Abstract
In the three years since the COVID-19 disease crossed, nasopharyngeal(NP) swab sampling and reverse-transcription Polymerase Chain Reaction (RT-PCR) test techniques have proven to be very effective and reliable tests for the early detection of infected individuals. However, this highly reliable RT-PCR testing is difficult to operate and is the only one of the conventional sampling methods that must be operated by a dedicated swab and trained medical personnel, unlike several other nasopharyngeal swabs, nasal swabs, which can be easily operated by ordinary people for sampling. This paper presents a robot that operates nasopharyngeal swabs for RT-PCR sampling. In order to mimic the difficult sampling operation process of medical professionals and to accurately and stably reach the sampling position deep in the posterior wall of the nasal cavity, this robotic system uses an advanced 3D vision and force sensing hybrid perception system to accurately locate the nostril position, feed the swab into the nostril and then carefully and carefully advance the swab along the inferior nasal tract to the designated sampling position in the posterior nasal cavity through Force and Torque (F/T) sensors. The swab is withdrawn after the sampling operation.
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Acknowledgements
This work is supported by the Shenzhen Science and Technology Program (Project No. JSGGKQTD20221101115656029 and JSGG20220606142803007) and the Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS). We thank Dr. Xizhu Sun, Dr. Jinzhou Qin of Luohu Hospital for their support. We thank Xiran Pan, Rufei Cai, Hao Sun, Chengjie Huang, Xiaoyi Liu, Quan Liu and Wei Liu from Moying Robotics for their help during hardware development. Min Ye and Hongyu Zhao from AIRS helped improve the writing.
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Zhang, T., Zheng, S., Liu, C., Yang, Y., Sun, Z., Lam, T.l. (2023). A Dual-Arm Nasopharyngeal Swab Manipulation Robot for Polymerization Chain Reaction Sampling. In: Okada, M. (eds) Advances in Mechanism and Machine Science. IFToMM WC 2023. Mechanisms and Machine Science, vol 148. Springer, Cham. https://doi.org/10.1007/978-3-031-45770-8_16
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DOI: https://doi.org/10.1007/978-3-031-45770-8_16
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