Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Efficient Model Learning and Adaptive Tracking Control of Magnetic Micro-Robots for Non-Contact Manipulation (2403.14414v1)

Published 21 Mar 2024 in cs.RO

Abstract: Magnetic microrobots can be navigated by an external magnetic field to autonomously move within living organisms with complex and unstructured environments. Potential applications include drug delivery, diagnostics, and therapeutic interventions. Existing techniques commonly impart magnetic properties to the target object,or drive the robot to contact and then manipulate the object, both probably inducing physical damage. This paper considers a non-contact formulation, where the robot spins to generate a repulsive field to push the object without physical contact. Under such a formulation, the main challenge is that the motion model between the input of the magnetic field and the output velocity of the target object is commonly unknown and difficult to analyze. To deal with it, this paper proposes a data-driven-based solution. A neural network is constructed to efficiently estimate the motion model. Then, an approximate model-based optimal control scheme is developed to push the object to track a time-varying trajectory, maintaining the non-contact with distance constraints. Furthermore, a straightforward planner is introduced to assess the adaptability of non-contact manipulation in a cluttered unstructured environment. Experimental results are presented to show the tracking and navigation performance of the proposed scheme.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (35)
  1. J. Rufo, F. Cai, J. Friend, M. Wiklund, and T. J. Huang, “Acoustofluidics for biomedical applications,” Nature Reviews Methods Primers, vol. 2, no. 1, p. 30, 2022.
  2. X. Wang, C. Ho, Y. Tsatskis, J. Law, Z. Zhang, M. Zhu, C. Dai, F. Wang, M. Tan, S. Hopyan, et al., “Intracellular manipulation and measurement with multipole magnetic tweezers,” Science robotics, vol. 4, no. 28, p. eaav6180, 2019.
  3. C. Dai, Z. Zhang, Y. Lu, G. Shan, X. Wang, Q. Zhao, C. Ru, and Y. Sun, “Robotic manipulation of deformable cells for orientation control,” IEEE Transactions on Robotics, vol. 36, no. 1, pp. 271–283, 2019.
  4. L. Ren, N. Nama, J. M. McNeill, F. Soto, Z. Yan, W. Liu, W. Wang, J. Wang, and T. E. Mallouk, “3d steerable, acoustically powered microswimmers for single-particle manipulation,” Science advances, vol. 5, no. 10, p. eaax3084, 2019.
  5. Y. Ren, Q. Chen, M. He, X. Zhang, H. Qi, and Y. Yan, “Plasmonic optical tweezers for particle manipulation: principles, methods, and applications,” ACS nano, vol. 15, no. 4, pp. 6105–6128, 2021.
  6. Z. Zhang, J. Liu, X. Wang, Q. Zhao, C. Zhou, M. Tan, H. Pu, S. Xie, and Y. Sun, “Robotic pick-and-place of multiple embryos for vitrification,” IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 570–576, 2016.
  7. S. Miao, J. Xu, Z. Jiang, J. Luo, X. Sun, X. Jiang, H. Wei, and Y.-H. Liu, “Microfluidics-enabled robotic system for embryo vitrification with real-time observation: Design, method, and evaluation,” IEEE/ASME Transactions on Mechatronics, 2023.
  8. D. Fan, F. Zhu, R. Cammarata, and C. Chien, “Electric tweezers,” Nano Today, vol. 6, no. 4, pp. 339–354, 2011.
  9. X. Li, C. C. Cheah, S. Hu, and D. Sun, “Dynamic trapping and manipulation of biological cells with optical tweezers,” Automatica, vol. 49, no. 6, pp. 1614–1625, 2013.
  10. M. P. Kummer, J. J. Abbott, B. E. Kratochvil, R. Borer, A. Sengul, and B. J. Nelson, “Octomag: An electromagnetic system for 5-dof wireless micromanipulation,” IEEE Transactions on Robotics, vol. 26, no. 6, pp. 1006–1017, 2010.
  11. T. Xu, C. Huang, Z. Lai, and X. Wu, “Independent control strategy of multiple magnetic flexible millirobots for position control and path following,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 2875–2887, 2022.
  12. L. Yang, Z. Yang, M. Zhang, J. Jiang, H. Yang, and L. Zhang, “Optimal parameter design and microrobotic navigation control of parallel-mobile-coil systems,” IEEE Transactions on Automation Science and Engineering, 2022.
  13. X. Du and J. Yu, “Image-integrated magnetic actuation systems for localization and remote actuation of medical miniature robots: A survey,” IEEE Transactions on Robotics, vol. 39, no. 4, pp. 2549–2568, 2023.
  14. Q. Wang, L. Yang, J. Yu, C.-I. Vong, P. W. Y. Chiu, and L. Zhang, “Magnetic navigation of a rotating colloidal swarm using ultrasound images,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2018, pp. 5380–5385.
