Abstract
Recent advances in deep learning have enabled robots to grasp objects even in complex environments. However, a large amount of data is required to train the deep-learning network, which leads to a high cost in acquiring the learning data owing to the use of an actual robot or simulator. This paper presents a new form of grasp data that can be generated automatically to minimize the data-collection cost. The depth image is converted into simplified grasp data called an irregular depth tile that can be used to estimate the optimal grasp pose. Additionally, we propose a new grasping algorithm that employs different methods according to the amount of free space in the bounding box of the target object. This algorithm exhibited a significantly higher success rate than the existing grasping methods in grasping experiments in complex environments.
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This research was supported by the MOTIE under the Industrial Foundation Technology Development Program supervised by the KEIT (No. 20008613).
Da-Wit Kim received his B.S. degree in aerospace & mechanical engineering from Korea Aerospace University in 2018 and his M.S. degree in mechatronics from Korea University in 2020.
HyunJun Jo received his B.S. degree from the School of Mechanical Engineering of Korea University in 2016. He is currently pursuing a Ph.D. degree at the School of Mechanical Engineering of Korea University. His research interests include deep learning, robot manipulation, and computer vision.
Jae-Bok Song received his B.S. and M.S. degrees in mechanical engineering from Seoul National University in Seoul, Korea, in 1983 and 1985, respectively. He received a Ph.D. in mechanical engineering from M.I.T. in 1992. He has been a professor in the Department of Mechanical Engineering at Korea University since 1993. His research interests include robot safety, manipulator design and control, and AI-based robot applications. He is a senior member of IEEE.
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Kim, DW., Jo, H. & Song, JB. Irregular Depth Tiles: Automatically Generated Data Used for Network-based Robotic Grasping in 2D Dense Clutter. Int. J. Control Autom. Syst. 19, 3428–3434 (2021). https://doi.org/10.1007/s12555-019-0758-1
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DOI: https://doi.org/10.1007/s12555-019-0758-1