Optimal Vision-Based Orientation Steering Control for a 3-D Printed Dexterous Snake-Like Manipulator to Assist Teleoperation

Endoscopic cameras attached to miniaturized snake-like continuum robotic arms can improve dexterity, accessibility, and visibility in minimally invasive surgical tasks. This steerable camera can expand the field of view and enhance the surgical experience with additional degrees of freedom. However, it also creates more control options that complicate human–machine interaction. This challenge presents an opportunity to develop novel human–machine control strategies using visual sensing to complement the dexterous actuation of a steerable surgical manipulator. This study presents a semiautonomous controller to assist teleoperation by steering a camera such that it keeps an arbitrary target in the center of the field of view, thus assisting in surveying different orientations about the target with image-based information. Two controllers are proposed using classical image-based visual servoing techniques and optimal visual predictive control techniques. These techniques are simulated and validated on our robot SnakeRaven: a 3-D printed patient-specific end-effector attached to the RAVEN II surgical platform. Both systems, most notably the visual predictive approach, operated successfully with robustness due to a lack of information about the target.