Actuation and Control Strategies for Miniature Robotic Surgical Systems
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Over the past 20 years, tremendous advancements have been made in the fields of minimally invasive surgery (MIS) and minimally invasive robotic assisted (MIRA) 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 tremors, and enabling the precise articulation of smaller instruments. These advancements come with their own drawbacks, however. Robotic systems used in MIRA procedures are large, costly, and do not offer the miniaturized articulation necessary to facilitate tremendous advancements in MIS. This research tests the hypothesis that miniature actuation can overcome some of the limitations of current robotic systems by demonstrating accurate, repeatable control of a small end-effector. A 10X model of a two link surgical manipulator is developed, using antagonistic shape memory alloy (SMA) wires as actuators, to simulate motions of a surgical end-effector. Artificial neural networks (ANNs) 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 0.5 mm. 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.
Neural Network, control, SMA, minimally invasive, surgery, robot, miniature