Knowledge of the tip contact force in continuum robots, which are often used as medical instruments, is critical for clinical applications. It enhances the interventionalist's decision-making, navigation efficiency, and procedural safety. However, accurately determining the tip contact force in conventionally sized instruments remains challenging. This study introduces a learning-based method for estimating the external contact force at the tip of a continuum robot. By leveraging curvature and bending angle data from a multi-core fiber equipped with fiber Bragg gratings (FBGs) embedded inside the Nitinol tube, the method maps these inputs to the corresponding tip force in 3D. Experiments conducted on an FBG-embedded Nitinol rod validate the feasibility of the proposed method, yielding Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) values of 20.9$\left(m N^{2}\right), 2.7(m N)$, and$4.6(m N)$, respectively, which represent a 26 % improvement compared to the learning-based vision methodology.

Endoscopic submucosal dissection (ESD) is a technically difficult, minimally invasive, organ preserving resection technique that yields improved clinical outcomes when compared to current conventional procedures but requires experienced surgeons and specialized skills. Difficulty in applying tension during ESD is recognized as the single greatest barrier to wide adoption of the procedure, and solution of this problem is sure to have wide-reaching and immediate adoption. This work presents a compact wireless retraction device that is magnetically actuated and has a high force output with adaptable traction control. The retraction device is 25 mm long and 4 mm in diameter. The device has two modes of operation: first spooling to collect string slack, then transitions via external permanent magnet to internal string twisting to generate a large retraction force. In slack collection the device can contract 11 cm in length at a speed of 6.88 millimeters per second, then clutch to force mode to reach a peak retraction force of 1.33 N, leveraging the micro-transmission twisted string actuation. The wireless device is designed for endoscopic deployment to any surgical environment or lesion within the gastrointestinal tract.

Open Spina Bifida (OSB) is a congenital neural tube defect that affects approximately 1 in 1000 births worldwide. Robotic in-utero OSB repair provides a minimally invasive alternative to open-surgery, which places significant strain on both baby and mother. Recent advancements in da Vinci miniature continuum tools reduce port sizes through the uterus for access to the fetus with lower maternal risk. However, idiosyncrasies in continuum tool behaviour further complicate an already difficult procedure. Consequently, a high-fidelity da Vinci OSB repair simulator is presented featuring continuum tools for surgeon skills training. The simulator incorporates a plugin for suture physics handling, soft body physics for deformable tissues and implements haptic virtual fixtures for improved situational awareness during suturing. Quantitative validation demonstrated virtual tool accuracy, with a mean-squared continuum backbone error of 0.64 mm2and system-level end-effector trajectory errors averaging 3.25 mm for a helix tracing task. During suturing, high-fidelity performance was maintained. Four expert surgeons from relevant specialties provided positive qualitative feedback, reporting that the simulator accurately replicates real tool control and offers a realistic and valuable training experience. Ultimately, the simulator shows promise as a training platform for safer robotic in-utero OSB repair and facilitating the adoption of novel continuum wristed tools in clinical settings.

Open Spina Bifida (OSB) is a congenital neural tube defect where a major component of the procedure to repair the defect involves the closure of a lesion wound through suturing. For a minimally invasive approach, tools entering the uterus to access the fetus should be as thin as possible to minimize maternal risk. This work presents the design of a 3 degrees-of-freedom, 2mm diameter tool wrist with a bending range of motion from 0° to 90°. This wrist is capable of generating up to 2N of force measured from the end of the wrist and achieving a bending curvature of 107m-1(9.35mm bending radius). A pseudo-rigid body kinematic model has been implemented for the control of this tool with a protocol for singularity mitigation and avoidance. Timed teleoperation studies explicitly demonstrate that the tool is able to reliably execute suturing with a fastest achieved time of under 3 minutes for a simple interrupted suturing technique.

