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Robot-assisted surgery is becoming standard-of-care in minimally invasive surgery. Given the intense development in this area, many believe that the next big step is surgical subtask automation, the partial automation of certain elements of the procedure. Autonomous execution at lower task levels has the potential to safely improve one element of a surgical process. Automation by artificial intelligence may significantly improve surgery with better accuracy and targeting, that can shorten the recovering time of the patient. Furthermore, partial automation can also help surgeons efficiently by reducing the fatigue in the case of time-consuming operations. In this paper, we present the automation of soft tissue retraction, an often recurring subtask of surgical interventions. Soft tissue retraction plays an important role in laparoscopic cholecystectomy, e.g., during the exploration of the Calot triangle, automatic retraction would streamline the procedure. The presented method only relies on a stereo camera image feed, and therefore does not put additional overhead on the already crowded operating room. We developed and tested multiple control methods for soft tissue retraction built on each other: a simple proportional control for reference, one using Hidden Markov Models for state estimation, and one employing fuzzy logic. Our method was tested comparatively with all three controllers in a simplified phantom environment.

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