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Autonomous Trajectory Planning for Spray Painting on Complex Surfaces Based on a Point Cloud Model.

Saul Nieto Bastida1, Chyi-Yeu Lin1,2

  • 1Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.

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Summary
This summary is machine-generated.

This study introduces an algorithm for autonomous robot trajectory generation in spray painting complex surfaces using 3D point cloud data. It simplifies path planning for varied production lines, enhancing efficiency and quality.

Keywords:
autonomous path planningfree-form surfacepoint cloud basedspray painting

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Area of Science:

  • Robotics
  • Computer Vision
  • Manufacturing Automation

Background:

  • Traditional robot programming for tasks like spray painting is complex and time-consuming.
  • Current methods are inefficient for low-volume, high-variation production lines due to setup time.
  • Surface complexity and the need for skilled operators pose significant challenges in automated spray painting.

Purpose of the Study:

  • To develop an algorithm for autonomous robot trajectory generation for spray painting complex surfaces.
  • To overcome the limitations of traditional programming methods in variable production environments.
  • To enable efficient and high-quality spray painting on objects with intricate geometries.

Main Methods:

  • Utilizing 3D point cloud data of objects as input.
  • Employing a predefined spherical mesh to organize geometrical attributes.
  • Implementing an algorithm for automatic path planning and region extraction.
  • Developing a graphical user interface (GUI) for parameter definition and visualization.
  • Integrating a 3D sensor for real-time workpiece localization and trajectory adjustment.

Main Results:

  • Successfully generated autonomous robot trajectories for spray painting complex surfaces.
  • Validated the approach in simulation with diverse workpieces.
  • Demonstrated the efficacy of the method on a real robot for motion execution.
  • The GUI facilitates visualization of point clouds, trajectories, and simulated paint quality.

Conclusions:

  • The proposed algorithm effectively automates robot path planning for spray painting complex objects.
  • This approach offers a flexible and efficient solution for low-production and high-variation manufacturing lines.
  • The integration of 3D sensing and autonomous path planning enhances precision and quality in robotic spray painting.