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A Model of Spray Tool and a Parameter Optimization Method for Spraying Path Planning

  • Research Article
  • Robotics
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Abstract

The digital camouflage spraying of special vehicles carried out by robots can greatly improve the spraying efficiency, spraying quality, and rapid adaptability to personalized patterns. The selection of spray tool and the accuracy of the adopted mathematical spray tool model has a great impact on the effectiveness of spray path planning and spraying quality. Since traditional conical spray tool models are not suitable for spraying rectangular digital camouflage, according to the characteristics of digital camouflage, the coating thickness cumulative distribution model of strip nozzle spray tool for 2D plane spraying and 3D surface spraying is derived, and its validity is verified by simulation. Based on the accumulation velocity model of the coating thickness (AVCT) on the curved surface and aiming at spraying path planning within the same surface and different surfaces, a path parameter optimization method based on coating uniformity evaluation of adjacent path overlapping area is proposed. Combined with the vehicle surface model, parameters such as path interval, spray tool angle and spray tool motion velocity can be calculated in real-time to ensure uniform coating. Based on the known local three-dimensional model of vehicle surface and the comprehensive spraying simulation, the validity of the purposed models: the coating thickness on the adjacent path area (CTAPA), the coating thickness on the intersection of two surfaces (CTITS), the coating thickness on the intersection of a plane and a surface (CTIPS), and the optimization method of path parameters are verified. The results show that compared with the traditional spray tool, the strip nozzle can better ensure the uniformity of the coating thickness of digital camouflage spray. Finally, according to a practical spraying experiment, the results prove that the proposed models not only are effective but also meet the practical industrial requirements and are of great practical value.

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Acknowledgements

This work was supported by Key Research and Development Program of China (No. 2018YFB1306303)

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Authors and Affiliations

  1. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100080, China

    Ru-Xiang Hua, Wei Zou & Hong-Xuan Ma

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Ru-Xiang Hua, Wei Zou & Hong-Xuan Ma

  3. Shandong Jonkin Intelligent Technology Co., Ltd, Weihai, 264200, China

    Guo-Dong Chen

  4. Inner Mongolia First Machinery Group of Co., Ltd, Baotou, 014000, China

    Wei Zhang

Authors
  1. Ru-Xiang Hua
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  2. Wei Zou
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  3. Guo-Dong Chen
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  4. Hong-Xuan Ma
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  5. Wei Zhang
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Correspondence to Ru-Xiang Hua.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Ru-Xiang Hua received the B. Eng. degree in mechanical engineering from China University of Mining and Technology, China in 2016, the M. Eng. degree from Beijing Information Science and Technology University, China in 2019. Currently, he is a Ph. D. degree candidate in control science and engineering at Institute of Automation, Chinese Academy of Sciences, China. He is also with University of Chinese Academy of Sciences, China.

His research interests include spray path planning and intelligent control.

Wei Zou received the B. Eng. degree in control science and engineering from Baotou University of Iron and Steel Technology, China in 1997, the M. Eng. degree in control science and engineering from Shandong University of Technology, China in 2000, and the Ph. D. degree in control science and engineering from Institute of Automation, Chinese Academy of Sciences (IACAS), China in 2003. Currently, he is a professor at the Research Center of Precision Sensing and Control, IACAS.

His research interests include intelligent robotics, visual servoing, robot localization and navigation.

Guo-Dong Chen received the B. Eng. degree in computer control engineering from China University of Mining and Technology, China in 2016, the M. Eng. degree in software engineering from Beijing Information Science and Technology University, China in 2019. Currently, he is a Ph.D. degree candidate in control science and engineering at Institute of Automation, Chinese Academy of Sciences, China. He is also with University of Chinese Academy of Sciences, China.

His research interests include spray path planning and intelligent control.

Hong-Xuan Ma received the B. Sc. degree in software engineering from Central South University, China in 2016. He is currently a Ph. D. degree candidate in Institute of Automation, Chinese Academy of Sciences, China. He is also with University of Chinese Academy of Sciences, China.

His research interests include computer vision and robotics.

Wei Zhang received the B. Eng. degree in mechatronic engineering from Inner Mongolia University of Technology, China in 2011. Currently, he is a vehicle engineer at Inner Mongolia First Machinery Group co., Ltd, China.

His research interests include industrial robot and vehicle manufacturing.

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Hua, RX., Zou, W., Chen, GD. et al. A Model of Spray Tool and a Parameter Optimization Method for Spraying Path Planning. Int. J. Autom. Comput. 18, 1017–1031 (2021). https://doi.org/10.1007/s11633-021-1310-3

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  • DOI: https://doi.org/10.1007/s11633-021-1310-3

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