Model Predictive Control of the Degree of Automation Optimizing Robot Health

Christian Alexander Braun; Aniketh Ramesh; Simon Rothfuss; Manolis Chiou; Rustam Stolkin; Soren  Hohmann

doi:10.1109/SACI58269.2023.10158596

Model Predictive Control of the Degree of Automation Optimizing Robot Health

Christian Alexander Braun, Aniketh Ramesh, Simon Rothfuss, Manolis Chiou, Rustam Stolkin, Soren Hohmann

Metallurgy and Materials

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

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Abstract

Adjusting operator support in human-machine systems is a promising way of combining operator involvement with high overall system performance. Adaptive automation aims to achieve this goal without burdening the operator with the task of selecting and setting the desired amount of support. In this work, two novel adaptive automations are presented. We use the performance measure of 'robot health' to formulate the optimal control problem of maximizing the robot health of a human-robot system through the adaption of operator support to develop two model predictive controllers. The first one considers discrete levels of operator support, or levels of automation, the second one uses the continuous conception of the degree of automation. We report on a proof-of-concept simulation study evaluating the proposed model predictive controllers in a collaborative teleoperation of a mobile robot; the results demonstrate the ability of both model predictive controllers to successfully arbitrate control between the operator and the robot's controller to maximize robot health.

Original language	English
Title of host publication	2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)
Publisher	IEEE
Pages	000381-000386
Number of pages	6
ISBN (Electronic)	9798350321104
ISBN (Print)	9798350321111 (PoD)
DOIs	https://doi.org/10.1109/SACI58269.2023.10158596
Publication status	Published - 27 Jun 2023
Event	17th International Symposium on Applied Computational Intelligence and Informatics - Faculty of Automation and Computers, Politehnica University of Timisoara, Timisoara, Romania Duration: 23 May 2023 → 26 May 2023

Publication series

Name	International Symposium on Applied Computational Intelligence and Informatics
Publisher	IEEE
ISSN (Print)	2833-9010
ISSN (Electronic)	2765-818X

Conference

Conference	17th International Symposium on Applied Computational Intelligence and Informatics
Abbreviated title	SACI 2023
Country/Territory	Romania
City	Timisoara
Period	23/05/23 → 26/05/23

Bibliographical note

Funding Information:
This work was supported by NCNR under Grants EP/R02572X/1, EP/P01366X/1, and EP/P017487/1, and by ReLiB under Grant FIRG005.

Publisher Copyright:
© 2023 IEEE.

Keywords

Adaptive automation
Variable autonomy
Shared control
Cooperation
Degree of automation
Levels of autonomy
Levels of automation
Mixed initiative control

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Information Systems
Information Systems and Management
Control and Optimization
Health Informatics
Education

UN SDGs

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

Access to Document

10.1109/SACI58269.2023.10158596

BraunC2023Model
C. A. Braun, A. Ramesh, S. Rothfuß, M. Chiou, R. Stolkin and S. Hohmann, "Model Predictive Control of the Degree of Automation Optimizing Robot Health," 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania, 2023, pp. 000381-000386, doi: 10.1109/SACI58269.2023.10158596. © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 817 KBLicence: Other (please specify with Rights Statement)

ReLIB - Faraday Challenge Fast Track proposal - Circular economy
Elliott, R., Lee, R., Allan, P., Slater, P., Stolkin, R., Walton, A., Overton, T., Reed, D., Anderson, P., Windridge, D. & Gough, R.
Engineering & Physical Science Research Council
1/03/18 → 30/06/21
Project: Research Councils
National Centre for Nuclear Robotics (NCNR)
Stolkin, R., Leonardis, A. & Stone, B.
Engineering & Physical Science Research Council
2/10/17 → 1/04/22
Project: Research
Robust remote sensing for multi-modal characterisation in nuclear and other extreme environments
Leonardis, A. & Stolkin, R.
Engineering & Physical Science Research Council
1/05/17 → 1/11/21
Project: Research
Robotics for Nuclear Environments - Lead Manchester University (EP/P01366X/1)
Leonardis, A. & Stolkin, R.
Engineering & Physical Science Research Council
1/04/17 → 31/03/23
Project: Research

