State estimation and feedforward tremor suppression for a handheld micromanipulator with a Kalman filter

Brian Becker, Robert MacLachlan, and Cameron Riviere
Proc. IEEE International Conference on Intelligent Robots and Systems, September, 2011, pp. 5160-5165.


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Abstract
Active compensation of physiological tremor for handheld micromanipulators depends on fast control and actuation responses. Because of real-world latencies, real-time compensation is usually not completely effective at eliminating unwanted hand motion. By modeling tremor, more effective cancellation is possible by anticipating future hand motion. We propose a feedforward control strategy that utilizes tremor velocity from a state-estimating Kalman filter. We demonstrate that estimating hand motion in a feedforward controller overcomes real-world latencies in micromanipulator actuation. In hold-still tasks with a fully handheld micromanipulator, the proposed feedforward approach improves tremor rejection by over 50%.

Keywords
medical robotics, micromanipulator, Kalman filter

Notes
Associated Center(s) / Consortia: Medical Robotics Technology Center
Associated Lab(s) / Group(s): Surgical Mechatronics Laboratory
Associated Project(s): Micron: Intelligent Microsurgical Instruments and ASAP
Number of pages: 6

Text Reference
Brian Becker, Robert MacLachlan, and Cameron Riviere, "State estimation and feedforward tremor suppression for a handheld micromanipulator with a Kalman filter," Proc. IEEE International Conference on Intelligent Robots and Systems, September, 2011, pp. 5160-5165.

BibTeX Reference
@inproceedings{Becker_2011_6928,
   author = "Brian Becker and Robert MacLachlan and Cameron Riviere",
   title = "State estimation and feedforward tremor suppression for a handheld micromanipulator with a Kalman filter",
   booktitle = "Proc. IEEE International Conference on Intelligent Robots and Systems",
   pages = "5160-5165",
   month = "September",
   year = "2011",
}