Intelligent Sampling of Anterior Human Nasal Swabs using a Collaborative Robotic Arm

  • Roman Parak Institute of Automation and Computer Science, Brno University of Technology, Czech Republic
  • Martin Juricek Institute of Automation and Computer Science, Brno University of Technology, Czech Republic
Keywords: Robotics, Smart Hospital, Convolution Neural Network (CNN), U-Net, ASPOCRNet, Robot Operating System (ROS)


Advanced robotics does not always have to be associated with Industry 4.0, but can also be applied, for example, in the Smart Hospital concept. Developments in this field have been driven by the coronavirus disease (COVID-19), and any improvement in the work of medical staff is welcome. In this paper, an experimental robotic platform was designed and implemented whose main function is the swabbing samples from the nasal vestibule. The robotic platform represents a complete integration of software and hardware, where the operator has access to a web-based application and can control a number of functions. The increased safety and collaborative approach cannot be overlooked. The result of this work is a functional prototype of the robotic platform that can be further extended, for example, by using alternative technologies, extending patient safety, or clinical tests and studies. Code is available at


Aguero, C., Koenig, N., Chen, I., Boyer, H., Peters, S., Hsu, J., Gerkey, B., Paepcke, S., Rivero, J., Manzo, J., Krotkov, E., and Pratt, G. Inside the virtual robotics challenge: Simulating real-time robotic disaster response. Automation Science and Engineering, IEEE Transactions on 12, 2 (April 2015), 494–506.

Andersen, T. T. Optimizing the universal robots ros driver. Tech. rep., Technical University of Denmark, Department of Electrical Engineering, 2015.

Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Computer Vision – ECCV 2018 (Cham, 2018), Springer International Publishing, pp. 833–851.

Crowe, S. Danish startup develops throat swabbing robot for covid-19 testing. Online, 2020.

Goodfellow, I., Bengio, Y., and Courville, A. Deep Learning. MIT Press, 2016.

Hu, Y., Li, J., Chen, Y., Wang, Q., Chi, C., Zhang, H., Gao, Q., Lan, Y., Li, Z., Mu, Z., Sun, Z., and Knoll, A. Design and control of a highly redundant rigid-flexible coupling robot to assist the covid-19 oropharyngeal-swab sampling. IEEE Robotics and Automation Letters 7, 2 (2022), 1856–1863.

Intuitive. Da vinci surgical system: User manual, 2021. Online. Available at:

Koenig, N., and Howard, A. Design and use paradigms for gazebo, an open-source multi-robot simulator. In IEEE/RSJ International Conference on Intelligent Robots and Systems (Sendai, Japan, Sep 2004), pp. 2149–2154.

KUKA. Medical robotics, lbr med, 2017. Online. Available at:

Li, C., Gu, X., Xiao, X., Lim, C. M., Duan, X., and Ren, H. A flexible transoral robot towards covid-19 swab sampling. Frontiers in Robotics and AI 8 (2021).

Mcculloch, W., and Pitts, W. A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5 (1943), 127–147.

Navi, B. Brain navi develops new robot performing nasal swab tests to prevent cross infections. Online, 2020.

Parak, R., and Matousek, R. Comparison of multiple reinforcement learning and deep reinforcement learning methods for the task aimed at achieving the goal. MENDEL Journal 27, 1 (June 2021), 1–8.

Parak, R., Matousek, R., and Lacko, B. I4c – roboticka bunka podle konceptu prumyslu 4.0. AUTOMA 27, 1 (January 2021), 10–12.

Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A. Ros: an open-source robot operating system. ICRA Workshop on Open Source Software 3 (03 2009).

Ronneberger, O., Fischer, P., and Brox, T. U-net: Convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015).

Ruzickova, K. The robot from but will save the work of laboratory technicians. it could help with the covid-19 samples at the university hospital brno. Online, 2020.

Seo, J., Shim, S., Park, H., Baek, J., Cho, J. H., and Kim, N.-H. Development of robotassisted untact swab sampling system for upper respiratory disease. Applied Sciences 10, 21 (2020).

David Coleman, Ioan A. Sucan, Sachin Chitta, Nikolaus Correll . Reducing the barrier to entry of complex robotic software: a moveit!case study. Journal of Software Engineering for Robotics 5 (2014), 3–16.

Ioan A. Sucan and Sachin Chitta. Moveit. Online. Available at:

Universal Robots. Ur3. Online. Available at:

Wang, S., Wang, K., Tang, R., Qiao, J., Liu, H., and Hou, Z.-G. Design of a low-cost miniature robot to assist the COVID-19 nasopharyngeal swab sampling. IEEE Transactions on Medical Robotics and Bionics 3, 1 (feb 2021), 289–293.

Yang, L., Cao, Q., Lin, M., Zhang, H., and Ma, Z. Robotic hand-eye calibration with depth camera: A sphere model approach. In 2018 4th International Conference on Control, Automation and Robotics (ICCAR) (2018), pp. 104–110.

Yuan, Y., Chen, X., and Wang, J. Objectcontextual representations for semantic segmentation. CoRR abs/1909.11065 (2019).

Zhou, Q.-Y., Park, J., and Koltun, V. Open3D: A modern library for 3D data processing. arXiv:1801.09847 (2018).

How to Cite
Parak, R. and Juricek, M. 2022. Intelligent Sampling of Anterior Human Nasal Swabs using a Collaborative Robotic Arm. MENDEL. 28, 1 (Jun. 2022), 32-40. DOI:
Research articles