Measurement Uncertainty Assessment of Torque Sensor in Robot Joints

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Authors: Binghui Ma, Xiangjun He, Zhonggang Zhu, Faxiang Xie, Yao Xiao, Yue Min, Wei Yong Shen
Cited by: 0
References: 10

Abstract

Torque sensors are critical perception components in robot joints, providing essential feedback for precise motion control and operational safety. To ensure their reliability, a comprehensive metrological evaluation, including uncertainty assessment, is imperative. This paper establishes a measurement and analysis methodology based on the deadweight torque standard machine, following the national verification regulation JJG 995-2005. The indication error of a torque sensor is measured, and the primary sources of measurement uncertainty—including repeatability, the standard machine's inherent uncertainty, and the sensor indicator's resolution—are quantitatively analyzed. The combined standard uncertainty and expanded uncertainty (with a coverage factor k=2) are calculated. For a 10 Newton-meter (Nm) measurement point, the relative expanded uncertainty is determined to be 0.08%. The results confirm the sensor's compliance with specifications and provide a validated framework for the uncertainty assessment of torque sensors in robotic applications, thereby supporting the pursuit of higher-precision robot control. This study not only demonstrates the practical application of uncertainty evaluation in torque sensors for robotics but also offers a methodological reference for manufacturers and researchers to enhance the accuracy and reliability of torque sensing systems in advanced robotic applications. The findings contribute to the development of more precise and safer robotic systems, particularly in fields such as medical robotics and high-precision assembly.

Fields of Study & Subjects

Scientific Measurement and Uncertainty Evaluation Advanced Measurement and Metrology Techniques Sensor Technology and Measurement Systems

Publication details

Journal / source
Automation Control and Intelligent Systems
Volume
14
Issue
1
Pages
1–5
Publication date
9 January 2026
Language
EN