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: Contribution to conference › Paper › peer-review

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
Pages	323-335
Number of pages	13
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

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

Collaborative Robotics; Prognostics and Health Management (PHM); Auto-encoder; LSTM; Machine Learning; Manufacturing Assembly.

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. 323-335. Paper presented at 26th International Conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2023, Florianópolis, Brazil. https://doi.org/10.1007/978-3-031-47272-5_27

@conference{f62320933db2482bbeb6f50961045429,

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.",

keywords = "Collaborative Robotics; Prognostics and Health Management (PHM); Auto-encoder; LSTM; Machine Learning; Manufacturing Assembly.",

author = "Naimul Hasan and Louie Webb and Chinanthai, {Malarvizhi Kaniappan} and Hossain, {Mohammad Al Amin} and Ozkat, {Erkan Caner} and Tokhi, {Mohammad Osman} and Bugra Alkan",

note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 26th International Conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2023 ; Conference date: 02-10-2023 Through 04-10-2023",

year = "2024",

month = jan,

day = "4",

doi = "10.1007/978-3-031-47272-5_27",

language = "English",

pages = "323--335",

}

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', Paper presented at 26th International Conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2023, Florianópolis, Brazil, 2/10/23 - 4/10/23 pp. 323-335. 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.
2024. 323-335 Paper presented at 26th International Conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2023, Florianópolis, Brazil.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Positional Health Assessment of Collaborative Robots Based on Long Short-Term Memory Auto-Encoder (LSTMAE) Network

AU - Hasan, Naimul

AU - Webb, Louie

AU - Chinanthai, Malarvizhi Kaniappan

AU - Hossain, Mohammad Al Amin

AU - Ozkat, Erkan Caner

AU - Tokhi, Mohammad Osman

AU - Alkan, Bugra

PY - 2024/1/4

Y1 - 2024/1/4

N2 - 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.

AB - 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.

KW - Collaborative Robotics; Prognostics and Health Management (PHM); Auto-encoder; LSTM; Machine Learning; Manufacturing Assembly.

U2 - 10.1007/978-3-031-47272-5_27

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

M3 - Paper

SP - 323

EP - 335

T2 - 26th International Conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2023

Y2 - 2 October 2023 through 4 October 2023

ER -

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. 2024. Paper presented at 26th International Conference series on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2023, Florianópolis, Brazil. 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

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this