arXiv subjects

Jay Katupitiya

Publications and source records attributed to Jay Katupitiya.

2 recordsLinked to original sources

arXiv2024

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

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.

cs.RO↗

arXiv2024

Haptics-Enabled Forceps with Multi-Modal Force Sensing: Towards Task-Autonomous Surgery

Many robotic surgical systems have been developed with micro-sized forceps for tissue manipulation. However, these systems often lack force sensing at the tool side and the manipulation forces are roughly estimated and controlled relying on the surgeon's visual perception. To address this challenge, we present a vision-based module to enable the micro-sized forceps' multi-modal force sensing. A miniature sensing module adaptive to common micro-sized forceps is proposed, consisting of a flexure, a camera, and a customised target. The deformation of the flexure is obtained by the camera estimating the pose variation of the top-mounted target. Then, the external force applied to the sensing module is calculated using the flexure's displacement and stiffness matrix. Integrating the sensing module into the forceps, in conjunction with a single-axial force sensor at the proximal end, we equip the forceps with haptic sensing capabilities. Mathematical equations are derived to estimate the multi-modal force sensing of the haptics-enabled forceps, including pushing/pulling forces (Mode-I) and grasping forces (Mode-II). A series of experiments on phantoms and ex vivo tissues are conducted to verify the feasibility of the proposed design and method. Results indicate that the haptics-enabled forceps can achieve multi-modal force estimation effectively and potentially realize autonomous robotic tissue grasping procedures with controlled forces. A video demonstrating the experiments can be found at https://youtu.be/pi9bqSkwCFQ.

cs.RO↗