Model Predictive Degree of Automation Regulation for Mobile Robots Using Robot Vitals and Robot Health

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

doi:10.1016/j.ifacol.2023.10.1025

Model Predictive Degree of Automation Regulation for Mobile Robots Using Robot Vitals and Robot Health

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

^*Corresponding author for this work

Metallurgy and Materials

Research output: Contribution to journal › Conference article › peer-review

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Abstract

Environmental adversities can severely impact the performance of human-robot teams, potentially even leading to task failure. If the operator and the robot automation are not equally affected, adjusting the degree of automation to shift control authority between them is a means of maintaining the performance of the human-robot team. The robot vitals and robot health framework is a recent approach to quantifying runtime performance degradation in robots. This framework can serve as a methodological foundation for the adjustment of the degree of automation based on the human-robot system's state. In this paper, we contribute two model predictive adaptive automation systems that can adjust either the level or the degree of automation of a robot. These systems optimize robot health to ensure optimal performance of the human-robot team when exposed to adversities. Feasibility studies in simulation showcase the ability of our systems to manage the level and degree of automation, thus allowing for an optimal task execution by the human-robot team.

Original language	English
Pages (from-to)	8345-8350
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	56
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.1025
Publication status	Published - 22 Nov 2023
Event	The 22nd World Congress of the International Federation of Automatic Control - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023

Bibliographical note

Funding:
This work was supported by the UKRI-EPSRC grant EP/R02572X/1 (UK National Centre for Nuclear Robotics).

Keywords

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

UN SDGs

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

Access to Document

10.1016/j.ifacol.2023.10.1025Licence: Creative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)

BraunC2023ModelFinal published version, 497 KBLicence: Creative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)

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

Cite this

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title = "Model Predictive Degree of Automation Regulation for Mobile Robots Using Robot Vitals and Robot Health",

abstract = "Environmental adversities can severely impact the performance of human-robot teams, potentially even leading to task failure. If the operator and the robot automation are not equally affected, adjusting the degree of automation to shift control authority between them is a means of maintaining the performance of the human-robot team. The robot vitals and robot health framework is a recent approach to quantifying runtime performance degradation in robots. This framework can serve as a methodological foundation for the adjustment of the degree of automation based on the human-robot system's state. In this paper, we contribute two model predictive adaptive automation systems that can adjust either the level or the degree of automation of a robot. These systems optimize robot health to ensure optimal performance of the human-robot team when exposed to adversities. Feasibility studies in simulation showcase the ability of our systems to manage the level and degree of automation, thus allowing for an optimal task execution by the human-robot team.",

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author = "Braun, {Christian Alexander} and Aniketh Ramesh and Simon Rothfuss and Manolis Chiou and Rustam Stolkin and Soren Hohmann",

note = "Funding: This work was supported by the UKRI-EPSRC grant EP/R02572X/1 (UK National Centre for Nuclear Robotics).; The 22nd World Congress of the International Federation of Automatic Control, IFAC 2023 ; Conference date: 09-07-2023 Through 14-07-2023",

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AU - Chiou, Manolis

AU - Stolkin, Rustam

AU - Hohmann, Soren

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PY - 2023/11/22

Y1 - 2023/11/22

N2 - Environmental adversities can severely impact the performance of human-robot teams, potentially even leading to task failure. If the operator and the robot automation are not equally affected, adjusting the degree of automation to shift control authority between them is a means of maintaining the performance of the human-robot team. The robot vitals and robot health framework is a recent approach to quantifying runtime performance degradation in robots. This framework can serve as a methodological foundation for the adjustment of the degree of automation based on the human-robot system's state. In this paper, we contribute two model predictive adaptive automation systems that can adjust either the level or the degree of automation of a robot. These systems optimize robot health to ensure optimal performance of the human-robot team when exposed to adversities. Feasibility studies in simulation showcase the ability of our systems to manage the level and degree of automation, thus allowing for an optimal task execution by the human-robot team.

AB - Environmental adversities can severely impact the performance of human-robot teams, potentially even leading to task failure. If the operator and the robot automation are not equally affected, adjusting the degree of automation to shift control authority between them is a means of maintaining the performance of the human-robot team. The robot vitals and robot health framework is a recent approach to quantifying runtime performance degradation in robots. This framework can serve as a methodological foundation for the adjustment of the degree of automation based on the human-robot system's state. In this paper, we contribute two model predictive adaptive automation systems that can adjust either the level or the degree of automation of a robot. These systems optimize robot health to ensure optimal performance of the human-robot team when exposed to adversities. Feasibility studies in simulation showcase the ability of our systems to manage the level and degree of automation, thus allowing for an optimal task execution by the human-robot team.

KW - Adaptive automation

KW - Shared control

KW - Cooperation

KW - Degree of automation

KW - Levels of autonomy

KW - Levels of automation

KW - Mixed initiative control

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DO - 10.1016/j.ifacol.2023.10.1025

M3 - Conference article

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VL - 56

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JO - IFAC-PapersOnLine

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T2 - The 22nd World Congress of the International Federation of Automatic Control

Y2 - 9 July 2023 through 14 July 2023

ER -

Model Predictive Degree of Automation Regulation for Mobile Robots Using Robot Vitals and Robot Health

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

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Projects

National Centre for Nuclear Robotics (NCNR)

Cite this