The content discusses a novel sensor fusion method using Unscented Kalman Filtering to estimate joint torques in humanoid robots without torque sensors. The proposed approach integrates various measurements and non-directly measurable effects to improve control architecture. Extensive testing on the ergoCub robot demonstrates the effectiveness of the method, showcasing low root mean square errors in torque tracking even in the presence of external contacts. The paper compares the proposed strategy with the existing state-of-the-art approach based on the recursive Newton-Euler algorithm, highlighting improvements in estimation accuracy. The study also presents experiments validating the joint torque estimation method and controller architecture used on the ergoCub robot.
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arxiv.org
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