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Model-free execution monitoring in behavior-based robotics.

Ola Pettersson¹, Lars Karlsson, Alessandro Saffiotti

¹Orebro University, SE-70182 Orebro, Sweden.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|August 19, 2007

Summary

This study introduces model-free execution monitoring for autonomous mobile robots. Pattern recognition techniques classify robot behaviors, enabling robust failure detection without predictive models for safer operation.

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Autonomous mobile robots require robust execution monitoring for safe operation in unpredictable environments.
Current methods often rely on predictive models, which can be complex and brittle.
Detecting and classifying execution failures is crucial for robot autonomy.

Purpose of the Study:

To explore model-free approaches for execution monitoring in autonomous robots.
To investigate the application of pattern recognition techniques for failure detection.
To demonstrate the utility of these methods in real-world robotic navigation tasks.

Main Methods:

Utilized pattern recognition techniques for execution monitoring.
Classified observed robot behavioral patterns as either normal or faulty.

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Conducted experiments with a mobile robot navigating indoor environments.

Main Results:

Demonstrated that pattern recognition can effectively implement model-free execution monitoring.
Successfully classified robot behaviors into normal and faulty execution categories.
Verified the practical utility of the proposed approach through experimental validation.

Conclusions:

Model-free execution monitoring using pattern recognition is a viable alternative to model-based approaches.
This method enhances the safety and reliability of autonomous mobile robots.
Pattern recognition offers a promising direction for developing more robust robot execution monitoring systems.