Effects of fiber number and density on fiber jamming: Towards follow-the-leader deployment of a continuum robot

Fiber jamming modules (FJMs) offer flexibility and quick stiffness variation, making them suitable for follow-the-leader (FTL) motions in continuum robots, which is ideal for minimally invasive surgery (MIS). However, their potential has not been fully exploited, particularly in designing and manufacturing small-sized FJMs with high stiffness variation. Although existing research has focused on factors like fiber materials and geometry to maximize stiffness variation, the results often do not apply to FJMs for MIS due to size constraints. Meanwhile, other factors such as fiber number and packing density, less significant to large FJMs but critical to small-sized FJMs, have received insufficient investigation regarding their impact on the stiffness variation for FTL deployment. In this paper, we design and fabricate FJMs with a diameter of 4mm. Through theoretical and experimental analysis, we find that fiber number and packing density significantly affect both absolute stiffness and stiffness variation. Our experiments confirm the feasibility of using FJMs in a medical FTL robot design. The optimal configuration is a 4mm FJM with 0.4mm fibers at a 56% packing density, achieving up to 3400% stiffness variation. A video demonstration of a prototype robot using the suggested parameters for achieving FTL motions can be found at https://youtu.be/7pI5U0z7kcE.

A Low-Cost Teleoperable Surgical Robot with a Macro-Micro Structure and a Continuum Tip for Open-Source Research

Surgical robotic systems equipped with microscale, high-dexterity manipulators have shown promising results in minimally invasive surgery (MIS). One barrier to the widespread adoption of such systems is the prohibitive cost of research and development efforts using current state-of-the-art equipment. To address this challenge, this paper proposes a low-cost and modifiable tendon-driven continuum manipulator for MIS applications. The device is capable of being teleoperated in conjunction with a macro-scale six-axis robotic arm using a haptic stylus. Its control software incorporates and extends freely available and open-source software packages. For verification, we perform teleoperation trials on the proposed continuum manipulator using an electromagnetic tracker. We then integrate the manipulator with a UR5e robotic arm. A series of simulated tumour biopsies were conducted using the integrated robotic system and an anatomical model (phantom), validating its potential efficacy in MIS applications. The complete source code, CAD files for all additively manufactured components, a parts list for the manipulator, and a demonstration video of the proposed system are made available in this work.

Automatic Tissue Traction with Haptics-Enabled 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 haptics-enabled 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 with controlled grasping and pulling forces. Specifically, the grasping stage relies on a controlled grasping force, while the pulling stage is under the guidance of a controlled pulling force. Notably, during the pulling process, the simultaneous control of both grasping and pulling forces is also enabled for more precise tissue traction, achieved through force decoupling. The force controller is built upon a static model of tissue manipulation, considering the interaction between the haptics-enabled 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. The results affirm the feasibility of implementing automatic tissue traction using micro-sized forceps with multi-force control, suggesting its potential to promote autonomous MIS. A video demonstrating the experiments can be found at https://youtu.be/8fe8o8IFrjE.

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

Current minimally invasive surgical robots are lacking in force sensing that is robust to temperature and electromagnetic variation while being compatible with micro-sized instruments. This paper presents a multi-axis force sensing module that can be integrated with micro-sized surgical instruments such as biopsy forceps. The proposed miniature sensing module mainly consists of a flexure, a camera, and a target. The deformation of the flexure is obtained by the pose variation of the top-mounted target, which is estimated by the camera with a proposed pose estimation algorithm. Then, the external force is estimated using the flexure's displacement and stiffness matrix. Integrating the sensing module, we further develop a pair of haptics-enabled forceps and realize its multi-modal force sensing, including touching, grasping, and pulling when the forceps manipulate tissues. To minimize the unexpected sliding between the forceps' clips and the tissue, we design a micro-level actuator to drive the forceps and compensate for the motion introduced by the flexure's deformation. Finally, a series of experiments are conducted to verify the feasibility of the proposed sensing module and forceps, including an automatic robotic grasping procedure on ex-vivo tissues. The results indicate the sensing module can estimate external forces accurately, and the haptics-enabled forceps can potentially realize multi-modal force sensing for task-autonomous robotic surgery. A video demonstrating the experiments can be found at https://youtu.be/4UUTT_hiFcI.