Active guidance of a handheld micromanipulator using visual servoing

  • Authors:
  • Brian C. Becker;Sandrine Voros;Robert A. MacLachlan;Gregory D. Hager;Cameron N. Riviere

  • Affiliations:
  • Robotics Institute, Carnegie Mellon University, Pittsburgh, PA;Computer Science Department, Johns Hopkins University, Baltimore, Maryland;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA;Computer Science Department, Johns Hopkins University, Baltimore, Maryland;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
  • Year:
  • 2009

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Abstract

In microsurgery, a surgeon often deals with anatomical structures of sizes that are close to the limit of the human hand accuracy. Robotic assistants can help to push beyond the current state of practice by integrating imaging and robot-assisted tools. This paper demonstrates control of a handheld tremor reduction micromanipulator with visual servo techniques, aiding the operator by providing three behaviors: snap-to, motion-scaling, and standoff-regulation. A stereo camera setup viewing the workspace under high magnification tracks the tip of the micromanipulator and the desired target object being manipulated. Individual behaviors activate in task-specific situations when the micromanipulator tip is in the vicinity of the target. We show that the snap-to behavior can reach and maintain a position at a target with an accuracy of 17.5 ± 0.4µm Root Mean Squared Error (RMSE) distance between the tip and target. Scaling the operator's motions and preventing unwanted contact with non-target objects also provides a larger margin of safety.