Record STMT-WCB80F7Aspirational

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

Report Institutional policyGlobalin force

This academic paper presents two model predictive adaptive automation systems designed to automatically regulate a mobile robot's level or degree of automation during runtime. The framework leverages a recently introduced "robot vitals and robot health" metric to quantify performance degradation when human-robot teams face environmental adversities. Through simulation studies, the authors demonstrate that optimizing robot health via dynamic control authority allocation improves task execution compared to static manual or fully autonomous operation.

Summary and content fields are drawn from the document text and are pending editorial review.

Who issued it

Informa UK Limited (Taylor & Francis Group)Industry · GB

Publisher

Legal forceAspirational

Under whose authorityInstitutional policy

Judged fromfull text

Who it governs

Researchers and academicsAI developers and providers

This paper should be read by robotics researchers and developers interested in adaptive autonomy and shared control systems.

Sectors

Research and science · robotics researchTransport and mobility · mobile robot navigation

What the document contains

What's covered

The document proposes and simulates two model predictive control architectures for adjusting the degree or level of automation in mobile human-robot teams based on robot health metrics and environmental adversities.

What's missing

The text does not provide regulatory, legal, or policy guidelines for commercial robot deployment, nor does it contain governmental enforcement mechanisms.

Key takeaways

Environmental adversities can severely impact human-robot team performance, necessitating dynamic control allocation.
The robot vitals and robot health framework can be used to quantify runtime performance degradation in mobile robots.
Model predictive adaptive automation systems can successfully adjust either the level or the degree of automation during task execution.
Continuous degree of automation regulation outperforms static fully autonomous or manual control modes by optimizing robot health.

Outline

· 1. Introduction
· 2. Literature Review
· 3. Problem Statement
· 4. Model Predictive Adaptive Automation
· 5. Simulation Results
· 6. Conclusion

Issued2023

Length10,785 words · 7 pages

FormatNot recorded

Scopeglobal

How it scores

Pending review

Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.

Accountability

0/5

Clear responsibility for AI outcomes

screening score · 2026-10-23

Fairness

0/5

Non-discrimination and equitable treatment

screening score · 2026-10-23

Human Dignity

0/5

Respect for the inherent worth of persons

screening score · 2026-10-23

Human Oversight

3/5

Humans remain in control of consequential decisions

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.

Abstract

screening score · 2026-10-23

Privacy

0/5

Protection of personal data and private life

screening score · 2026-10-23

Enforcement

0/5

Consequences and compliance mechanisms with real force

screening score · 2026-10-23

Impact Assessment

0/5

Required assessment of an AI system's risks/impacts before or during use

screening score · 2026-10-23

Topics

Physical AI Governancesupporting

adaptive_automation shared_control mobile_robots robot_health model_predictive_control