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Weld Seam Tracking and Detection Robot Based on Artificial Intelligence Technology.

Jiuxin Wang1, Lei Huang2, Jiahui Yao1

  • 1School of Science, Xi'an Polytechnic University, Xi'an 710048, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

A novel wall-climbing robot with integrated weld seam tracking and detection was developed. This robot utilizes an optimized DeepLabv3+ model for precise, real-time weld inspection, enhancing industrial safety and efficiency.

Keywords:
DeepLabv3+inspection robotwall-climbing robotweld seam identification

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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Materials Science and Engineering

Background:

  • Weld seam detection is critical for safety and efficiency in large-scale special equipment manufacturing.
  • Existing methods often lack the precision and real-time capabilities required for complex industrial environments.
  • Robotic solutions offer a promising avenue for automated and intelligent inspection.

Purpose of the Study:

  • To design and develop a wall-climbing robot capable of autonomous weld seam tracking and detection.
  • To optimize a semantic segmentation model for efficient deployment on embedded systems for real-time analysis.
  • To improve the accuracy and speed of weld seam identification for industrial applications.

Main Methods:

  • A wall-climbing robot was engineered using a permanent magnet array and Mecanum wheels.
  • The DeepLabv3+ semantic segmentation model was optimized with Mobilenetv2 and attention mechanisms.
  • Depthwise separable dilated convolutions replaced traditional convolutions for enhanced efficiency.
  • Least squares method was employed for welding path fitting based on segmentation results.

Main Results:

  • The optimized model volume was reduced by 92.9% to 21.8 Mb.
  • Average precision reached 98.5%, an improvement of 1.4% over the original model.
  • Reasoning speed achieved 21 frames/s, meeting real-time industrial detection requirements.
  • The robot successfully demonstrated autonomous weld seam identification and tracking.

Conclusions:

  • The developed wall-climbing robot effectively integrates seam tracking and detection capabilities.
  • Model optimization significantly enhances performance for embedded deployment in industrial settings.
  • This technology advances automatic and intelligent weld seam detection, contributing to improved industrial safety and efficiency.