Output Regulation Based on Object Detection and High Gain Observers in Liquid Handling Robots
DOI:
https://doi.org/10.46842/ipn.cien.v30n1a14Keywords:
liquid handling robot, deep learning, object identification, output regulation, labware recognition, high-gain observerAbstract
Liquid Handling Robots have acquired a significant role in modern laboratories due to their capabilities to automate pipetting and dispensing liquids with high precision and repeatability. Additionally, advances in artificial intelligence have contributed to research areas such as biology or material science, generating sophisticated systems to conduct complex assays. This paper presents an overview of the impact of artificial intelligence techniques and Liquid Handling Robots in applications related to sample preparation, such as cell culturing, microscopy, and in vivo experimentation. In this context, the paper proposes object identification of common labware to support liquid handling tasks. The detection system is based on the ResNet-50 classifier and YOLO v2 detector. The information provided by the detector is used for trajectory planning of a robotic system. To minimize the tracking position error, is proposed a control scheme based on High-Gain observer and Output Regulation, where the trajectories are considered as unmodeled reference signals.
References
[1] J. A. Bennett, M. Abolhasani, "Autonomous chemical science and engineering enabled by self-driving laboratories," Chem. Eng. J., vol. 446, no. 2, Art. no. 137100, pp. 1–10, Oct. 2022. doi: https://doi.org/10.1016/j.coche.2022.100831
[2] F. Häse, L. M. Roch, A. Aspuru-Guzik, "Next-generation experimentation with self-driving laboratories," Trends Chem., vol. 1, no. 3, pp. 282–291, Jun. 2019. doi: https://doi.org/10.1016/j.trechm.2019.02.007
[3] M. J. Tamasi, A. J. Gormley, "Biologic formulation in a self-driving biomaterials lab," Cell Rep. Phys. Sci., vol. 3, no. 9, Art. no. 101019, pp. 1–17, Sep. 2022. doi: https://doi.org/10.1016/j.xcrp.2022.101041
[4] J. T. Rapp, B. J. Bremer, P. A. Romero, "Self-driving laboratories to autonomously navigate the protein fitness landscape," Nat. Chem. Eng., vol. 1, no. 1, pp. 97–107, Jan. 2024. doi: https://doi.org/10.1038/s44286-023-00002-4
[5] J. Beal, M. Rogers, "Levels of autonomy in synthetic biology engineering," Mol. Syst. Biol., vol. 16, no. 12, Art. no. e10014, pp. 1–5, Dec. 2020. doi: https://doi.org/10.15252/msb.202010019
[6] R. D. King, et al., "The robot scientist Adam," Computer, vol. 42, no. 8, pp. 46–54, Aug. 2009. doi: https://doi.org/10.1109/MC.2009.270
[7] R. Hickman, P. Bannigan, Z. Bao, A. Aspuru-Guzik, C. Allen, "Self-driving laboratories: A paradigm shift in nanomedicine development," Matter, vol. 6, no. 4, pp. 1071–1081, Apr. 2023. doi: https://doi.org/10.1016/j.matt.2023.02.007
[8] K. Williams et al., "Cheaper faster drug development validated by repositioning of drugs against neglected tropical diseases," J. R. Soc. Interface, vol. 12, no. 104, Art. no. 20141289, pp. 1–9, Mar. 2015. doi: https://doi.org/10.1098/rsif.2014.1289
[9] A. Sparkes et al., "Towards robot scientists for autonomous scientific discovery," Autom. Exp., vol. 2, no. 1, Art. no. 1, pp. 1–11, Jan. 2010. doi: https://doi.org/10.1186/1759-4499-2-1
[10] C. E. Anderson, et al., "Automated liquid handling robot for rapid lateral flow assay development," Anal. Bioanal. Chem., vol. 414, no. 8, pp. 2607-2618, Mar. 2022. doi: https://doi.org/10.1007/s00216-022-03897-9
[11] H. Tegally, J. E. San, J. Giandhari, T. de Oliveira, "Unlocking the efficiency of genomics laboratories with robotic liquid handling," BMC Genomics, vol. 21, no. 1, Art. no. 729, pp. 1–15, Oct. 2020. doi: https://doi.org/10.1186/s12864-020-07137-1
