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Research Papers

Force-Controlled Exploration for Updating Virtual Fixture Geometry in Model-Mediated Telemanipulation

[+] Author and Article Information
Long Wang

Advanced Robotics and
Mechanism Applications,
Department of Mechanical Engineering,
Vanderbilt University,
Nashville, TN 37235
e-mail: long.wang@Vanderbilt.edu

Zihan Chen

Laboratory for Computational
Sensing and Robotics,
Department of Computer Science,
Johns Hopkins University,
Baltimore, MD 21218
e-mail: zihan.chen@jhu.edu

Preetham Chalasani

Laboratory for Computational
Sensing and Robotics,
Department of Computer Science,
Johns Hopkins University,
Baltimore, MD 21218
e-mail: pchalas1@jhu.edu

Rashid M. Yasin

Advanced Robotics and
Mechanism Applications,
Department of Mechanical Engineering,
Vanderbilt University,
Nashville, TN 37235
e-mail: rashid.m.yasin@Vanderbilt.edu

Peter Kazanzides

Laboratory for Computational
Sensing and Robotics,
Department of Computer Science,
Johns Hopkins University,
Baltimore, MD 21218
e-mail: pkaz@jhu.edu

Russell H. Taylor

Laboratory for Computational
Sensing and Robotics,
Department of Computer Science,
Johns Hopkins University,
Baltimore, MD 21218
e-mail: rht@jhu.edu

Nabil Simaan

Advanced Robotics and
Mechanism Applications,
Department of Mechanical Engineering,
Vanderbilt University,
Nashville, TN 37235
e-mail: nabil.simaan@Vanderbilt.edu

Manuscript received October 13, 2016; final manuscript received December 16, 2016; published online March 9, 2017. Assoc. Editor: Hai-Jun Su.

J. Mechanisms Robotics 9(2), 021010 (Mar 09, 2017) (11 pages) Paper No: JMR-16-1304; doi: 10.1115/1.4035684 History: Received October 13, 2016; Revised December 16, 2016

This paper proposes an approach for using force-controlled exploration data to update and register an a priori virtual fixture geometry to a corresponding deformed and displaced physical environment. An approach for safe exploration implementing hybrid motion/force control is presented on the slave robot side. During exploration, the shape and the local surface normals of the environment are estimated and saved in an exploration data set. The geometric data collected during this exploration scan are used to deform and register the a priori environment model to the exploration data set. The environment registration is achieved using a deformable registration based on the coherent point drift method. The task-description of the high-level assistive telemanipulation law, called a virtual fixture (VF), is then deformed and registered in the new environment. The new model is updated and used within a model-mediated telemanipulation framework. The approach is experimentally validated using a da-Vinci research kit (dVRK) master interface, a dVRK patient side manipulator, and a Cartesian stage robot. Experiments demonstrate that the updated VF and the updated model allow the users to improve their path following performance and to shorten their completion time when the updated path following VF is applied. The approach presented has direct bearing on a multitude of surgical applications including force-controlled ablation.

Copyright © 2017 by ASME
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References

Figures

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Fig. 1

System architecture

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Fig. 2

A custom Cartesian slave robot system: (a) experiment setup, (b) ball probe finger ATI force torque sensor, and (c) a phantom model used in experiment

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Fig. 3

PSM experiment setup: (a) experiment setup, (b) ball probe finger adapter integrated with EM tracker, and (c) a phantom model mounted on a force plate

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Fig. 4

PSM force-controlled slave MLC-LLC controller

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Fig. 5

Vanderbilt slave MLC-LLC controller

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Fig. 6

Master MLC impedance type controller: q—joint position; q˙—joint velocity; x—Cartesian position; x˙—Cartesian velocity; T—total joint torque applied to robot; TVF—joint torque from virtual fixture controller Tgc—joint torque from gravity compensation

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Fig. 7

Surface following frame with master robot tip and force feedback

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Fig. 8

Curve following frame with master robot tip and force feedback

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Fig. 9

Force-controlled exploration strategy

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Fig. 10

Contact location and surface norm estimation

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Fig. 11

Orientation optimization for force-controlled exploration using a robot with wrist

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Fig. 12

Force-controlled exploration using custom Cartesian robot: (a) is the planned scan pattern and (b) is the actual scanned point cloud

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Fig. 13

Force-controlled exploration using dVRK PSM: (a) is the silicone phantom organ and (b) is the actual collected point cloud from exploration

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Fig. 14

Creating an apriori model of the silicone phantom: (a) apriori STL model (PA), (b) digitizing the target curve (Cdig) using Faro Arm, and (c) laser scan (Pls) and the digitized curve (Cdig) in red

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Fig. 15

The process of updating the virtual fixture (VF) geometry: (a) the apriori model (pre-operative model) with a VF curve, (b) the deformed environment obtained from exploration data, and (c) using correspondence list to find the VF points in the exploration data set that match the curve from apriori data set

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Fig. 17

Errors between the updated VF curve and the digitized ground truth fitted curve: (a) PSM robot case, (b) PSM laser comparison, (c) Cartesian robot case, and (d) Cartesian laser comparison

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Fig. 19

Comparisons of the users' performance with and without virtual fixture on target curve tracing RMS errors and completion time

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Fig. 18

Ground truth digitization data points and the smooth curve fit

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Fig. 16

Deformable registration using exploration data from dVRK PSM robot and ground truth: (a)–(g) show iterations of deformable registration using PSM robot data where iteration numbers are {1, 3, 5, 10, 20, 50, 100}, (h) is the deformable registration result using laser scan data

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