Object Segmentation by Spraying Robot Based on Multi-Layer Perceptron
Author:
Zhu Mingxiang, Zhang GuangmingORCID, Zhang Lingxiu, Han Weisong, Shi Zhihan, Lv XiaodongORCID
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.
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
Reference25 articles.
1. Chen, K.Z., and Yun-Zhang, L.I. (2017). A study on construction technology of load-bearing structure of traditional architecture in Taiping district. Archit. Technol. 2. He, G., Ahmad, K.M., Yu, W., Xu, X., and Kumar, J. (2021). A comparative analysis of machine learning and grey models. Arxiv e-prints. 3. On unified framework for continuous-time grey models: An integral matching perspective;Wei;Appl. Math. Model.,2022 4. Hu, M., Mao, J., Li, J., Wang, Q., and Zhang, Y. (2021). A Novel lidar signal denoising method based on convolutional autoencoding deep learning neural network. Atmosphere, 12. 5. Yul, C.J., Keun, Y.T., Gi, S.J., Jiyong, K., Taewoong, U.T., Hyungtaek, R.T., and Liu, B. (2017). Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database. PLoS ONE, 12.
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