Abstract
This article discusses vision systems based on neural networks to optimize various processes in robotics. To perform aliquotation tasks, it is necessary to determine the volume of liquid available for sampling from a test tube. This problem can be solved by using vision systems. To determine the coordinates along which the delta manipulator should move, it was proposed to use a vision system based on the YOLO neural network. The algorithm, as well as the structure of the YOLO neural network for the operation of the control system of a robotic complex for aliquoting is considered. Conducting this study allowed us to choose the most appropriate version of the neural network for subsequent work.
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Acknowledgements
This work was supported by the state assignment of Ministry of Science and Higher Education of the Russian Federation under Grant FZWN-2020-0017.
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Rybak, L.A., Cherkasov, V.V., Malyshev, D.I., Carbone, G. (2023). Blood Serum Recognition Method for Robotic Aliquoting Using Different Versions of the YOLO Neural Network. In: Petrič, T., Ude, A., Žlajpah, L. (eds) Advances in Service and Industrial Robotics. RAAD 2023. Mechanisms and Machine Science, vol 135. Springer, Cham. https://doi.org/10.1007/978-3-031-32606-6_18
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DOI: https://doi.org/10.1007/978-3-031-32606-6_18
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