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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

542
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
542

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Updated: Nov 15, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Coal and Gangue Separating Robot System Based on Computer Vision.

Zhiyuan Sun1, Linlin Huang1, Ruiqing Jia2

  • 1School of Mechanical Electronic & Information Engineering, China University of Mining & Technology, Beijing 100083, China.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a robot system using computer vision to efficiently separate coal from gangue, achieving 98% identification accuracy and 75% separation rate on moving belts. This innovation improves coal quality and resource utilization while minimizing environmental impact.

Keywords:
YOLOcoal and ganguedetectionrobot

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Materials Science

Background:

  • Coal production generates significant gangue, impacting coal quality, environmental conditions, and resource efficiency.
  • Traditional separation methods face challenges due to the subtle visual differences between coal and gangue, and the irregular shapes of gangue particles.
  • Robotic systems with computer vision offer a promising, efficient, and environmentally friendly solution for automated coal-gangue separation.

Purpose of the Study:

  • To develop an advanced robot system for accurate and real-time separation of coal and gangue.
  • To address the challenges of visual identification and robotic grasping in complex industrial environments.
  • To enhance coal quality, resource utilization, and environmental sustainability in coal processing.

Main Methods:

  • Implementation of a computer vision-based robot system for coal-gangue separation.
  • Development of a convolutional neural network (CNN) for precise classification and localization of coal and gangue.
  • Design of a multi-objective motion planning algorithm for real-time robotic grasping and separation.

Main Results:

  • Achieved a high coal-gangue identification accuracy of 98% while maintaining real-time operational capability.
  • Demonstrated an average separation rate of 75% across low-, medium-, and high-speed conveyor belts.
  • Validated the system's effectiveness through simulation and experimental testing, meeting practical project requirements.

Conclusions:

  • The developed computer vision-based robot system effectively addresses the challenges of coal-gangue separation.
  • The system offers significant improvements in accuracy, efficiency, and real-time performance for industrial applications.
  • This approach provides valuable guidance for object detection and separation tasks in complex, dynamic environments.