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Xiangtao Kong1

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Summary
This summary is machine-generated.

This study introduces an improved robotic arm loading method using visual guidance and a refined sparrow search algorithm. It enhances target localization and path planning for complex industrial environments.

Keywords:
Feature extractionMachine learningMechanical fault diagnosisMulti-strategy improvement methodSparrow search algorithm

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Automated robotic arm loading faces challenges in unstable target localization and inefficient path planning.
  • Complex lighting and dynamic environments exacerbate these issues in industrial settings.

Purpose of the Study:

  • To develop an integrated method for automated robotic arm loading that improves target localization and path planning efficiency.
  • To address the limitations of current methods in complex and dynamic industrial environments.

Main Methods:

  • A visual guidance system using a depth camera for object recognition and 3D pose mapping via template matching and depth fusion.
  • An improved sparrow search algorithm incorporating non-dominated sorting and multinomial mutation for trajectory planning.
  • Optimization of motion time and impact smoothness as multi-objective constraints.

Main Results:

  • The proposed method achieves reliable target object recognition and stable spatial perception.
  • Enhanced sparrow search algorithm demonstrates improved optimization capabilities in dynamic, multi-objective scenarios.
  • Experimental results show high grasping success rates, superior positioning accuracy, and increased path planning efficiency.

Conclusions:

  • The integrated visual guidance and improved sparrow search algorithm effectively enhances automated robotic arm loading.
  • The method is validated as effective and practical for industrial applications, outperforming comparative approaches.