Needle Grasp and Entry Port Selection for Automatic Execution of Suturing Tasks inRobotic Minimally Invasive Surgery

Abstract

This paper presents algorithms for selection of needle grasp and for selection of entry ports of robotic instruments, for autonomous robotic execution of the minimally invasive surgical suturing task. A critical issue for automatic execution of surgical tasks, such as suturing, is the choice of needle grasp for the robotic system. Inappropriate needle grasp increases operating time requiring multiple regrasps to complete the desired task. In robotic minimally invasive surgery, the entry port that the surgical robot goes through into the patient’s body has a significant role on the performance of the robot. Improper entry port affects the robot’s dexterity, manipulability and reachability. The proposed methods use manipulability, dexterity and torque metrics for needle grasp selection, and employ needle grasp robustness and target location robustness metrics for port selection. The results of a case study simulation in thoracoscopic surgery is also presented to demonstrate the proposed methods. Note to Practitioners—This paper is motivated by the problem of automating low-level surgical tasks in robotic surgery, such as, suturing, retraction, dissection, and providing exposure. Specifically, this paper focuses on needle grasp and entry port selection for automating robotic surgical suturing. Selection of an appropriate way of grasping a needle is critical for successfully and robustly completing autonomous suturing. To the best authors’ knowledge, there are no earlier studies in the literature which focus on the needle grasp selection problem. The proposed approach determines how to grasp the needle by optimizing the surgical system’s manipulation performance. The existing approaches in the literature for selecting entry ports for the robotic surgical tools only consider the teleoperated robotic minimally invasive surgery, in which the surgeons directly control the robotic instruments. However, automated performance of suturing introduces additional challenges due to uncertainties in needle localization and grasping. This paper proposes two new performance metrics on selecting port locations from the perspective of autonomously performing surgical suturing, without direct involvement of the human user. The paper also presents preliminary experiments which demonstrate the effectiveness of the proposed methods.

Intelligent control of neurosurgical robot MM-3 using dynamic motion scaling

“…The automation of dynamic motion scaling is a new and original proposal that is trying to incorporate intelligence into the neurosurgical robot. Compared with other automated controls of surgical robots, such as in fully automated suturing, 6 the responsibility of the operator is greater in our scenario, which is an advantage considering regulatory issues.…”

Haptic-guided shared control for needle grasping optimization in minimally invasive robotic surgery

“…3) 3 with the needle tangent (this results in a 2-DoFs rotational constraint). In addition, grasping requires the gripper position to belong to the needle shape (additional 2-DoFs position constraint) [17]. The remaining subspace of possible grasping configurations (2-DoFs) can be conveniently parametrized using the needle curvilinear abscissa n ∈ [0, n ] and the angle around the needle tangent α ∈ [α − , α + ] (see Fig.…”

“…4. Grasp parameterization: a successful grasp can be expressed by α, representing the angle around the needle tangent, and n, representing the needle curvilinear abscissa [17], [19].…”

“…Liu et al presented in [16], [17] an offline optimizationbased solution to needle grasping and robotic instruments entry ports selection. An exhaustive search finds the optimal needle grasping pose and entry ports.…”

Needle-tissue interaction force state estimation for robotic surgical suturing

“…Since the needle grasp can have a major effect on the forces generated on the force sensor, tracking the needle grasp (using a model similar to [22]) is the subject of future work. For the purposes of validating the deformation estimation, the suture needle tissue geometry and needle grasp are assumed to be known and used as constants in the UKF.…”