Effects of master-slave tool misalignment in a teleoperated surgical robot

In a teleoperated system, misalignment between the master and slave manipulators can result from clutching, errors in the kinematic model, and/or sensor errors. This study examines the effects of type and magnitude of misalignment on the performance of the teleoperator. We first characterized the magnitude and direction of orientation misalignment created when clutching and unclutching during use of two surgical robots: the Raven II and the da Vinci Research Kit. We then purposely generated typical misalignments in order to measure the impact of such misalignment on user performance of a peg transfer task with the Raven II. Users were able to compensate for misalignment angles up to approximately 20 degrees in both tool orientation and camera viewpoint misalignment. These results can be used to guide the design and control of teleoperated systems for a variety of applications.

[1]  Juan Antonio Cuesta-Albertos,et al.  On projection-based tests for directional and compositional data , 2009, Stat. Comput..

[2]  Jessie Y. C. Chen,et al.  Human Performance Issues and User Interface Design for Teleoperated Robots , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  Blake Hannaford,et al.  Raven Surgical Robot Training in Preparation for da Vinci® Use: A Randomized Prospective Trial , 2014, MMVR.

[4]  Pieter Abbeel,et al.  Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).

[5]  Terrence Fong,et al.  Multi-robot remote driving with collaborative control , 2003, IEEE Trans. Ind. Electron..

[6]  Robin R. Murphy,et al.  Human-robot interaction in rescue robotics , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  R. A. Bailey Designing Experiments and Analyzing Data: a Model Comparison Perspective, 2nd edn , 2005 .

[8]  Russell H. Taylor,et al.  Medical robotics in computer-integrated surgery , 2003, IEEE Trans. Robotics Autom..

[9]  Blake Hannaford,et al.  Raven-II: An Open Platform for Surgical Robotics Research , 2013, IEEE Transactions on Biomedical Engineering.

[10]  Blake Hannaford,et al.  Robustness of the Unscented Kalman filter for state and parameter estimation in an elastic transmission , 2009, Robotics: Science and Systems.

[11]  G. Fried,et al.  Development of a model for training and evaluation of laparoscopic skills. , 1998, American journal of surgery.

[12]  Michael A. Stephens,et al.  The Testing of Unit Vectors for Randomness , 1964 .

[13]  Bernard D. Adelstein,et al.  Human Control in Rotated Frames: Anisotropies in the Misalignment Disturbance Function of Pitch, Roll, and Yaw , 2012 .

[14]  Thomas B. Sheridan,et al.  Telerobotics , 1989, Autom..

[15]  Scott E. Maxwell,et al.  Designing Experiments and Analyzing Data: A Model Comparison Perspective , 1990 .

[16]  Yan Yan,et al.  Evaluation of laparoscopic performance with alteration in angle of vision. , 2006, Journal of endourology.

[17]  S. Glantz,et al.  Primer of Applied Regression & Analysis of Variance , 1990 .

[18]  S. Glantz Primer of applied regression and analysis of variance / Stanton A. Glantz, Bryan K. Slinker , 1990 .

[19]  Allison M. Okamura,et al.  Effect of load force feedback on grip force control during teleoperation: A preliminary study , 2014, 2014 IEEE Haptics Symposium (HAPTICS).