Skip to main content

Research on the Intelligent Unmanned Vehicle Measurement System

  • Conference paper
  • First Online:
Recent Trends in Intelligent Computing, Communication and Devices

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1006))

  • 1232 Accesses

Abstract

With the expansion of cities and the development of modern life, the traditional vehicle-mounted measurement control system has been difficult to meet the measurement demand in some cases. In order to meet the requirements of vehicle-mounted measurement intelligence and solve the problems of complicated and refined measurement tasks, an intelligent unmanned vehicle-mounted measurement system is proposed. This paper is based on artificial intelligence technology, unmanned vehicle as a platform, the control system modular design, realized unmanned vehicle measurement. This paper presents part of the data obtained from the current manned vehicle measurement, briefly introduces the control system, and discusses the advantages of the intelligent unmanned vehicle measurement system. This study provides some suggestions for the development of intelligent unmanned vehicle measurement equipment.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  1. Zhou, Z.-H., Wu, J., Tang, W.: Corrigendum to “Ensembling neural networks: many could be better than all” [Artificial Intelligence 137 (1–2) (2002) 239–263]. Artif. Intell. 137(1), 239–263 (2002)

    Article  Google Scholar 

  2. Ramos, C., Augusto, J.C., Shapiro, D.: Ambient Intelligence—the next step for artificial intelligence. IEEE Intell. Syst. 23(2), 15–18 (2008)

    Article  Google Scholar 

  3. Ververidis, D., Kotropoulos, C.: Emotional speech recognition: resources, features, and methods. Speech Commun. 48(9), 1162–1181 (2006)

    Article  Google Scholar 

  4. Hinton, G., Deng, L., Yu, D., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)

    Article  Google Scholar 

  5. Inigo, R.M.: Application of machine vision to traffic monitoring and control. IEEE Trans. Veh. Technol. 38(3), 112–122 (2002)

    Article  Google Scholar 

  6. Paliwal, J., Visen, N.S., Jayas, D.S., et al.: Cereal grain and dockage identification using machine vision. Biosyst Eng 85(1), 51–57 (2003)

    Article  Google Scholar 

  7. Pennebaker, J.W., Mehl, M.R., Niederhoffer, K.G.: Psychological aspects of natural language use: our words, our selves. Annu. Rev. Psychol. 54(1), 547–577 (2003)

    Article  Google Scholar 

  8. Cai, G., Chen, B.M., Dong, X., et al.: Design and implementation of a robust and nonlinear flight control system for an unmanned helicopter. Mechatronics 21(5), 803–820 (2011)

    Article  Google Scholar 

  9. Du, H., Fan, G., Yi, J.: Autonomous takeoff control system design for unmanned seaplanes. Ocean Eng. 85(3), 21–31 (2014)

    Article  Google Scholar 

  10. Xia, Y., Pu, F., Fu, M., et al.: Modeling and compound control for unmanned turret system with coupling. IEEE Trans. Industr. Electron. 63(9), 5794–5803 (2016)

    Article  Google Scholar 

  11. Conejero, J.A., Jordán, C., Sanabria-Codesal, E.: An algorithm for self-organization of driverless vehicles of a car-rental service. Nonlinear Dyn. 84(1), 107–114 (2016)

    Article  MathSciNet  Google Scholar 

  12. Chang, T.H., Hsu, C.S., Wang, C., et al.: Onboard measurement and warning module for irregular vehicle behavior. IEEE Trans. Intell. Transp. Syst. 9(3), 501–513 (2008)

    Article  Google Scholar 

  13. Wang, Q., Terzis, A., Szalay, A.: A novel soil measuring wireless sensor network. IEEE, 412–415 (2010)

    Google Scholar 

  14. Howlader, M.M.R., Selvaganapathy, P.R., Deen, M.J., et al.: Nanobonding technology toward electronic, fluidic, and photonic systems integration. IEEE J. Sel. Top. Quantum Electron. 17(3), 689–703 (2011)

    Article  Google Scholar 

  15. Zaharia, M., Xin, R.S., Wendell, P., et al.: Apache Spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)

    Article  Google Scholar 

  16. Brovelli, M.A., Cannata, M.: Digital terrain model reconstruction in urban areas from airborne laser scanning data: the method and an example for Pavia (northern Italy). Comput. Geosci. 30(4), 325–331 (2004)

    Article  Google Scholar 

  17. Zheng, J.Y., Tsuji, S.: Panorama representation for route recognition by a mobile robot. Int. J. Comput. Vision 9(1), 55–76 (1992)

    Article  Google Scholar 

  18. Pires, A., Chaminé, H.I., Piqueiro, F., et al.: Combining coastal geoscience mapping and photogrammetric surveying in maritime environments (Northwestern Iberian Peninsula): focus on methodology. Environ. Earth Sci. 75(3), 196 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bing Zhang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, B., Liu, L., Zhao, W. (2020). Research on the Intelligent Unmanned Vehicle Measurement System. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_43

Download citation

Publish with us

Policies and ethics