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Common Leveling Mistakes and Errors

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Related Experiment Video

Updated: May 29, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

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

Published on: October 14, 2017

Sensing error for a mobile robot using line navigation.

K C Drake1, E S McVey, R M Inigo

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

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study determines optimal line widths for autonomous mobile robot navigation in factories. Analytical methods consider sensor details and errors to ensure reliable visual guidance systems.

Related Experiment Videos

Last Updated: May 29, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

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

Published on: October 14, 2017

Area of Science:

  • Robotics
  • Computer Vision
  • Industrial Automation

Background:

  • Autonomous mobile robots (AMRs) are increasingly used in factory settings.
  • Visual navigation systems rely on distinct environmental features for guidance.
  • Contrasting lines are a common method for visual navigation in industrial environments.

Purpose of the Study:

  • To develop and analyze the use of contrasting lines for visual navigation of AMRs in factories.
  • To determine the optimal linewidths for reliable robot guidance.
  • To investigate the impact of sensor parameters and error conditions on navigation line visibility.

Main Methods:

  • Analytical determination of minimum and maximum linewidths.
  • Consideration of sensor geometry, field of view, and system error conditions.
  • Analysis of error effects on line width in the image plane.

Main Results:

  • Established analytical methods for calculating optimal linewidths.
  • Quantified the influence of sensor parameters and errors on perceived line width.
  • Provided numerical examples with typical sensor parameters.

Conclusions:

  • Optimal linewidths are crucial for robust AMR visual navigation.
  • Sensor characteristics and error analysis are key to defining these optimal widths.
  • The developed methodology enables precise line design for factory navigation systems.