenges in sensing and reacting to disturbances like wind gusts. This work introduces a new microscale bio-inspired digital strain sensor to detect these perturbations. The sensor is designed to change logic states when a specified strain threshold has been reached. The sensors are 3D printed on a flexible Mylar wing using two-photon polymerization. Three digital sensors with varying strain thresholds demonstrate differences in activation timing due to different design parameters. The sensors are tested at the 25 Hz flapping frequency of a hawkmoth, an insect with comparable wing size. A perturbation was added to the flapping wing by subjecting it to a 3 m/s wind gust. A single digital sensor is able to identify the wind disturbance by comparing the time of the first strain threshold crossing. A separate approach looks at the change in sensor ‘on’-time for each flap cycle and provides a clear indication of the wind disturbance.

Moths use hundreds of strain sensors (campaniform sensilla) on each wing to quickly respond to perturbations that may otherwise destabilize the moth during flight. A similar sensing approach could help stabilize micro-aerial vehicles (MAVs), but large sensor arrays are challenging due to the wiring and large latency that exists when capturing data from many traditional strain sensors. This work introduces a simplified bio-inspired strain sensor; the sensor interface and kinematics were inspired by campaniform sensilla that output a spike only in response to signals of interest. The engineered sensor outputs a discrete analog signal representing strain thresholds. A kinematic model of the sensor design is developed and describes the measured strain in terms of the sensor’s geometric parameters. This model is used to understand trade-offs between sensor resolution and range, and is validated using a finite element model (FEM) of the sensor. The sensor was designed with ease of fabrication in mind, using simple techniques and commercially available components. Fabricated sensors were tested in a four-point flexural test, and the data from the analytical and FEM model show good agreement with the experimental results. The sensors demonstrate resolutions of 83, 158, and 281 microstrain for the different designs tested. A sensor is placed on a model wing to illustrate future applications to MAVs as well as the sensor’s ability to sense both compressive and tensile strains.

This work presents the design, fabrication, and characterization of an airflow sensor inspired by the whiskers of animals. The body of the whisker was replaced with a fin structure in order to increase the air resistance. The fin was suspended by a micro-fabricated spring system at the bottom. A permanent magnet was attached beneath the spring, and the motion of fin was captured by a readily accessible and low- cost 3D magnetic sensor located below the magnet. The sensor system was modeled in terms of the dimension parameters of fin and the spring stiffness, which were optimized to improve the performance of the sensor. The system response was then characterized using a commercial wind tunnel and the results were used for sensor calibration. The sensor was integrated into a micro aerial vehicle (MAV) and demonstrated the capability of capturing the velocity of the MAV by sensing the relative airflow during flight.

Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for robustness and safety. In this paper, we use novel, bio-inspired airflow sensors to measure the airflow acting on a MAV, and we fuse this information in an Unscented Kalman filter (UKF) to simultaneously estimate the three-dimensional wind vector, the drag force, and other interaction forces (e.g. due to collisions, interaction with a human) acting on the robot. To this end, we present and compare a fully model-based and a deep learning-based strategy. The model-based approach considers the MAV and airflow sensor dynamics and its interaction with the wind, while the deep learning-based strategy uses a Long Short-Term Memory (LSTM) to obtain an estimate of the relative airflow, which is then fused in the proposed filter. We validate our methods in hardware experiments, showing that we can accurately estimate relative airflow of up to 4 m/s, and we can differentiate drag and interaction force.