Robot Vitals and Robot Health: Towards Systematically Quantifying Runtime Performance Degradation in Robots Under Adverse Conditions

Aniketh Ramesh, Rustam Stolkin, Manolis Chiou

Research output: Contribution to journalLetterpeer-review

Abstract

This letter addresses the problem of automatically detecting and quantifying performance degradation in remote mobile robots, in real-time, during task execution. A robot may encounter a variety of uncertainties and adversities during task execution, which can impair its ability to carry out tasks effectively and cause its performance to degrade. Such situations can be mitigated or averted by timely detection and intervention, e.g., by a remote human supervisor taking over control in teleoperation mode. Inspired by patient triaging systems in hospitals, we introduce the framework of 'robot vitals' for estimating overall 'robot health'. A robot's vitals are a set of lower-level metrics that estimate a variety of indicators of performance degradation faced by a robot at any given point in time. Robot health is a higher-level metric that combines robot vitals into a single scalar value estimate of performance degradation. Experiments, both in simulation and on a real mobile robot, demonstrate that the proposed robot vitals and robot health can be used effectively for online estimation of robot performance degradation during run-time.

Original languageEnglish
Pages (from-to)10729-10736
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number4
Early online date20 Jul 2022
DOIs
Publication statusPublished - Oct 2022

Bibliographical note

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

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Failure detection and recovery
  • human-robot teaming
  • robotics in hazardous fields

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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