Identification of Intrinsic Friction and Torque Ripple for a Robotic Joint with Integrated Torque Sensors with Application to Wheel-Bearing Characterization

Turlapati, Harsha; Nguyen, Van Pho; Gurnani, Juhi; Ariffin, Mohammad Zaidi Bin; Kana, Sreekanth; Wong, Alvin Hong Yee; Han, Boon Siew; Campolo, Domenico

doi:10.3390/s24237465

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Identification of Intrinsic Friction and Torque Ripple for a Robotic Joint with Integrated Torque Sensors with Application to Wheel-Bearing Characterization

by

Harsha Turlapati Sri

^1,†

,

Van Pho Nguyen

^1,2,†

,

Juhi Gurnani

¹

,

Mohammad Zaidi Bin Ariffin

¹

,

Sreekanth Kana

¹

,

Alvin Hong Yee Wong

²,

Boon Siew Han

² and

Domenico Campolo

^1,*

¹

School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore

²

Schaeffler Hub for Advanced Research, Nanyang Technological University, Singapore 639798, Singapore

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Sensors 2024, 24(23), 7465; https://doi.org/10.3390/s24237465

Submission received: 14 October 2024 / Revised: 14 November 2024 / Accepted: 21 November 2024 / Published: 22 November 2024

(This article belongs to the Special Issue Advances in Sensing, Control and Path Planning for Robotic Systems)

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Abstract

Although integrated joint torque sensors in robots dispel the need for external force/torque sensors at the wrist to measure interactions, an inherent challenge is that they also measure the robot’s intrinsic dynamics. This is especially problematic for delicate robot manipulation tasks, where interaction forces may be comparable to the robot intrinsic dynamics. Therefore, the intrinsic dynamics must first be experimentally estimated under no-load conditions, when the measurement only consists of torques due to the transmission of the robot actuator, before external interactions may be measured. In this work, we propose an approach for identifying and predicting the intrinsic dynamics using linear regression with non-linear radial basis functions. Then, we validate this regression on a wheel-bearing turning task, in which its friction is a measure of quality, and thus must be accurately measured. The results showed that the bearing torque measured by the joint 7 torque sensor was within an RMS error of 11% of the torque measured by the external force/torque sensor. This error is much lower than that before our proposed model in compensating the intrinsic dynamics of the robot arm.

Keywords: intrinsic dynamics; linear regression; contact-rich; wheel-bearing inspection

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MDPI and ACS Style

Turlapati, H., Sri; Nguyen, V.P.; Gurnani, J.; Ariffin, M.Z.B.; Kana, S.; Wong, A.H.Y.; Han, B.S.; Campolo, D. Identification of Intrinsic Friction and Torque Ripple for a Robotic Joint with Integrated Torque Sensors with Application to Wheel-Bearing Characterization. Sensors 2024, 24, 7465. https://doi.org/10.3390/s24237465

AMA Style

Turlapati H Sri, Nguyen VP, Gurnani J, Ariffin MZB, Kana S, Wong AHY, Han BS, Campolo D. Identification of Intrinsic Friction and Torque Ripple for a Robotic Joint with Integrated Torque Sensors with Application to Wheel-Bearing Characterization. Sensors. 2024; 24(23):7465. https://doi.org/10.3390/s24237465

Chicago/Turabian Style

Turlapati, Harsha, Sri, Van Pho Nguyen, Juhi Gurnani, Mohammad Zaidi Bin Ariffin, Sreekanth Kana, Alvin Hong Yee Wong, Boon Siew Han, and Domenico Campolo. 2024. "Identification of Intrinsic Friction and Torque Ripple for a Robotic Joint with Integrated Torque Sensors with Application to Wheel-Bearing Characterization" Sensors 24, no. 23: 7465. https://doi.org/10.3390/s24237465

APA Style

Turlapati, H., Sri, Nguyen, V. P., Gurnani, J., Ariffin, M. Z. B., Kana, S., Wong, A. H. Y., Han, B. S., & Campolo, D. (2024). Identification of Intrinsic Friction and Torque Ripple for a Robotic Joint with Integrated Torque Sensors with Application to Wheel-Bearing Characterization. Sensors, 24(23), 7465. https://doi.org/10.3390/s24237465

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Identification of Intrinsic Friction and Torque Ripple for a Robotic Joint with Integrated Torque Sensors with Application to Wheel-Bearing Characterization

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