DEEP LEARNING-BASED OBJECT RECOGNITION ROBOT CONTROL VIA WEB AND MOBILE USING AN INTERNET OF THINGS (IoT) CONNECTION

  • Basuki Rahmat Informatics Department, Faculty of Computer Science, Universitas Pembangunan Nasional "Veteran" Jawa Timur
  • Budi Nugroho Informatics Department, Faculty of Computer Science, Universitas Pembangunan Nasional "Veteran" Jawa Timur

Abstract

The paper presents the intelligent surveillance robotic control techniques via web and mobile via an Internet of Things (IoT) connection. The robot is equipped with a Kinect Xbox 360 camera and a Deep Learning algorithm for recognizing objects in front of it. The Deep Learning algorithm used is OpenCV's Deep Neural Network (DNN). The intelligent surveillance robot in this study was named BNU 4.0. The brain controlling this robot is the NodeMCU V3 microcontroller. Electronic board based on the ESP8266 chip. With this chip, NodeMCU V3 can connect to the cloud Internet of Things (IoT). Cloud IoT used in this research is cloudmqtt (https://www.cloudmqtt.com). With the Arduino program embedded in the NodeMCU V3 microcontroller, it can then run the robot control program via web and mobile. The mobile robot control program uses the Android MQTT IoT Application Panel.

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References

[1] B. Zhang, J. Wu, L. Wang, and Z. Yu, “Accurate dynamic modeling and control parameters design of an industrial hybrid spray-painting robot,” Robot. Comput. Integr. Manuf., vol. 63, p. 101923, 2020.
[2] S. Pagano, R. Russo, and S. Savino, “A vision guided robotic system for flexible gluing process in the footwear industry,” Robot. Comput. Integr. Manuf., vol. 65, p. 101965, 2020.
[3] C. Chen, F. Peng, R. Yan, X. Tang, Y. Li, and Z. Fan, “Rapid prediction of posture-dependent FRF of the tool tip in robotic milling,” Robot. Comput. Integr. Manuf., vol. 64, p. 101906, 2020.
[4] J. Jiang, Z. Huang, Z. Bi, X. Ma, and G. Yu, “State-of-the-Art control strategies for robotic PiH assembly,” Robot. Comput. Integr. Manuf., vol. 65, p. 101894, 2020.
[5] H. Cao, J. Zhou, P. Jiang, K. K. B. Hon, H. Yi, and C. Dong, “An integrated processing energy modeling and optimization of automated robotic polishing system,” Robot. Comput. Integr. Manuf., vol. 65, p. 101973, 2020.
[6] K. Lin, Y. Li, J. Sun, D. Zhou, and Q. Zhang, “Multi-sensor fusion for body sensor network in medical human–robot interaction scenario,” Inf. Fusion, vol. 57, pp. 15–26, 2020.
[7] L. Nicholls and Y. Strengers, “Robotic vacuum cleaners save energy? Raising cleanliness conventions and energy demand in Australian households with smart home technologies,” Energy Res. Soc. Sci., vol. 50, pp. 73–81, 2019.
[8] A. Cheong, M. W. S. Lau, E. Foo, J. Hedley, and J. W. Bo, “Development of a Robotic Waiter System,” IFAC-PapersOnLine, vol. 49, no. 21, pp. 681–686, 2016.
[9] M. Lanz, R. Pieters, and R. Ghabcheloo, “Learning environment for robotics education and industry-academia collaboration,” Procedia Manuf., vol. 31, pp. 79–84, 2019.
[10] P. Kopacek, “Robots in Entertainment, Leisure and Hobby New Tasks for Robot Control,” IFAC Proc. Vol., vol. 33, no. 27, pp. 539–543, 2000.
[11] J. Azeta et al., “An Android Based Mobile Robot for Monitoring and Surveillance,” Procedia Manuf., vol. 35, pp. 1129–1134, 2019.
[12] L. Jóźwiak, “Advanced mobile and wearable systems,” Microprocess. Microsyst., vol. 50, pp. 202–221, 2017.
Published
2020-12-15
How to Cite
RAHMAT, Basuki; NUGROHO, Budi. DEEP LEARNING-BASED OBJECT RECOGNITION ROBOT CONTROL VIA WEB AND MOBILE USING AN INTERNET OF THINGS (IoT) CONNECTION. Jurnal Ilmiah Kursor, [S.l.], v. 10, n. 4, dec. 2020. ISSN 2301-6914. Available at: <http://kursorjournal.org/index.php/kursor/article/view/242>. Date accessed: 18 may 2021. doi: https://doi.org/10.21107/kursor.v10i4.242.
Section
Articles