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 threedimensional 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 modelbased approach considers the MAV and airflow sensor dynamics and its interaction with the wind, while the deep learningbased 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.

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 lowcost 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.

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Developing small‐scale, lightweight, and flexible devices with integrated microactuators is one of the critical challenges in wearable haptic devices, soft robotics, and microrobotics. In this study, a novel fabrication process that leverages the benefits of 3D printing with two‐photon polymerization and flexible printed circuit boards (FPCBs) is presented. This method enables flexible microsystems with 3D‐printed electrostatic microactuators, which are demonstrated in a flexible integrated micromirror array and a legged microrobot with a mass of 4 mg. 3D electrostatic actuators on FPCBs are robust enough to actuate the micromirrors while the device is deformed, and they are easily integrated with off‐the‐shelf electronics. The crawling robot is one of the lightest legged microrobots actuated without external fields, and the legs actuated with 3D electrostatic actuators enable a locomotion speed of 0.27 body length per second. The proposed fabrication framework opens up a pathway toward a variety of highly integrated flexible microsystems.