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The Path Planning and Location Method of Inspection Robot in a Large Storage Tank Bottom.

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This study introduces a novel path planning and localization algorithm for large tank bottom inspection robots. The new method improves path smoothness and accuracy, significantly reducing errors in robot positioning and defect detection.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Mechanical Engineering

Background:

  • Inspection robots are crucial for large tank defect detection.
  • Existing algorithms suffer from local minima and inaccurate positioning.

Purpose of the Study:

  • To develop an improved path planning and localization algorithm for tank bottom inspection robots.
  • To address limitations of existing methods, including local minima and path smoothness.

Main Methods:

  • Designed a preset spiral path tailored to tank bottom geometry.
  • Incorporated a rotating potential field (RPF) to prevent local minima in path planning.
  • Utilized a three-point positioning algorithm based on acoustic emission distance measurement.

Main Results:

  • Achieved accurate and smooth path planning results.
  • The RPF method reduced average Root Mean Square Error (RMSE) by 9.49% compared to state-of-the-art.
  • Demonstrated an average positioning error of only 0.0748 ± 0.0032 on the spiral path.

Conclusions:

  • The proposed algorithm enhances the efficiency and accuracy of large tank bottom inspection robots.
  • The RPF method effectively overcomes path planning local minima issues.
  • The acoustic emission-based positioning system provides highly accurate robot localization.