Design of an Inductive Torque Sensor for Robotic Applications
Collaborative robots enhance work efficiency through human-interaction functionalities, such as collision detection and direct teaching. These functionalities are often achieved by equipping each rotary joint of the robot with torque sensors, which improves workspace safety and convenience. However, commercial strain gauge-based torque sensors are often unsuitable for collaborative robots due to their high cost and incompatible form factors. Moreover, various forces applied to the robot’s joints induce crosstalk errors in the torque sensors, which degrades the overall performance. Previous studies have addressed crosstalk errors by employing complex processing methods or mechanisms that induce structural deformation in specific directions. Although these approaches effectively decrease crosstalk error, they often increase the size or cost of torque sensors, which limits their applicability to robots. This study proposes a novel torque sensor based on the inductive sensing technology, characterized by high-torque measurement performance and a simplified manufacturing process. The proposed torque sensor estimates the torque by measuring the inductance of a rectangular coil implemented on a printed circuit board (PCB). The coil is designed to exhibit minimal inductance change under crosstalk conditions to minimize crosstalk errors. The developed torque sensor has a diameter of 90 mm and a thickness of 13 mm. The performance of the proposed sensor was evaluated in calibration and torque measurement experiments. Comparative studies with a commercial force/torque sensor (FTS) and crosstalk error measurements demonstrated the feasibility of the proposed torque sensor for use in collaborative robots.
Authors
- J. S. Blogh MEng. (ORCID: https://orcid.org/0009-0002-3687-2698)
- Jae-Bok Song (ORCID: https://orcid.org/0000-0002-5818-1938)
- Seohyun Kim (ORCID: https://orcid.org/0009-0006-4371-9455)
Institutions
- Korea University (KR)
Publication Details
- Journal
- IEEE Sensors Journal
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1109/jsen.2025.3541215
- Citations
- 1
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 1.21