Dynamic Electromagnetic Navigation
Abstract: Magnetic navigation offers wireless control over magnetic objects, which has important medical applications, such as targeted drug delivery and minimally invasive surgery. Magnetic navigation systems are categorized into systems using permanent magnets and systems based on electromagnets. Electromagnetic Navigation Systems (eMNSs) are believed to have a superior actuation bandwidth, facilitating trajectory tracking and disturbance rejection. This greatly expands the range of potential medical applications and includes even dynamic environments as encountered in cardiovascular interventions. To showcase the dynamic capabilities of eMNSs, we successfully stabilize a (non-magnetic) inverted pendulum on the tip of a magnetically driven arm. Our approach employs a model-based framework that leverages Lagrangian mechanics to capture the interaction between the mechanical dynamics and the magnetic field. Using system identification, we estimate unknown parameters, the actuation bandwidth, and characterize the system's nonlinearity. To explore the limits of electromagnetic navigation and evaluate its scalability, we characterize the electrical system dynamics and perform reference measurements on a clinical-scale eMNS, affirming that the proposed dynamic control methodologies effectively translate to larger coil configurations. A state-feedback controller stabilizes the inherently unstable pendulum, and an iterative learning control scheme enables accurate tracking of non-equilibrium trajectories. Furthermore, to understand structural limitations of our control strategy, we analyze the influence of magnetic field gradients on the motion of the system. To our knowledge, this is the first demonstration to stabilize a 3D inverted pendulum through electromagnetic navigation.
- J. J. Abbott, E. Diller, and A. J. Petruska, “Magnetic methods in robotics,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 3, no. 1, 2020.
- Y. Kim, E. Genevriere, P. Harker, J. Choe, M. Balicki, R. W. Regenhardt, J. E. Vranic, A. A. Dmytriw, A. B. Patel, and X. Zhao, “Telerobotic neurovascular interventions with magnetic manipulation,” Science Robotics, vol. 7, no. 65, 2022.
- J. Hwang, J.-y. Kim, and H. Choi, “A review of magnetic actuation systems and magnetically actuated guidewire-and catheter-based microrobots for vascular interventions,” Intelligent Service Robotics, vol. 13, 2020.
- J. Li, B. E.-F. de Ávila, W. Gao, L. Zhang, and J. Wang, “Micro/nanorobots for biomedicine: Delivery, surgery, sensing, and detoxification,” Science Robotics, vol. 2, no. 4, 2017.
- Y. Kim, G. A. Parada, S. Liu, and X. Zhao, “Ferromagnetic soft continuum robots,” Science Robotics, vol. 4, no. 33, 2019.
- D. V. Kladko and V. V. Vinogradov, “Magnetosurgery: Principles, design, and applications,” Smart Materials in Medicine, vol. 5, no. 1, 2023.
- Z. Yang and L. Zhang, “Magnetic actuation systems for miniature robots: A review,” Advanced Intelligent Systems, vol. 2, no. 9, 2020.
- G. Ciuti, P. Valdastri, A. Menciassi, and P. Dario, “Robotic magnetic steering and locomotion of capsule endoscope for diagnostic and surgical endoluminal procedures,” Robotica, vol. 28, no. 2, 2010.
- S. B. Kesner and R. D. Howe, “Position control of motion compensation cardiac catheters,” IEEE Transactions on Robotics, vol. 27, no. 6, 2011.
- T. Glück, A. Eder, and A. Kugi, “Swing-up control of a triple pendulum on a cart with experimental validation,” Automatica, vol. 49, no. 3, 2013.
- K. Åström and K. Furuta, “Swinging up a pendulum by energy control,” Automatica, vol. 36, no. 2, 2000.
- M. Muehlebach and R. D’Andrea, “Nonlinear analysis and control of a reaction-wheel-based 3-D inverted pendulum,” IEEE Transactions on Control Systems Technology, vol. 25, no. 1, 2017.
- O. Boubaker, “The inverted pendulum: A fundamental benchmark in control theory and robotics,” in Proceedings of the International Conference on Education and e-Learning Innovations, 2012.
- 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, 2010.
- A. J. Petruska and B. J. Nelson, “Minimum bounds on the number of electromagnets required for remote magnetic manipulation,” IEEE Transactions on Robotics, vol. 31, no. 3, 2015.
- J. Edelmann, A. J. Petruska, and B. J. Nelson, “Magnetic control of continuum devices,” The International Journal of Robotics Research, vol. 36, no. 1, 2017.
- T. Liu and M. C. Çavuşoğlu, “Three dimensional modeling of an MRI actuated steerable catheter system,” in Proceedings of the International Conference on Robotics and Automation, 2014.
- G. Pittiglio, L. Barducci, J. W. Martin, J. C. Norton, C. A. Avizzano, K. L. Obstein, and P. Valdastri, “Magnetic levitation for soft-tethered capsule colonoscopy actuated with a single permanent magnet: A dynamic control approach,” IEEE Robotics and Automation Letters, vol. 4, no. 2, 2019.
- B. Scaglioni, L. Previtera, J. Martin, J. Norton, K. L. Obstein, and P. Valdastri, “Explicit model predictive control of a magnetic flexible endoscope,” IEEE Robotics and Automation Letters, vol. 4, no. 2, 2019.
- E. Erdem Tuna, T. Liu, R. C. Jackson, N. Lombard Poirot, M. Russell, and M. C. Çavuşoğlu, “Analysis of dynamic response of an MRI-guided magnetically-actuated steerable catheter system,” 2018.
- H. Marino, C. Bergeles, and B. J. Nelson, “Robust electromagnetic control of microrobots under force and localization uncertainties,” IEEE Transactions on Automation Science and Engineering, vol. 11, no. 1, 2014.
- C. Pawashe, S. Floyd, and M. Sitti, “Modeling and experimental characterization of an untethered magnetic micro-robot,” The International Journal of Robotics Research, vol. 28, no. 8, 2009.
- M. Hehn and R. D’Andrea, “An iterative learning scheme for high performance, periodic quadrocopter trajectories,” in Proceedings of the European Control Conference, 2013.
- C. Sferrazza, M. Muehlebach, and R. D’Andrea, “Learning-based parametrized model predictive control for trajectory tracking,” Optimal Control Applications and Methods, vol. 41, no. 6, 2020.
- R.-E. Precup, C. Gavriluta, M.-B. Radac, S. Preitl, C.-A. Dragos, J. Tar, and E. Petriu, “Iterative learning control experimental results for inverted pendulum crane mode control,” in Proceedings of the International Symposium on Intelligent Systems and Informatics, 2009.
- Q. Boehler, S. Gervasoni, S. L. Charreyron, C. Chautems, and B. J. Nelson, “On the workspace of electromagnetic navigation systems,” IEEE Transactions on Robotics, vol. 39, no. 1, 2023.
- H. Ma, D. Büchler, B. Schölkopf, and M. Muehlebach, “A learning-based iterative control framework for controlling a robot arm with pneumatic artificial muscles,” in Proceedings of Robotics: Science and Systems XVIII, 2022.
- M. Hofer, M. Muehlebach, and R. D’Andrea, “The one-wheel Cubli: A 3D inverted pendulum that can balance with a single reaction wheel,” Mechatronics, vol. 91, 2023.
- D. Bristow, M. Tharayil, and A. Alleyne, “A survey of iterative learning control,” IEEE Control Systems Magazine, vol. 26, no. 3, 2006.
- J. Zughaibi, M. Hofer, and R. D’Andrea, “A fast and reliable pick-and-place application with a spherical soft robotic arm,” in Proceedings of the International Conference on Soft Robotics, 2021.
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