Optimization of Grasping Efficiency of a Robot Used for Sorting Construction and Demolition Waste

Abstract

The recycling of construction and demolition waste (CDW) remains an urgent problem to be solved. In the industry, raw CDW needs to be manually sorted. To achieve high efficiency and avoid the risks of manual sorting, a sorting robot can be designed to grasp and sort CDW on a conveyor belt. But dynamic grasping on the conveyor belt is a challenge. We collected location information with a three-dimensional camera and then evaluated the method of dynamic robotic grasping. This paper discusses the grasping strategy of rough processed CDW on the conveyor belt, and implements the function of grasping and sorting on the recycling line. Furthermore, two new mathematical models for a robotic locating system are established, the accuracy of the model is tested with Matlab, and the selected model is applied to actual working conditions to verify the sorting accuracy. Finally, the robot kinematics parameters are optimized to improve the sorting efficiency through experiments in a real system, and it was concluded that when the conveyor speed was kept at around 0.25 m·s−1, better sorting results could be achieved. Increasing the speed and shortening the acceleration/deceleration time would reach the maximum efficiency when the load would allow it. Currently, the sorting efficiency reached approximately 2000 pieces per hour, showing a high accuracy.

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Acknowledgments

The authors are thankful for the financial support provided by the Science and Technology Project of Quanzhou (Nos. 2018C100R and 2019G003), the Science and Technology Cooperation Program of Quanzhou (No. 2018C001), the Science and Technology Cooperation Program of Fujian (No. 201811006), the Joint Innovation Project of Industrial Technology in the Fujian Province, and Subsidized Project for Postgraduates’ Innovative Fund in Scientific Research of Huaqiao University.

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Correspondence to Jian-Hong Yang.

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Recommended by Associate Editor Qing-Long Han

Yue-Dong Ku received the B. Eng. degree in vehicle engineering from Huaqiao University, China in 2018. Currently, he is a M. Eng. degree candidate in mechanical engineering from Huaqiao University, China.

His research interests include robot manipulation, automation control, and computer vision.

Jian-Hong Yang received the M. Eng. degree in mechanical engineering from Huaqiao University, China in 2004, and the Ph. D. degree in mechanical engineering from Huaqiao University, China in 2010. Currently, he is a professor in College of Mechanical Engineering and Automation at Huaqiao University, China, and a technical adviser in Fujian South Highway Machinery Co., Ltd, China.

His research interests include robotics, precision measurement and control technology, and modern sensing technology and fault diagnosis.

Huai-Ying Fang received the M. Eng. degree in mechanical manufacturing and automation from Anhui University of Science and Technology, China in 2003, and the Ph. D. degree in mechanical engineering from Huaqiao University, China in 2012. Currently, she is a professor in College of Mechanical Engineering and Automation at Huaqiao University, China.

Her research interests include efficient crushing of brittle materials, modeling and simulation of multiphase flow coupling, and computer dynamic simulation technology.

Wen Xiao received the B. Eng. degree in mechanical manufacturing and automation from Huaqiao University, China in 2018. Currently, he is a M. Eng. degree candidate in mechanical engineering from Huaqiao University, China.

His research interests include machine learning, pattern recognition, and hyper-spectral image technology.

Jiang-Teng Zhuang received the B. Eng. degree in mechanical manufacturing and automation from Huaqiao University, China in 2018. Currently, he is a M. Eng. degree candidate in mechanical engineering from Huaqiao University, China.

His research interests include computer vision, image processing technology, and 3D stereo imaging technology.

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Ku, YD., Yang, JH., Fang, HY. et al. Optimization of Grasping Efficiency of a Robot Used for Sorting Construction and Demolition Waste. Int. J. Autom. Comput. 17, 691–700 (2020). https://doi.org/10.1007/s11633-020-1237-0

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Keywords

  • Construction and demolition waste
  • sorting robot
  • grasping strategy
  • mathematical models
  • robot kinematics parameters