Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor - Robotics Institute Carnegie Mellon University

Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor

Andrea Tagliabue, Aleix Paris, Suhan Kim, Regan Kubicek, Sarah Bergbreiter, and Jonathan P. How
Conference Paper, Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1645 - 1652, October, 2020

Abstract

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.

BibTeX

@conference{Tagliabue-2020-128007,
author = {Andrea Tagliabue and Aleix Paris and Suhan Kim and Regan Kubicek and Sarah Bergbreiter and Jonathan P. How},
title = {Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor},
booktitle = {Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {2020},
month = {October},
pages = {1645 - 1652},
}