Abstract
Grasp point detection is a necessary ability to handle for industrial robots. In recent years, various deep learning-based techniques for robotic grasping have been introduced. To follow this trend, we introduce a convolutional neural network-based approach for model-free one step method for grasp point detection. This method provides all feasible grasp points suitable for parallel grippers, based on a single RGB image of the scene. A case study, which shows the outstanding accuracy of the presented approach as well as its acceptable response time, is presented at the end of this contribution.
The work has been supported by the SGS grant no. SGS_2021_019 at the University of Pardubice. This support is very gratefully acknowledged.
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Dolezel, P., Stursa, D., Honc, D. (2021). One Step Deep Learning Approach to Grasp Detection in Robotics. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Intelligent Systems. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 231. Springer, Cham. https://doi.org/10.1007/978-3-030-90321-3_2
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DOI: https://doi.org/10.1007/978-3-030-90321-3_2
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