JRM Vol.20 No.1 pp. 151-158
doi: 10.20965/jrm.2008.p0151


Robot Manipulation and Guidance Using Magnetic Motion Capture Sensor and a Rule-Based Controller

Carlos R. Tercero Villagran*1,*2, Seiichi Ikeda*1,
Toshio Fukuda*1, Kosuke Sekiyama*1, Yuta Okada*1,
Tomomi Uchiyama*3,Makoto Negoro*4, and Ikuo Takahashi*5

*1Micro-Nano Systems Engineering Department, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

*2Electronics Engineering Department, Del Valle de Guatemala University

*3Complex System Science Department, Nagoya University, 1 Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan

*4Department of Neurosurgery, Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi 470-1192, Japan

*5Department of Neurosurgery, Anjo Kosei Hospital, 28 Higashi Hirokute, Anjo-cho, Anjo, Aichi 446-8602, Japan

March 27, 2007
December 11, 2007
February 20, 2008
magnetic trackers, robot manipulation, robot guidance, path planning, path reconstruction

Magnetic motion capture sensors (MMCS) are not commonly used for robot control due to the need for complex, resource-consuming calibration to correct error introduced by the magnetic sensor. We propose avoiding such calibration using a rule-based controller that only uses spatial coordinates from the magnetic sensor. This controller uses a sparse look-up table of spatial coordinates and actions conducted by the robot and reacts to the presence of the sensor near reference points. The control method was applied to manipulate a robotic camera to track a catheter-shaped sensor inside vessels silicone models. A second evaluation was done guiding a mechanism to reconstruct catheter insertion in major silicone vasculature models. The robotic camera tracked the catheter by reacting to the sensor within 10 mm of each reference point. The catheter insertion mechanism reconstructed the catheter trajectory by reacting to the sensor within 6 mm of each reference point. We found that the proposed method allowed robot control in a bounded space without having to correct for the magnetic tracker output distortion.

Cite this article as:
Carlos R. Tercero Villagran, Seiichi Ikeda,
Toshio Fukuda, Kosuke Sekiyama, Yuta Okada,
Tomomi Uchiyama, Makoto Negoro, and Ikuo Takahashi, “Robot Manipulation and Guidance Using Magnetic Motion Capture Sensor and a Rule-Based Controller,” J. Robot. Mechatron., Vol.20, No.1, pp. 151-158, 2008.
Data files:
  1. [1] G. A. Krombach, A. Mahnken, J. Tacke, G. Staatz, S. Haller, C. C. A. Nolte-Ernsting, J. Meyer, P. Haage, and R. W. Günther “US-guided nephrostomy with the aid of a magnetic field-based navigation device in the porcine pelvicaliceal system,” J. Vascular Interventional Radiology, Vol.12, pp. 623-628, 2001.
  2. [2] A. Chung, P. Edwards, F. Deligianni, and G. Yang, “Freehand Cocalibration of Optical and Electromagnetic Trackers for Navigated Bronchoscopy,” The Second Int.Workshop on Medical Imaging and Augmented Reality (MIAR 2004), Beijing, China, 2004.
  3. [3] K. Nakada, M. Nakamoto, Y. Sato, K. Konishi, M. Hashizume, and S. Tamura, “A rapid method for magnetic tracker calibration using a magneto-optic hybrid tracker,” MICCAI 2003, Lecture Notes in Computer Science (2879), Springer-Verlag, pp. 285-293, 2003.
  4. [4] M. Livingstone and A. State, “Magnetic tracker for improved augmented reality registration,” PESCENCE: Teleoperators and Virtual Environments, Vol.6, No.5, pp. 532-546, 1997.
  5. [5] M. Ikits, J. Brederson, C. Hansen, and J. Hollerbach, “An improved calibration framework for electromagnetic tracking devices,” Proc. of the IEEE Virtual Reality, pp. 63-70, 2000.
  6. [6] V. Kindratenko and W. Sherman, “Neural Network Based calibration of electromagnetic tracking systems,” Virtual Reality, Vol.9, pp. 78-78, 2006.
  7. [7] V. Kindratenko and A. Bennett, “A survey of electromagnetic position tracker calibration techniques,” Virtual Environments 2000, Proc. of the Eurographics Workshop, Springer Computer Science Series, Springer-Verlag, Berlin, Germany, Vol.5, No3, pp. 169-182, 2000.
  8. [8] S. Ikeda, F. Arai, T. Fukuda, M. Negoro, and K. Irie, “An in vitro patient-specific biological model of the cerebral artery reproduced with a membranous configuration for simulating endovascular intervention,” Journal of Robotics and Mechatronics, Vol.17, No.3, pp. 327-33, 2005.
  9. [9] P. Turski, M. Steighorst, C. Strother, A. Crummy, R. Leiberman, and C. Mistretta, “Digital Subtraction Angiography “Road Map”,” Technical Notes, AJR139, pp. 1233-1234, 1982.
  10. [10] F. Arai, R. Fujimura, T. Fukuda, and M. Negoro, “New catheter driving method using linear stepping mechanism for intravascular neurosurgery,” Proc. of the IEEE ICRA, pp. 2944-2949, Washington, DC, USA, 2002.
  11. [11] C. Tercero et al., “Autonomous Catheter Insertion System using Magnetic Motion Capture Sensor for endovascular surgery,” Int. Journal of Medical Robotics and Computer Assisted Surgery, Vol.3, pp. 52-58, 2007.
  12. [12] M. Tanimoto, F. Arai, T. Fukuda, H. Iwata, K. Itoigawa, Y. Gotoh, M. Hashimoto, and M. Negoro, “Study on Micro Force Sensor for Minimum Invasive Surgery,” Transactions of the Japan Society of Mechanical Engineers, Vol.64 No.620-C, pp. 150-155.
  13. [13] S. Ikeda, F. Arai, F. Fukuda, M. Negoro, K. Irie, and N. Takahashi, “An In-Vitro Patient-Tailored Model of Human Cerebral Artery for Simulating Endovascular Surgery Intervention,” MICCAI 2005, Lecture Notes in Computer Science (3749), Springer-Verlag, pp. 925-932, 2005.
  14. [14] C. Tercero, T. Uchiyama, T. Fukuda, S. Ikeda, and Y. Ono, “Realtime vessel 3D modeling for navigation in laparoscopic surgery,” Proc. of the 14th Congress of the Japanese Society of Computer Assisted Surgery, pp. 75-76, Chiba, Japan, Nov. 19-21, 2005.

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