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Object Segmentation by Spraying Robot Based on Multi-Layer Perceptron

Author

Listed:
  • Mingxiang Zhu

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China
    Taizhou College, Nanjing Normal University, Taizhou 225300, China)

  • Guangming Zhang

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China)

  • Lingxiu Zhang

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China)

  • Weisong Han

    (College of Transportation Engineering, Nanjing Tech University, Nanjing 211899, China)

  • Zhihan Shi

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China)

  • Xiaodong Lv

    (College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China)

Abstract

The vision system provides an important way for construction robots to obtain the type and spatial location information of the object. The characteristics of the construction environment, construction object, and robot structure are jointly examined in this paper to propose an approach of object segmentation by spraying the robot based on multi-layer perceptron. Firstly, the hand-eye system experimental platform is built through establishing the mathematical model of the system and calibrating the parameters of the model. Secondly, effort is made to carry out research on image preprocessing algorithms and related experiments, and compare the effects of different binocular stereo-matching algorithms in the actual engineering environment. Finally, research and an experiment are conducted to identify the applicability and effect of the depth image object segmentation algorithm based on multi-layer perceptron. The experimental results prove that the application of multi-layer perceptron to object segmentation by spraying robots can meet the requirement on solution accuracy and is suitable for the object segmentation of complex projects in real life. This approach not only overcomes the shortcomings of the existing recognition methods that are poor in accuracy and difficult to be used widely, but also provides basic data for the subsequent three-dimensional reconstruction, thus making a significant contribution to the research of image processing by spraying robots.

Suggested Citation

  • Mingxiang Zhu & Guangming Zhang & Lingxiu Zhang & Weisong Han & Zhihan Shi & Xiaodong Lv, 2022. "Object Segmentation by Spraying Robot Based on Multi-Layer Perceptron," Energies, MDPI, vol. 16(1), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:232-:d:1014703
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    References listed on IDEAS

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    1. Joon Yul Choi & Tae Keun Yoo & Jeong Gi Seo & Jiyong Kwak & Terry Taewoong Um & Tyler Hyungtaek Rim, 2017. "Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
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