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Vision Sharing Method of Network Robot Based on Deep Learning

  • Yang XiaoEmail author
Conference paper
  • 17 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)

Abstract

With the development of science and technology, robots are evolving from scratch, from low-level systems to high-level systems. The future of intelligent robots is towards intelligent and emotional development, and finally to achieve the goal of human-computer coexistence. Network robot is the main direction of future development. It is a robot controlled by computer network. Human-computer interaction technology, visual sharing and remote monitoring technology, data transmission and communication are the focus of their research. Therefore, the purpose of this paper is to explore the research of vision sharing method based on the theory of robotic algorithm, which leads to different thinking about the future development direction of robots. This paper will adopt the research method of concrete analysis of specific problems to make data comparison and draw conclusions. The results of this study show that as a typical representative of advanced manufacturing equipment, the network intelligent robot will have a large industrial development space and broad market prospects. At the same time, the country should draw lessons from the successful practices of foreign advanced robot industry development, formulate the development strategy and related policies of the robot industry, which is the key to the success or failure of China’s robot industry development.

Keywords

Network robot Vision sharing Artificial intelligence Human-computer cooperation 

References

  1. 1.
    Torres-González, A., Dios, M.D., Ollero, A.: Range-only slam for robot-sensor network cooperation. Auton. Robot. 42(4), 1–15 (2017)Google Scholar
  2. 2.
    He, W., Chen, Y., Yin, Z.: Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans. Cybern. 46(3), 620–629 (2017)CrossRefGoogle Scholar
  3. 3.
    Gao, B., Han, W.: Neural network model reference decoupling control for single leg joint of hydraulic quadruped robot. Assembly Autom. 38(4), 465–475 (2018)CrossRefGoogle Scholar
  4. 4.
    Gang, C., Zhang, W.G., Wang, L.M.: Fuzzy-neural-network-based speed control method and experiment verification for electromagnetic direct drive robot driver. Chin. Sci. Bull. 62(30), 3514–3527 (2017)CrossRefGoogle Scholar
  5. 5.
    Singh, N.H., Thongam, K.: Neural network-based approaches for mobile robot navigation in static and moving obstacles environments. Intell. Serv. Robot. 12(1), 1–13 (2018)Google Scholar
  6. 6.
    Yan, X., Lu, Y.: Progressive visual secret sharing for general access structure with multiple decryptions. Multimedia Tools Appl. 77(2), 1–20 (2017)Google Scholar
  7. 7.
    Zhang, W., Shih, F.Y., Hu, S., Jian, M.: A visual secret sharing scheme based on improved local binary pattern. Int. J. Pattern Recogn. Artif. Intell. 32(2), 5 (2018)MathSciNetGoogle Scholar
  8. 8.
    Ding, H., Li, Z., Wei, B.: (k, n) halftone visual cryptography based on Shamir’s secret sharing. J. China Univ. Posts Telecommun. 25(2), 60–76 (2018)Google Scholar
  9. 9.
    Song, W., Lu, Y., Yan, X., Wang, Y., Chang, C.: Visual secret sharing scheme for (k, n) threshold based on QR code with multiple decryptions. J. Real-Time Image Process. 14(9), 1–16 (2017)Google Scholar
  10. 10.
    Liu, Y., Chang, C.C.: A turtle shell-based visual secret sharing scheme with reversibility and authentication. Multimedia Tools Appl. 77(3), 1–16 (2018)MathSciNetGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina

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