Abstract
The conventional operation and maintenance of 5G computer rooms is characterized by a low level of automation, which can result in untimely replacement of hard disks and potential data loss. In order to achieve efficient and timely replacement of hard disks in 5G computer rooms, this paper proposes a novel methodology for attitude recognition of such disks using an RGB-D depth camera and quintic polynomial interpolation algorithm. This method obtains two-dimensional position information of the hard disk through RGB images, and then combines depth images to obtain the coordinate system of the three-dimensional hard disk. The precise identification of the area to grasp a hard disk is achieved through the design of the grasping process, RGB-D image preprocessing, attitude estimation, and grasping trajectory planning. Additionally, by combining hard disk attitude estimation, the robot arm can be effectively controlled to complete the grasping process of the hard disk. The experiments carried out on the visual recognition method proposed in the article have shown that it achieves high accuracy in recognizing the grasping area of a hard disk. Moreover, the robot arm grasping system based on this method has been used to replace hard disks in 5G computer rooms automatically.
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Acknowledgements
This research is supported by National Natural Science Foundation of China (Grant nos. 52205536). Wuhan Science and Technology Program (Grant nos. 2022012202015069). Gusu Innovation and Entrepreneurship Leading Talent Plan (Grant nos. ZXL2022518). Provincial Service Industry Development Guided Funds Plan of Wuhan in 2022 (Grant nos. Wufa Reform Service [2023] NO.120). Fourteenth Graduate Education Innovation Fund of Wuhan Institute of Technology (Grant nos. CX2022076).
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Li, C., Zhang, C., Shi, L., Zheng, R., Shen, Q. (2023). Hard Disk Posture Recognition and Grasping Based on Depth Vision. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14267. Springer, Singapore. https://doi.org/10.1007/978-981-99-6483-3_46
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