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Compensation of Electrical Current Drift in Human-Robot Collision

Published

Author(s)

Vinh Nguyen, Jennifer Case

Abstract

Human-robot collaborative systems are being increasingly adopted in manufacturing environments due to their application flexibility, adaptability, and cost-effectiveness. These systems typically use electrical current sensors to measure joint torque in industrial robot arms and limit the robot's impact in the event of a collision, particularly with a human operator. However, these electrical current sensors are known to experience sensor drift, which results in measurement inaccuracy that can result in improper joint-torque or end-effector force readings. The influence of this drift on the severity of human-robot collision is not well understood. This paper examines the influence of electrical current drift in the event of a human-robot collision by using a biofidelic test device with an embedded soft force sensor that mimics the deformation response of the human forearm. To study the influence of electrical current sensor drift in human-robot collision, an experimental setup was developed where a robot joint collided with the biofidelic test device, and the soft sensor was used to measure the deformation and force under the influence of electrical current drift. In addition, this setup was used to train a neural network-based controller, which was shown to compensate for electrical current drift and reduce the impact of the robot collision with the biofidelic material. Thus, this research provides a methodology to quantify the behavior of electrical current sensor drift on human-robot collision in addition to providing a data-driven methodology for compensation without requiring human subjects.
Citation
International Journal of Advanced Manufacturing Technology
Volume
123

Keywords

Human-Robot Interaction, Biofidelic Sensor, Collaborative Robot

Citation

Nguyen, V. and Case, J. (2022), Compensation of Electrical Current Drift in Human-Robot Collision, International Journal of Advanced Manufacturing Technology, [online], https://doi.org/10.1007/s00170-022-10369-y, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934518 (Accessed April 15, 2024)
Created November 5, 2022, Updated August 2, 2023