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A Collaborative Approach for Surface Inspection Using Aerial Robots and Computer Vision.

Martin Molina¹, Pedro Frau², Dario Maravall³

¹Department of Artificial Intelligence, Technical University of Madrid, Boadilla del Monte, Madrid 28660, Spain. martin.molina@upm.es.

Sensors (Basel, Switzerland)

|March 23, 2018

Summary

This summary is machine-generated.

This study introduces a semi-automatic approach for aerial robot surface inspection, enhancing autonomy through human-robot collaboration. Drones request human assistance when uncertain, improving anomaly detection in challenging environments.

Keywords:

Automated visual inspection aerial robotics human-robot collaboration surface inspection

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Aerial robots with onboard cameras are crucial for inspecting hard-to-reach surfaces.
Increasing the autonomy of these robots is desirable for efficient inspection.
Current automated visual recognition methods struggle in uncontrolled environments, limiting fully automatic inspection.

Purpose of the Study:

To present a semi-automatic visual inspection solution that enhances aerial robot autonomy.
To enable human-robot collaboration for improved surface anomaly detection.
To address the limitations of fully automated inspection in real-world scenarios.

Main Methods:

Developed a semi-automatic approach based on human-robot collaboration.
Implemented a system where drones delegate exploration and recognition tasks.
Integrated a mechanism for drones to request human assistance when encountering uncertainty.

Main Results:

Validated the proposed solution through an experimental robotic system.
Utilized the Aerostack software framework for system development.
Demonstrated an increased degree of autonomy in detecting surface anomalies.

Conclusions:

The human-robot collaboration model significantly boosts aerial robot autonomy for surface inspection.
The developed system effectively handles uncertainty by seeking operator assistance.
This approach offers a practical solution for improving automated visual inspection capabilities.