Abstract-In this paper, we study sensor fusion for the attitude estimation of Micro Aerial Vehicles (MAVs), in particular mechanical flying insects.First, following a geometric approach, 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, the traditional structure of complementary filters, suitable for multiple sensor fusion, is specialized to the Lie group of rigid body rotations SO(3).Then, a numerical implementation of the filter is provided for the specific case of inertial/magnetic navigation, i.e. when gravitometers, magnetometer and gyroscopes are available.Finally, 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.
In this paper, we study sensor fusion for the attitude stabilization of Micro Aerial Vehicles (MAVs), in particular mechanical flying insects. Following a geometric approach, 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, the traditional structure of complementary filters, suitable for multiple sensor fusion, is specialized to the Lie group of rigid body rotations SO(3). The filter performance based on a 3-axis accelerometer and a 3-axis gyroscope is experimentally tested on a 2 degreesof-freedom support showing its effectiveness. Finally, attitude stabilization is proposed based on a feedback scheme with dynamic estimation of the state, namely the orientation and the angular velocity. Almost-global stability of the proposed controller in the case of dynamic state estimation is demonstrated via the separation principle, and realistic numerical simulations with noisy sensors and external disturbances are provided to validate the proposed control scheme.keywords: attitude control on SO(3), separation principle, geometric control, complementary filtering, biologically inspired robots.
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 that estimates attitude based on kinematic data available from different and redundant bio-inspired sensors such as halteres, ocelli, gravitometers, magnetic compasses and light polarization compasses. In particular, following a geometric approach, the traditional structure of complementary filters, suitable for multiple sensor 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-d.o.f. 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
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