Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network

Naimul Hasan; Louie Webb; Malarvizhi Kaniappan Chinanthai; Mohammad Al Amin Hossain; Erkan Caner Ozkat; Mohammad Osman Tokhi; Bugra Alkan

doi:10.1007/978-3-031-47272-5_27

Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network

Naimul Hasan, Louie Webb, Malarvizhi Kaniappan Chinanthai, Mohammad Al Amin Hossain, Erkan Caner Ozkat, Mohammad Osman Tokhi, Bugra Alkan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Abstract

Calibration is a vital part of ensuring the safety and smooth operation of any industrial robot and this is particularly essential for collaborative robots as any issue pertaining to safety can adversely impact the human operator. Towards this aim, Prognostics and Health Management (PHM) has been widely implemented in the context of collaborative robots to ensure safe and efficient working environments. In this research, as a subset of PHM research, a novel positional health assessment approach based on a Long Short-Term Memory auto-encoder network (LSTMAE) is proposed. An experimental test setup is utilised, wherein the collaborative robot is subject to variations of coordinate system positional error. The operational 3-axis position time-series data of the collaborative robot is collected with the aid of an industrial data acquisition platform utilising influxDB. The experiments show that, with the aid of this approach, manufacturers can assess the positional health of their collaborative robot systems.

Original language	English
Title of host publication	Synergetic Cooperation Between Robots and Humans - Proceedings of the CLAWAR 2023 Conference—Volume 2
Editors	Ebrahim Samer El Youssef, Mohammad Osman Tokhi, Manuel F. Silva, Leonardo Mejia Rincon
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	323-335
Number of pages	13
ISBN (Print)	9783031472718
DOIs	https://doi.org/10.1007/978-3-031-47272-5_27
Publication status	Published - 4 Jan 2024
Event	26th International Conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2023 - Florianópolis, Brazil Duration: 2 Oct 2023 → 4 Oct 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	811
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	26th International Conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2023
Country/Territory	Brazil
City	Florianópolis
Period	2/10/23 → 4/10/23

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Auto-encoder
Collaborative robotics
LSTM
Machine learning
Manufacturing assembly
Prognostics and Health Management (PHM)
Wavelength scattering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-47272-5_27

CLAWAR 2023 paper 17 (Accepted Version)

Cite this

Hasan, N., Webb, L., Chinanthai, M. K., Hossain, M. A. A., Ozkat, E. C., Tokhi, M. O., & Alkan, B. (2024). Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network. In E. S. E. Youssef, M. O. Tokhi, M. F. Silva, & L. M. Rincon (Eds.), Synergetic Cooperation Between Robots and Humans - Proceedings of the CLAWAR 2023 Conference—Volume 2 (pp. 323-335). (Lecture Notes in Networks and Systems; Vol. 811). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-47272-5_27

Hasan, Naimul ; Webb, Louie ; Chinanthai, Malarvizhi Kaniappan et al. / Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network. Synergetic Cooperation Between Robots and Humans - Proceedings of the CLAWAR 2023 Conference—Volume 2. editor / Ebrahim Samer El Youssef ; Mohammad Osman Tokhi ; Manuel F. Silva ; Leonardo Mejia Rincon. Springer Science and Business Media Deutschland GmbH, 2024. pp. 323-335 (Lecture Notes in Networks and Systems).

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title = "Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network",

abstract = "Calibration is a vital part of ensuring the safety and smooth operation of any industrial robot and this is particularly essential for collaborative robots as any issue pertaining to safety can adversely impact the human operator. Towards this aim, Prognostics and Health Management (PHM) has been widely implemented in the context of collaborative robots to ensure safe and efficient working environments. In this research, as a subset of PHM research, a novel positional health assessment approach based on a Long Short-Term Memory auto-encoder network (LSTMAE) is proposed. An experimental test setup is utilised, wherein the collaborative robot is subject to variations of coordinate system positional error. The operational 3-axis position time-series data of the collaborative robot is collected with the aid of an industrial data acquisition platform utilising influxDB. The experiments show that, with the aid of this approach, manufacturers can assess the positional health of their collaborative robot systems.",

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Hasan, N, Webb, L, Chinanthai, MK, Hossain, MAA, Ozkat, EC, Tokhi, MO & Alkan, B 2024, Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network. in ESE Youssef, MO Tokhi, MF Silva & LM Rincon (eds), Synergetic Cooperation Between Robots and Humans - Proceedings of the CLAWAR 2023 Conference—Volume 2. Lecture Notes in Networks and Systems, vol. 811, Springer Science and Business Media Deutschland GmbH, pp. 323-335, 26th International Conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2023, Florianópolis, Brazil, 2/10/23. https://doi.org/10.1007/978-3-031-47272-5_27

Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network. / Hasan, Naimul; Webb, Louie; Chinanthai, Malarvizhi Kaniappan et al.
Synergetic Cooperation Between Robots and Humans - Proceedings of the CLAWAR 2023 Conference—Volume 2. ed. / Ebrahim Samer El Youssef; Mohammad Osman Tokhi; Manuel F. Silva; Leonardo Mejia Rincon. Springer Science and Business Media Deutschland GmbH, 2024. p. 323-335 (Lecture Notes in Networks and Systems; Vol. 811).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Hasan N, Webb L, Chinanthai MK, Hossain MAA, Ozkat EC, Tokhi MO et al. Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network. In Youssef ESE, Tokhi MO, Silva MF, Rincon LM, editors, Synergetic Cooperation Between Robots and Humans - Proceedings of the CLAWAR 2023 Conference—Volume 2. Springer Science and Business Media Deutschland GmbH. 2024. p. 323-335. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-47272-5_27