Design of Robot Arm for Service Using Deep Learning and Sensors


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 5, pp. 221-228, May. 2022
https://doi.org/10.3745/KTSDE.2022.11.5.221,   PDF Download:
Keywords: Robot Arm, Deep Learning, Inverse Kinematics, Force Sensor
Abstract

With the application of artificial intelligence technology, robots can provide efficient services in real life. Unlike industrial manipulators that do simple repetitive work, this study presented design methods of 6 degree of freedom robot arm and intelligent object search and movement methods for use alone or in collaboration with no place restrictions in the service robot field and verified performance. Using a depth camera and deep learning in the ROS environment of the embedded board included in the robot arm, the robot arm detects objects and moves to the object area through inverse kinematics analysis. In addition, when contacting an object, it was possible to accurately hold and move the object through the analysis of the force sensor value. To verify the performance of the manufactured robot arm, experiments were conducted on accurate positioning of objects through deep learning and image processing, motor control, and object separation, and finally robot arm was tested to separate various cups commonly used in cafes to check whether they actually operate.


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Cite this article
[IEEE Style]
M. S. Pak, K. T. Kim, M. S. Koo, Y. J. Ko, S. H. Kim, "Design of Robot Arm for Service Using Deep Learning and Sensors," KIPS Transactions on Software and Data Engineering, vol. 11, no. 5, pp. 221-228, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.5.221.

[ACM Style]
Myeong Suk Pak, Kyu Tae Kim, Mo Se Koo, Young Jun Ko, and Sang Hoon Kim. 2022. Design of Robot Arm for Service Using Deep Learning and Sensors. KIPS Transactions on Software and Data Engineering, 11, 5, (2022), 221-228. DOI: https://doi.org/10.3745/KTSDE.2022.11.5.221.