Realtime On-Board Attitude Estimation of High-Frequency Flapping Wing MAVs Under Large Instantaneous Oscillation

Abstract

Unlike conventional aerial vehicles of fixed or rotary wings, realtime on-board attitude estimation of insect or hummingbird scale Flapping Wing Micro Aerial Vehicles (FWMAVs) is very challenging due to the severe instantaneous oscillations (approximately ten times of gravity on our platform) induced by high-frequency wing flapping. In this work, we present a novel sensor fusion algorithm for realtime on-board attitude estimation of FWMAVs. The algorithm is proposed with adaptive model-based compensation for both sensing drift and aerodynamic forces induced by flapping wings. We validated our approach on a 12.5 grams hummingbird robot. The experimental results demonstrated the accuracy, convergence, and robustness of the proposed algorithm.