5 research outputs found
Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor
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) neural network 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.Comment: The first two authors contributed equall
A Whisker-inspired Fin Sensor for Multi-directional Airflow Sensing
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.Air Force Office of Scientific Research (Award MURI FA9550-19-1-0386)National Science Foundation (NSF) (Award BCS-1921251