  15. J. Li, H. Wang, Q. Shi, Z. Zheng, J. Cui, T. Sun, P. Ferraro, Q. Huang, and T. Fukuda, “Biped walking of magnetic microrobot in oscillating field for indirect manipulation of non-magnetic objects,” IEEE Transactions on Nanotechnology, vol. 19, pp. 21–24, 2019.
  16. F. Ruggiero, V. Lippiello, and B. Siciliano, “Nonprehensile dynamic manipulation: A survey,” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 1711–1718, 2018.
  17. J. Moura, T. Stouraitis, and S. Vijayakumar, “Non-prehensile planar manipulation via trajectory optimization with complementarity constraints,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 970–976.
  18. Y. Jiang, Y. Jia, and X. Li, “Contact-aware non-prehensile manipulation for object retrieval in cluttered environments,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, pp. 10 604–10 611.
  19. W. Chen, H. Zhou, B. Zhang, Q. Cao, B. Wang, and X. Ma, “Recent progress of micro/nanorobots for cell delivery and manipulation,” Advanced Functional Materials, vol. 32, no. 18, p. 2110625, 2022.
  20. S. Floyd, C. Pawashe, and M. Sitti, “Two-dimensional contact and noncontact micromanipulation in liquid using an untethered mobile magnetic microrobot,” IEEE Transactions on Robotics, vol. 25, no. 6, pp. 1332–1342, 2009.
  21. C. Pawashe, S. Floyd, E. Diller, and M. Sitti, “Two-dimensional autonomous microparticle manipulation strategies for magnetic microrobots in fluidic environments,” IEEE Transactions on Robotics, vol. 28, no. 2, pp. 467–477, 2011.
  22. R. Pieters, H.-W. Tung, S. Charreyron, D. F. Sargent, and B. J. Nelson, “Rodbot: A rolling microrobot for micromanipulation,” in 2015 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2015, pp. 4042–4047.
  23. T. Petit, L. Zhang, K. E. Peyer, B. E. Kratochvil, and B. J. Nelson, “Selective trapping and manipulation of microscale objects using mobile microvortices,” Nano letters, vol. 12, no. 1, pp. 156–160, 2012.
  24. Z. Ye and M. Sitti, “Dynamic trapping and two-dimensional transport of swimming microorganisms using a rotating magnetic microrobot,” Lab on a Chip, vol. 14, no. 13, pp. 2177–2182, 2014.
  25. Z. Lin, X. Fan, M. Sun, C. Gao, Q. He, and H. Xie, “Magnetically actuated peanut colloid motors for cell manipulation and patterning,” ACS nano, vol. 12, no. 3, pp. 2539–2545, 2018.
  26. I. S. Khalil, A. Klingner, Y. Hamed, Y. S. Hassan, and S. Misra, “Controlled noncontact manipulation of nonmagnetic untethered microbeads orbiting two-tailed soft microrobot,” IEEE transactions on robotics, vol. 36, no. 4, pp. 1320–1332, 2020.
  27. Y. Dai, L. Jia, L. Wang, H. Sun, Y. Ji, C. Wang, L. Song, S. Liang, D. Chen, Y. Feng, et al., “Magnetically actuated cell-robot system: Precise control, manipulation, and multimode conversion,” Small, vol. 18, no. 15, p. 2105414, 2022.
  28. I. Sinn, P. Kinnunen, S. N. Pei, R. Clarke, B. H. McNaughton, and R. Kopelman, “Magnetically uniform and tunable janus particles,” Applied Physics Letters, vol. 98, no. 2, 2011.
  29. T. Xu, Z. Hao, C. Huang, J. Yu, L. Zhang, and X. Wu, “Multimodal locomotion control of needle-like microrobots assembled by ferromagnetic nanoparticles,” IEEE/ASME Transactions on Mechatronics, vol. 27, no. 6, pp. 4327–4338, 2022.
  30. E. Bertrand, T. D. Blake, and J. De Coninck, “Influence of solid–liquid interactions on dynamic wetting: a molecular dynamics study,” Journal of Physics: Condensed Matter, vol. 21, no. 46, p. 464124, 2009.
  31. E. J. Dickinson, H. Ekström, and E. Fontes, “Comsol multiphysics®: Finite element software for electrochemical analysis. a mini-review,” Electrochemistry communications, vol. 40, pp. 71–74, 2014.
  32. H. Yu, T. Xie, S. Paszczynski, and B. M. Wilamowski, “Advantages of radial basis function networks for dynamic system design,” IEEE Transactions on Industrial Electronics, vol. 58, no. 12, pp. 5438–5450, 2011.
  33. M. Yu, K. Lv, H. Zhong, S. Song, and X. Li, “Global model learning for large deformation control of elastic deformable linear objects: An efficient and adaptive approach,” IEEE Transactions on Robotics, vol. 39, no. 1, pp. 417–436, 2023.
  34. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
  35. G. Bradski, “The opencv library.” Dr. Dobb’s Journal: Software Tools for the Professional Programmer, vol. 25, no. 11, pp. 120–123, 2000.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now