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

Research output: Contribution to journalConference articlepeer-review

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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 languageEnglish
Pages (from-to)8345-8350
Number of pages6
Issue number2
Publication statusPublished - 22 Nov 2023
EventThe 22nd World Congress of the International Federation of Automatic Control - Yokohama, Japan
Duration: 9 Jul 202314 Jul 2023

Bibliographical note

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


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


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