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) 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.

I. INTRODUCTION

The deployment of MAVs in uncertain and constantly changing atmospheric conditions [1]–[3] requires the ability to estimate and adapt to disturbances such as the aerodynamic drag force applied by wind gusts. Simultaneously, as many new interaction-based missions [4]–[6] arise, so increases the need to better differentiate between forces caused by aerodynamic disturbances and other sources of interaction [7]–[10]. Differentiating between aerodynamic drag force and interaction force can be extremely important for safety reasons. For example, the controller of a robot should react differently depending on whether a large disturbance is caused by a wind gust, or by a human trying to interact with the machine [11].