Dexterity is demanded for an endoscopic tool to handle complicated procedures in neurosurgery, e.g., removing diseased tissue from inside the deep brain along a tortuous path. Current robotic tools are either rigid or lack wristed motion ability at the tip, leading to limited usage in minimally invasive procedures. In this paper, a hybrid steerable robot with a magnetic wristed forceps is proposed to provide enhanced dexterity for endoscopic epilepsy surgery. A set of three precurved Nitinol tubes with concentric deployment, called a concentric tube robot (CTR), serves as a 6 degrees-of-freedom (DoF) robotic positioner. The magnetic wristed forceps is composed of a rotational wrist joint, and forceps at the tip, both of which are actuated remotely by magnetic fields. The magnetic wrist and forceps provide an extra rotational DoF and a gripping DoF on top of the CTR, respectively. The magnetic wrist and gripper are designed to have a hollow channel along their common axis, inside which a soft tube is deployed as a second functional tool for irrigation or suction. An electromagnetic navigation system (eMNS) with 8 coils is used to create the quasi-static magnetic fields. Experimental characterization of the robot kinematics is performed and the results show the mean motion error of CTR is 2.8 mm. The workspace is also analyzed and results indicate that the proposed hybrid robot has a significantly larger reachable area compared to the one of the CTR alone. Mock epilepsy procedures are performed on a brain phantom to validate the feasibility of the hybrid robot for neurosurgery applications.

Autonomous surgical robotics is a growing area of research, with advances being made in the areas of vision and control. Central to this research is the need for simulations to facilitate data collection and simulate learning environments for Reinforcement Learning (RL) agents. Recent simulators have facilitated RL policy generation, but lack a robust sim2real pipeline and a proven vision-based policy that can use any type of camera including the da Vinci Surgical System (dVSS) Endoscope. To solve this, we build a ROS-based sim2real pipeline that incorporates a Unity3D da Vinci Research Kit (dVRK) simulation, modular kinematics, and shared interfaces. We examine the vision-based task of cube pushing, and train RL policies to execute in real life through Domain Randomization. Our experiments evaluate model success in simulation and two camera systems: OAK-1 and the dVSS Endoscope. Our results indicate that Domain Randomization is effective at bridging the sim2real gap, and even extends to the difficult endoscope scenario. We achieve 100% transfer success rate on both OAK-1 and the dVSS Endoscope, with gains of over 60% compared to a base model with no Domain Randomization. We examine the various randomization parameters, including lighting, camera, and physics variables, and determine that all parameters play a significant role in bridging the sim2real gap. Testing across extreme lighting and camera configurations not seen in simulation, our models continue to perform well, with 85% accuracy on the OAK-1 camera. Our future work will extend to other tasks and more complex policies to take advantage of stereo-camera imaging. Further project information is available at https://medcvr.utm.utoronto.ca/iros2022-sim2real.html

During the last two decades, minimally invasive surgery (MIS) has become popular because it offers advantages such as less pain, faster recovery, improved cosmesis, and reduced complications. Single-port laparoscopic surgery is a form of MIS where surgeons operate exclusively through a single entry. However, the view from the rigid endoscope is often obscured by the instruments which pass through the same single entry. To remove the need for a secondary viewing port and the blind spots during operation, we propose a deployable wrist mechanism for minimally invasive laparoscopic surgery. It utilizes an S-shape nitinol tube with a curvature of 15 mm and 1.83 mm in diameter. When retracted, the s-shaped wrist is straightened into the main shaft of the laparoscopic tool. As the wrist translates outward, the S-shaped nitinol wrist emerges from an opening on the tool shaft and bends to point at the tooltip. The wrist has two degrees of freedom: translational displacement for controlling the bending and rotational movement of the wrist. The bending mechanism was analyzed by finite element method simulation and validated by experiments. For future work, we will try to widen the scope of its applications including laser ablation tools, triangularization, and other microsurgical procedures.

This paper presents a dynamic constraint formulation to provide protective virtual fixtures of 3D anatomical structures from polygon mesh representations. The proposed approach can anisotropically limit the tool motion of surgical robots without any assumption of the local anatomical shape close to the tool. Using a bounded search strategy and Principle Directed tree, the proposed system can run efficiently at 180 Hz for a mesh object containing 989,376 triangles and 493,460 vertices. The proposed algorithm has been validated in both simulation and skull cutting experiments. The skull cutting experiment setup uses a novel piezoelectric bone cutting tool designed for the da Vinci research kit. The result shows that the virtual fixture assisted teleoperation has statistically significant improvements in the cutting path accuracy and penetration depth control. The code has been made publicly available at https://github.com/mli0603/PolygonMeshVirtualFixture.

