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Updated: Jun 19, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

A survey of ontology-enabled processes for dependable robot autonomy.

Esther Aguado^1,2, Virgilio Gomez^1,2, Miguel Hernando²

¹Autonomous Systems Laboratory, Universidad Politécnica de Madrid, Madrid, Spain.

Frontiers in Robotics and AI

|July 25, 2024

Summary

Knowledge-based approaches using ontologies enhance autonomous robots. This research reviews how robots can adapt and re-plan missions in real-time for improved dependability and trust.

Keywords:

adaptation ontology resilience robot autonomy robustness

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Area of Science:

Robotics
Artificial Intelligence
Knowledge Representation

Background:

Autonomous robots operate in diverse domains but face challenges in dependability and trust.
Knowledge representation and reasoning offer tools for deeper situational understanding.

Purpose of the Study:

To explore the application of declarative knowledge and ontologies in autonomous robots.
To review research enhancing robot autonomy through knowledge-based systems.

Main Methods:

Systematic exploration and analysis of ontologies used in autonomous robotics.
Surveying research on knowledge representation for robot environment, design, and mission reasoning.

Main Results:

Ontologies facilitate robot adaptation and re-planning at runtime for mission completion.
Knowledge-driven approaches improve flexibility, explainability, and efficacy in autonomous systems.

Conclusions:

Declarative knowledge and ontologies are crucial for advancing robot autonomy.
Leveraging ontologies in real-time ensures mission success and aligns with user expectations.