Toward automated tissue retraction in robot-assisted surgery

Abstract

Abstract-Robotic surgical assistants are enhancing physician performance, enabling physicians to perform more delicate and precise minimally invasive surgery. However, these devices are currently tele-operated and lack autonomy. In this paper, we present initial steps toward automating a commonly performed surgical task, tissue retraction, which involves grasping and lifting a thin layer of tissue to expose an underlying area. Given a model of tissues in the vicinity, our method computes a motion plan for a 6-DOF gripper that grasps a tissue flap at an optimal location and retracts it such that an underlying target is fully visible. The planner considers three optimization objectives relevant to medical applications: minimizing the maximum deformation energy, minimizing maximum stress, and minimizing the control effort in lifting the tissue flap. The planner can be used to locally improve physician specified retraction trajectories based on the optimization criteria or to compute a de novo plan. We use a physically-based simulation to compute equilibrium configurations of the tissue flap subject to manipulation constraints. These configurations are used with a sampling-based planner to explore the space of deformations and compute an optimal plan subject to discretization and modeling error. Our experimental results illustrate the ability of the method to compute retractions for heterogeneous tissues while avoiding obstacles and minimizing tissue damage.

Autonomous multilateral debridement with the Raven surgical robot

“…We note that prior work has addressed the problem of designing planning and control algorithms for autonomous execution of other surgical sub-tasks such as knot tying or suturing [28], [39] and tissue retraction during surgery [22], [14].…”

Dynamic Gesture Recognition Using a Smart Glove in Hand-Assisted Laparoscopic Surgery

“…The actions to test the collaborative robot take into account the various robotic actions covered by the literature [20], among which are the guidance of the laparoscopic camera for the safe movement of the endoscope [21] or a needle insertion [22], the prediction of the end point [23,24], the knotting and unknotting on suture procedures [25], or grasping and lifting on tissue retraction [26]. Ultimately, we selected three actions to be performed by the collaborative robot: center the image from the endoscope, indicate a place to suture, and stretch the thread to suture.…”

Modelling and control of non-linear tissue compression and heating using an LQG controller for automation in robotic surgery

“…Though current robot-assisted surgical procedures are performed with a robot faithfully following the surgeon's hand motion in a master-slave teleoperation system, semi or fully autonomous control of surgical tasks will allow surgeons to let the robot finish tasks with more precision or accuracy, such as applying forces and temperatures to tissues, and eventually improving the safety and reliability of surgical procedure (Feng et al, 2012;Kanno et al, 2013;Low and Phee, 2006). Previous work on automation in robot-assisted surgery has focused on tissue compression (Sankaranarayanan et al, 2011;Yu, 2008;Yu andChizeck, 2008a, 2008b;Yu et al, 2007) or tissue retraction (Patil and Alterovitz, 2010). However, simultaneous compression and heating of tissue are common electrosurgical procedures, such as vessel closure, dissection and coagulation.…”

“…In this paper, these models and experiment data will be comprehensively used to design a controller for surgical automation. The second step is the design of control algorithms based on the system model (Patil and Alterovitz, 2010;Sankaranarayanan et al, 2011;Yu, 2008;. Relevant control approaches studied in the references include robot force control, robot impedance control, automatic needle placement, automatic ligation and force control of knot tying (Yu, 2008).…”