Temperature Compensation Method for a Six-Axis Force/Torque Sensor Using a Gated Recurrent Unit

This study aims to enhance the accuracy of a six-axis force/torque (F/T) sensor by improving upon existing approaches that use a multilayer perceptron (MLP) and the least-squares method (LSM). While previous research has used MLPs for thermal compensation, it has not effectively addressed temperature-induced drift. The sensor used in this study operates based on infrared light and incorporates a photocoupler, which makes it highly sensitive to dark current effects, resulting in significant drift under temperature variations. Moreover, its compact and lightweight design (45 g) leads to low thermal capacity, making it susceptible to rapid temperature fluctuations even with minimal heat input, which in turn affects real-time performance. To address these challenges, this study proposes a gated recurrent unit (GRU)-based method and compares it with the conventional MLP approach. Experimental results demonstrate that the GRU-based model significantly reduces drift from 26 to 2.7 N, and the root mean square error (RMSE) from 500 to 3—representing a 100fold improvement. These findings suggest that GRU-based modeling substantially improves real-time F/T measurements in temperature-sensitive environments, benefiting applications in robotics and precision instrumentation.