UKF-Based Sensor Fusion for Joint-Torque Sensorless Humanoid Robots
CoRR(2024)
摘要
This paper proposes a novel sensor fusion based on Unscented Kalman Filtering
for the online estimation of joint-torques of humanoid robots without
joint-torque sensors. At the feature level, the proposed approach considers
multimodal measurements (e.g. currents, accelerations, etc.) and non-directly
measurable effects, such as external contacts, thus leading to joint torques
readily usable in control architectures for human-robot interaction. The
proposed sensor fusion can also integrate distributed, non-collocated
force/torque sensors, thus being a flexible framework with respect to the
underlying robot sensor suit. To validate the approach, we show how the
proposed sensor fusion can be integrated into a twolevel torque control
architecture aiming at task-space torquecontrol. The performances of the
proposed approach are shown through extensive tests on the new humanoid robot
ergoCub, currently being developed at Istituto Italiano di Tecnologia. We also
compare our strategy with the existing state-of-theart approach based on the
recursive Newton-Euler algorithm. Results demonstrate that our method achieves
low root mean square errors in torque tracking, ranging from 0.05 Nm to 2.5 Nm,
even in the presence of external contacts.
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