Quadruped Robot for Walking on the Uneven Terrain and Object Detection using Deep Learning


KIPS Transactions on Software and Data Engineering, Vol. 12, No. 5, pp. 237-242, May. 2023
https://doi.org/10.3745/KTSDE.2023.12.5.237,   PDF Download:
Keywords: Quadruped Robot, Deep Learning, Object Detection, Walking Robo
Abstract

Research on high-performance walking robots is being actively conducted, and quadruped walking robots are receiving a lot of attention due to their excellent mobility and adaptability on uneven terrain, but they are difficult to introduce and utilize due to high cost. In this paper, to increase utilization by applying intelligent functions to a low-cost quadruped robot, we present a method of improving uneven terrain overcoming ability by mounting IMU and reinforcement learning on embedded board and automatically detecting objects using camera and deep learning. The robot consists of the legs of a quadruped mammal, and each leg has three degrees of freedom. We train complex terrain in simulation environments with designed 3D model and apply it to real robot. Through the application of this research method, it was confirmed that there was no significant difference in walking ability between flat and non-flat terrain, and the behavior of performing person detection in real time under limited experimental conditions was confirmed.


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Cite this article
[IEEE Style]
M. S. Pak, S. M. Han, S. H. Kim, "Quadruped Robot for Walking on the Uneven Terrain and Object Detection using Deep Learning," KIPS Transactions on Software and Data Engineering, vol. 12, no. 5, pp. 237-242, 2023. DOI: https://doi.org/10.3745/KTSDE.2023.12.5.237.

[ACM Style]
Myeong Suk Pak, Seong Min Han, and Sang Hoon Kim. 2023. Quadruped Robot for Walking on the Uneven Terrain and Object Detection using Deep Learning. KIPS Transactions on Software and Data Engineering, 12, 5, (2023), 237-242. DOI: https://doi.org/10.3745/KTSDE.2023.12.5.237.