Attitude Estimation of a Biologically Inspired Robotic Housefly via Multimodal Sensor Fusion

In this paper, we address sensor fusion for the attitude estimation of Micromechanical Aerial Vehicles (MAVs), in particular a biologically inspired robotic housefly. First, a dynamic observer is proposed which estimates attitude based on kinematic data available from different and redundant bio-inspired sensors such as halteres, ocelli, gravitometers, magnetic compass and light polarization compass. In particular, following a geometric approach, the traditional structure of complementary filters, suitable for multiple sensors fusion, is specialized to the Lie group of rigid body rotations SO(3) and almost-global asymptotic stability is proved. Then, the filter performance is experimentally tested via a 3 degrees-of-freedom robotic flapper and a custom-made set of inertial/magnetic sensors. Experimental results show good agreement, upon proper tuning of the filter, between the actual kinematics of the robotic flapper and the kinematics reconstructed from the inertial/magnetic sensors via the proposed filter. keywords: Sensor Fusion, Dynamic Attitude Estimation, Biologically Inspired Robots.