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TECHNICAL PAPERS

Actuation and Control Strategies for Miniature Robotic Surgical Systems

[+] Author and Article Information
Jason M. Stevens

Department of Mechanical and Aerospace Engineering North Carolina State University, Box 7910, Raleigh, NC 27695

Gregory D. Buckner

Department of Mechanical and Aerospace Engineering North Carolina State University, Box 7910, Raleigh, NC 27695greg_buckner@ncsu.edu

J. Dyn. Sys., Meas., Control 127(4), 537-549 (Dec 21, 2004) (13 pages) doi:10.1115/1.2098892 History: Received August 25, 2003; Revised December 21, 2004

During the past 20years, tremendous advancements have been made in the fields of minimally invasive surgery (MIS) and minimally invasive, robotically assisted (MIRA) cardiac surgery. Benefits from MIS include reduced pain and trauma, reduced risks of post-operative complications, shorter recovery times, and more aesthetically pleasing results. MIRA approaches have extended the capabilities of MIS by introducing three-dimensional vision, eliminating hand tremors, and enabling the precise articulation of smaller instruments. These advancements come with their own drawbacks, however. Robotic systems used in MIRA cardiac procedures are large, costly, and do not offer the miniaturized articulation necessary to facilitate tremendous advancements in MIS. This paper demonstrates that miniature actuation can overcome some of the limitations of current robotic systems by providing accurate, repeatable control of a small end effector. A 10× model of a two link surgical manipulator is developed, using antagonistic shape memory alloy wires as actuators, to simulate motions of a surgical end-effector. Artificial neural networks are used in conjunction with real-time visual feedback to “learn” the inverse system dynamics and control the manipulator endpoint trajectory. Experimental results are presented for indirect, on-line learning and control. Manipulator tip trajectories are shown to be accurate and repeatable to within 0.5mm. These results confirm that SMAs can be effective actuators for miniature surgical robotic systems, and that intelligent control can be used to accurately control the trajectory of these systems.

Copyright © 2005 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Laparoscopic surgical instruments (5)

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Figure 2

The daVinci surgical robot from Intuitive Surgical (37)

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Figure 3

Suturing with Intuitive’s da Vinci robotic system

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Figure 4

Energy densities of miniature actuation candidates (adapted from (25))

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Figure 5

Solid model of a conceptual, semiautonomous, microrobotic suturing device

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Figure 6

Photograph of the 10× serial manipulator

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Figure 7

Photograph of the complete 10× serial manipulator test rig

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Figure 8

Hardware schematic

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Figure 9

Photograph of the translational SMA test rig

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Figure 10

Open-loop step response of the translational SMA test rig

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Figure 11

Tracking response of the translational SMA test rig: proportional control at 1000Hz sampling rate

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Figure 12

Tracking response of the translational SMA test rig: proportional control at 100Hz sampling rate

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Figure 13

Tracking response of the translational SMA test rig: proportional control at 25Hz sampling rate

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Figure 14

Tracking response of the translational SMA test rig: proportional control at 10Hz sampling rate

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Figure 15

Tracking response of the translational SMA test rig: proportional control at 1Hz sampling rate

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Figure 16

Functional schematic of the intelligent control system

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Figure 18

Tracking response of the translational SMA test rig: untrained intelligent control at 1Hz sampling rate

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Figure 19

Tracking response of the translational SMA test rig: trained intelligent control at 1Hz sampling rate

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Figure 20

Tracking response of the translational SMA test rig: trained intelligent control versus proportional control at 1Hz sampling rate

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Figure 21

Random input/output training data used for off-line training

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Figure 22

Tracking response of the manipulator tip: on-line training and control, with off-line pretraining

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