Cite this

Braun, C. A., Ramesh, A., Rothfuss, S., Chiou, M., Stolkin, R., & Hohmann, S. (2023). Model Predictive Control of the Degree of Automation Optimizing Robot Health. In 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI) (pp. 000381-000386). Article 10158596 ( International Symposium on Applied Computational Intelligence and Informatics). IEEE. https://doi.org/10.1109/SACI58269.2023.10158596

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abstract = "Adjusting operator support in human-machine systems is a promising way of combining operator involvement with high overall system performance. Adaptive automation aims to achieve this goal without burdening the operator with the task of selecting and setting the desired amount of support. In this work, two novel adaptive automations are presented. We use the performance measure of 'robot health' to formulate the optimal control problem of maximizing the robot health of a human-robot system through the adaption of operator support to develop two model predictive controllers. The first one considers discrete levels of operator support, or levels of automation, the second one uses the continuous conception of the degree of automation. We report on a proof-of-concept simulation study evaluating the proposed model predictive controllers in a collaborative teleoperation of a mobile robot; the results demonstrate the ability of both model predictive controllers to successfully arbitrate control between the operator and the robot's controller to maximize robot health.",

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note = "Funding Information: This work was supported by NCNR under Grants EP/R02572X/1, EP/P01366X/1, and EP/P017487/1, and by ReLiB under Grant FIRG005. Publisher Copyright: {\textcopyright} 2023 IEEE.; 17th International Symposium on Applied Computational Intelligence and Informatics, SACI 2023 ; Conference date: 23-05-2023 Through 26-05-2023",

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Braun, CA, Ramesh, A, Rothfuss, S, Chiou, M , Stolkin, R & Hohmann, S 2023, Model Predictive Control of the Degree of Automation Optimizing Robot Health. in 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)., 10158596, International Symposium on Applied Computational Intelligence and Informatics, IEEE, pp. 000381-000386, 17th International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, 23/05/23. https://doi.org/10.1109/SACI58269.2023.10158596

Model Predictive Control of the Degree of Automation Optimizing Robot Health. / Braun, Christian Alexander; Ramesh, Aniketh; Rothfuss, Simon et al.
2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2023. p. 000381-000386 10158596 ( International Symposium on Applied Computational Intelligence and Informatics).

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

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AU - Hohmann, Soren

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N2 - Adjusting operator support in human-machine systems is a promising way of combining operator involvement with high overall system performance. Adaptive automation aims to achieve this goal without burdening the operator with the task of selecting and setting the desired amount of support. In this work, two novel adaptive automations are presented. We use the performance measure of 'robot health' to formulate the optimal control problem of maximizing the robot health of a human-robot system through the adaption of operator support to develop two model predictive controllers. The first one considers discrete levels of operator support, or levels of automation, the second one uses the continuous conception of the degree of automation. We report on a proof-of-concept simulation study evaluating the proposed model predictive controllers in a collaborative teleoperation of a mobile robot; the results demonstrate the ability of both model predictive controllers to successfully arbitrate control between the operator and the robot's controller to maximize robot health.

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Braun CA, Ramesh A, Rothfuss S, Chiou M , Stolkin R, Hohmann S. Model Predictive Control of the Degree of Automation Optimizing Robot Health. In 2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE. 2023. p. 000381-000386. 10158596. ( International Symposium on Applied Computational Intelligence and Informatics). doi: 10.1109/SACI58269.2023.10158596

Model Predictive Control of the Degree of Automation Optimizing Robot Health

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Fingerprint

Projects

ReLIB - Faraday Challenge Fast Track proposal - Circular economy

National Centre for Nuclear Robotics (NCNR)

Robust remote sensing for multi-modal characterisation in nuclear and other extreme environments

Robotics for Nuclear Environments - Lead Manchester University (EP/P01366X/1)

Cite this