[12] G. Becattini, L. S. Mattos, D. Caldwell, "A fully automated system for adherent cells microinjection," IEEE J. Biomed. Health Inform., vol. 18, no. 1, pp. 83–93, Jan. 2014. doi: https://doi.org/10.1109/JBHI.2013.2248161
[13] G. Singh, K. D. Lagoo, A. V. S. S. Nayan Rao, D. N. Badodkar, "Liquid handling robot for DNA extraction," in Machines, Mechanism and Robotics, K. V. Gangadharan, R. M. Chandelf, S. M. Kulkaru, Eds. Singapore: Springer, 2019, pp. 829–838. doi: https://doi.org/10.1007/978-981-10-8597-0_71
[14] M. Zaleski Cox, P. N. Miklas, A. Soler Garzón, V. Hoyos Villegas, "Automating high throughput screening for anthracnose resistance in common bean using allele specific PCR," Plant Methods, vol. 19, no. 1, Art. no. 102, pp. 1–10, Sep. 2023. doi: https://doi.org/10.1186/s13007-023-01071-5
[15] S. Taguchi, Y. Suda, K. Irie, H. Ozaki, "Automation of yeast spot assays using an affordable liquid handling robot," SLAS Technol., vol. 28, no. 2, pp. 55–62, Apr. 2023. doi: https://doi.org/10.1016/j.slast.2022.12.001
[16] L. Tong-Tong, W. Jian-Wei, S. Qian-Nuan, W. Hui-Feng, P. Jian-Zhang, "An automated, fully-integrated nucleic acid analyzer based on microfluidic liquid handling robot technique," Anal. Chim. Acta, vol. 1239, Art. no. 340662, pp. 1–9, Jan. 2023. doi: https://doi.org/10.1016/j.aca.2022.340698
[17] W. Yu et al., "Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a mammalian cell," Nat. Commun., vol. 15, no. 1, Art. no. 1279, pp. 1–14, Feb. 2024. doi: https://doi.org/10.1038/s41467-024-45659-4
[18] M. Alexovič, Y. Dotsikas, P. Bober, J. Sabo, "Achievements in robotic automation of solvent extraction and related approaches for bioanalysis of pharmaceuticals," J. Chromatogr. B, vol. 1092, pp. 402–421, Aug. 2018. doi: https://doi.org/10.1016/j.jchromb.2018.06.037
[19] R. P. Wierenga, S. M. Golas, W. Ho, C. W. Coley, K. M. Esvelt, "PyLabRobot: An open-source, hardware-agnostic interface for liquid-handling robots and accessories," Device, vol. 1, no. 4, Art. no. 100111, pp. 1–13, Oct. 2023. doi: https://doi.org/10.1016/j.device.2023.100111
[20] Y. Xiong, V. Shapaval, A. Kohler, J. Li, P. J. From, "A fully automated robot for the preparation of fungal samples for FTIR spectroscopy using deep learning," IEEE Access, vol. 7, pp. 132763–132774, Sep. 2019. doi: https://doi.org/10.1109/ACCESS.2019.2941704
[21] J. Zhang, W. Wan, N. Tanaka, M. Fujita, K. Takahashi, K. Harada, "Integrating a pipette into a robot manipulator with uncalibrated vision and TCP for liquid handling," IEEE Trans. Autom. Sci. Eng., vol. 21, no. 4, pp. 6051–6070, Oct. 2024. doi: https://doi.org/10.1109/TASE.2023.3312657
[22] P. Sardana et al., "DropletAI: Deep learning-based classification of fluids with different Ohnesorge numbers during non-contact dispensing," Fluids, vol. 8, no. 6, Art. no. 183, pp. 1–15, Jun. 2023. doi: https://doi.org/10.3390/fluids8060183
[23] E. Whitehead, F. Rudolf, H. M. Kaltenbach, J. Stelling, "Automated planning enables complex protocols on liquid-handling robots," ACS Synth. Biol., vol. 7, no. 3, pp. 922–932, Mar. 2018. doi: https://doi.org/10.1021/acssynbio.8b00021
[24] G. N. Kanda et al., "Robotic search for optimal cell culture in regenerative medicine," eLife, vol. 11, Art. no. e77007, pp. 1–25, Jun. 2022. doi: https://doi.org/10.7554/elife.77007
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Copyright (c) 2026 Ricardo Tapia Herrera, Tonatiuh Hernández Cortes, Beatriz Adriana Jaime Fonseca, Jesús Alberto Meda Campaña (Autor/a)
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