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.

翻译：本文提出一种基于无迹卡尔曼滤波的创新型传感器融合方法，用于在无关节力矩传感器的仿人机器人中在线估计关节力矩。在特征层面，所提方法综合考虑多模态测量值（如电流、加速度等）及不可直接测量的效应（如外部接触），从而生成可直接用于人机交互控制架构的关节力矩。该传感器融合还能集成分布式非同轴力/力矩传感器，因此对机器人底层传感器配置具有灵活适配性。为验证该方法，我们展示了如何将其集成至面向任务空间力矩控制的双层级力矩控制架构中。通过在当前由意大利技术研究院开发的新型仿人机器人ergoCub上开展大量测试，验证了所提方法的性能。此外，我们将本策略与基于递归牛顿-欧拉算法的现有先进方法进行对比。结果表明，即使在存在外部接触的情况下，本方法在力矩跟踪中仍能达到0.05 Nm至2.5 Nm的低均方根误差。