Automatic Tissue Traction Using Miniature Force-Sensing Forceps for Minimally Invasive Surgery

TL;DR

A method to automate tissue traction that comprises grasping and pulling stages that affirm the feasibility of implementing automatic tissue traction using miniature forceps with multiforce control, suggesting its potential to promote autonomous MIS.

Abstract

A common limitation of autonomous tissue manipulation in robotic minimally invasive surgery (MIS) is the absence of force sensing and control at the tool level. Recently, our team has developed miniature force-sensing forceps that can simultaneously measure the grasping and pulling forces during tissue manipulation. Based on this design, here we further present a method to automate tissue traction that comprises grasping and pulling stages. During this process, the grasping and pulling forces can be controlled either separately or simultaneously through force decoupling. The force controller is built upon a static model of tissue manipulation, considering the interaction between the force-sensing forceps and soft tissue. The efficacy of this force control approach is validated through a series of experiments comparing targeted, estimated, and actual reference forces. To verify the feasibility of the proposed method in surgical applications, various tissue resections are conducted on ex vivo tissues employing a dual-arm robotic setup. Finally, we discuss the benefits of multi-force control in tissue traction, evidenced through comparative analyses of various ex vivo tissue resections with and without the proposed method, and the potential generalization with traction on different tissues. The results affirm the feasibility of implementing automatic tissue traction using miniature forceps with multi-force control, suggesting its potential to promote autonomous MIS. A video demonstrating the experiments can be found at https://youtu.be/f5gXuXe67Ak.

Figures (14)

• Figure 1: Typical tissue resection with forceps and scissors. (a) Dual-arm robotic system for lesion resection, where the forceps are responsible for tissue traction, while the scissors are responsible for tissue cutting (inset adapted from skandalakis2021surgical). The grasping force $F_g$ control and pulling force $F_p$ control are crucial for efficient and safe tissue traction. (b) and (c) show tissue slide and break results from insufficient and excessive grasping forces $F_g$, respectively. (d) insufficient tissue pulling force $F_p$ results in cutting surface overlapping and prevents the scissors from performing continuous cuts. (e) unexpected tissue split results from excessive pulling force $F_p$.
• Figure 2: Portable instruments and micro-level actuator. (a) The micro-level actuator updated from liu2023hapticsenabled, wherein the screw 1 is used to fix the instrument’s stainless tube, and the screw 2 is used to lock the forceps’ driving cable. (b) Instrument prototypes that integrate with the force-sensing forceps and scissors, wherein the docking part positions and orientates the instrument. (c) force-sensing forceps with a vision-based force sensing module presented in liu2023hapticsenabled, where the camera (5) is used to measure the deformation of the spring (4) by tracking and estimating the pose of the target (3).
• Figure 3: (a) Forces applied to the forceps when they grasp and pull a tissue include driving force $F_d$ from the cable, supporting force $F_s$ from the sensing module, contact force $F_g$ (also called grasping force), and friction force $F_f$ from the tissue. (b) Model of tissue traction with force-sensing microforceps. ${}^{T}d_p$ and ${}^{T}d_s$ denote the displacement of the forceps’ jaws related to the tissue body {$T$} and that of the spring base related to {$T$}, respectively. ${}^{R}d_l$ is the lower driver displacement associated with its mounting points on the robotic arm {$R$}, while ${}^{L}d_u$ is the upper driver displacement related to its mounting points on the lower driver {$L$}.
• Figure 4: Experimental setup for force control evaluation, where a commercial gauge force sensor and pressure sensor were adopted to measure reference pulling and grasping forces, respectively. The inset shows three 3D-printed extension jaws configured with $\theta$ = $10^\circ$, $30^\circ$, and $50^\circ$.
• Figure 5: Evaluation results of tissue grasping with controlled $F_g$. (a), (b), and (c) are results of experiments conducted with $F^{\ast}_g$ frequency was set to 1/30Hz, 1/15Hz, and 1/10Hz, respectively. $F^{\ast}_{g,\:,i}$, $F^e_{g,\:i}$, and $F^r_{g,\:i}$ indicate targeted, estimated, and reference grasping forces. $i=$ 1, 2, and 3 indicate experiments conducted with grasping angle $\theta = 10^\circ$, $30^\circ$, $50^\circ$, and the corresponding amplitudes of $F^{\ast}_{g,\: i}$ are 0.2N, 0.25N, and 0.3N. (d) Corresponding errors patched with red ($\theta=10^\circ$), green ($\theta=30^\circ$), and blue ($\theta=50^\circ$). The first nine boxes are the errors between $F^e_g$ and $F^\ast_g$, while the last nine are the errors between $F^e_g$ and $F^r_g$.