Abstract
With the development of artificial intelligence technology, the application of mobile robots is more and more extensive. How to solve the control problem of mobile robots in complex network environment is one of the core problems that plague the promotion and application of AGV clusters. In view of the above problems, this paper studies the control technology in the cloud big data environment, and realizes the decision, planning and control of AGV in the cloud environment. Firstly, cloud-side data sharing and cross-domain collaboration are used to realize intelligent adaptive association of heterogeneous data, then establish a collaborative hierarchical information cloud processing model, and design a new AGV sensing structure based on various devices such as laser radar and ultrasonic sensors. Finally, a set of AGV motion control methods in cloud environment is proposed. The experimental results show that the efficient coordination among network nodes in the heterogeneous AGV system in the cloud environment is stable overall and has a lower delay rate, which will greatly promote the application of AGV in various complex network environments.
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References
Zhong, X., Feng, M., Huang, W., Wang, Z., Satoh, S.: Poses guide spatiotemporal model for vehicle re-identification. In: 25th International Conference, MMM 2019, Thessaloniki, Greece, 8–11 January, Proceedings, Part II, MultiMedia Modeling (2019)
Zhang, J., et al.: Hybrid computation offloading for smart home automation in mobile cloud computing. Pers. Ubiquitous Comput. 22(1), 121–134 (2018). https://doi.org/10.1007/s00779-017-1095-0
Xu, X., et al.: An IoT-Oriented data placement method with privacy preservation in cloud environment. J. Netw. Comput. Appl. 124(2), 148–157 (2018)
Nguyen, D.A., Huynh, M.K.: A research on locating AGV via RSS signals. Appl. Mech. Mater. 889, 418–424 (2019)
Xu, X., Liu, X., Qi, L., Chen, Y., Ding, Z., Shi, J.: Energy-efficient virtual machine scheduling across cloudlets in wireless metropolitan area networks. Mob. Netw. Appl. (2019). https://doi.org/10.1007/s11036-019-01242-6
Yoshitake, H., Kamoshida, R., Nagashima, Y.: New automated guided vehicle system using real-time holonic scheduling for warehouse picking. IEEE Robot. Autom. Lett. 4(2), 1–1 (2019)
Xu, X., et al.: A computation offloading method over big data for IoT-enabled cloud-edge computing. Future Gener. Comput. Syst. 95, 522–533 (2019)
Xu, X., et al.: An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles. Future Gener. Comput. Syst. 96, 89–100 (2019)
Kim, C.Y., Kim, Y.H., Ra, W.-S.: Modified 1D virtual force field approach to moving obstacle avoidance for autonomous ground vehicles. J. Electr. Eng. Technol. 14, 1367–1374 (2019)
Xu, X., et al.: An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks. J. Netw. Comput. Appl. 133, 75–85 (2019)
Acknowledgements
This paper was funded by the international training and improvement project of graduate students of China agricultural university; the Ministry of Education of China and Chinese Academy of Sciences (Grant: 4444-10099609).
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Zhang, X., Gong, W., Xiang, H., Chen, Y., Li, D., Wang, Y. (2020). Cloud-Based AGV Control System. In: Zhang, X., Liu, G., Qiu, M., Xiang, W., Huang, T. (eds) Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. CloudComp SmartGift 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-48513-9_24
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DOI: https://doi.org/10.1007/978-3-030-48513-9_24
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