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Related Experiment Video

Updated: May 23, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Machine vision applied to vehicle guidance.

R M Inigo¹, E S McVey, B J Berger

¹School of Engineering and Applied Science, University of Virginia, Charlottesville, VA 22901.

IEEE Transactions on Pattern Analysis and Machine Intelligence

|April 14, 2012

Summary

This study presents a method for mobile robots to navigate pathways using computer vision. It detects path boundaries and obstacles, enabling safer semiautonomous operation.

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Vision

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Semiautonomous operation of mobile robots requires robust perception systems for navigation.
Identifying pathways and obstacles is crucial for safe and efficient robot locomotion.

Purpose of the Study:

To develop and describe algorithms for mobile robot navigation in typical pathways.
To enable robots to identify path boundaries and detect obstacles for enhanced operational safety.

Main Methods:

Perspective correction and edge detection algorithms identify pathway boundaries.
Hough transform is employed to detect straight path boundaries.
Stereo vision and edge detection are used to identify and differentiate obstacles from shadows.

Related Experiment Videos

Last Updated: May 23, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Main Results:

The system successfully identifies pathway boundaries and determines the distance to road edges.
Obstacles and shadows within the defined region of interest are detected.
Stereo vision provides a method for distinguishing between obstacles and shadows.

Conclusions:

The described methods enable mobile robots to perceive and navigate pathways effectively.
The developed techniques enhance the safety and reliability of semiautonomous robot operation.