This paper presents a minimally invasive ultrasonic bone cutting tool designed for the da Vinci®research kit (dVRK). An ultrasonic transducer is modelled using finite element software, and correlated with testing results using an impedance analyzer. A multi-objective genetic algorithm is then used to design and analyze the remaining components of the ultrasonic system, in order to maximize system performance. The system is fabricated and mounted to the da Vinci®research kit system and tested on a cutting phantom.

In clinical practice, magnetic resonance imaging (MRI) is used to locate a lesion/tumor for bone biopsy in children. However, there is a lack of MR-compatible tools that can be used simultaneously during imaging and biopsy while maintaining surgical accuracy and safety. The Pediatric Surgery Robot (PSR) platform is a 5-DOF robot with a modular tool interface. For the case of bone biopsy, a Bone Biopsy Tooling (BBT) is attached. It is designed to fit within a Philips Achieva 3.0T MRI bore and carry a modified titanium bone biopsy needle. A surgical pre-planning and control interface has been developed for joint and Cartesian level control. The PSR-BBT has demonstrated 1.65 +/- 1.77 mm accuracy in Cartesian control in free space. The PSR-BBT can generate 12.46 +/- 0.32 N of axial force while drilling at a speed of 30 rpm, which is sufficient for cortical and cancellous bone phantoms. Under MRI testing (T1-FFE, T1-SE, T2-FFE and T2-TSE scans), the system demonstrated less than 33% signal-to-noise ratio variation while drilling and a 0.46% geometric distortion while powered on without significantly impacting MRI guidance in situ. These results show that the PSR-BBT can allow the user to simultaneously image and perform the biopsy and presents the PSR as a viable platform for MR-guided robotic surgery.

One of the major problems facing the development and road to practical usage of concentric tube continuum robots in surgical environments is that of instability. This issue, also known as the snapping problem, is caused by a tube having a high bending to torsional stiffness ratio (BTSR). Past efforts have shown that by cutting patterns on the tubes, this problem can be avoided. This paper seeks to redesign the topology of the tubes so that BTSR is decreased and the snapping problem is resolved in a particular tube set. The generated designs are then tested through finite element analysis as well as experimental testing to demonstrate the elimination of the snapping problem. Using this novel tube design on a concentric tube robotic system can increase its stable workspace because it allows the usage of greater tube curvatures and/or curve lengths.

We present a kinetostatic model for a needle-sized notched tube continuum joint design, and examine the impact that the design parameters have on both stiffness and joint kinematics. The joint is fabricated by removing a series of asymmetric notches from a metal tube. By fixing a cable to the distal end of the tube, and routing this tendon through the tube's lumen, the joint can be actuated in bending. This simple cutting pattern allows for impressive performance compared to other mechanisms of similar size. However, selecting the cutting geometry using kinematics alone results in many redundant solutions. Since the notches significantly affect the overall structure's stiffness, and limit the maximum forces that it can transmit, a stiffness model can be used to constrain the design-space. Further, because the notch geometry represents a non-prismatic beam configuration, modelling the force-deflection behavior is nontrivial. We have approached this problem using a variation of Castigliano's 2ndTheorem, experimentally validated its performance with several test specimens, and analyzed the behavior of the model over a design space specific to neurosurgical applications. The outcomes of this study aim to aid in the design of joints given task-specific constraints, particularly within the field of surgical robotics.

Concentric-tube manipulators could experience a snapping-through motion that negatively impacts their smooth operation. This may limit their adoption in the operating room, despite their advantage of enabling dexterity within a highly confined space. In this paper, we demonstrate through the kinematics of concentric-tube robots, how this adverse effect of torsion could be reduced or potentially eliminated. As proof of principle, we demonstrate experimentally and numerically that by adopting tubular composite structures, such as multi-layer helical tubes or cellular tubes, that could be designed to exhibit higher torsional-to-bending stiffness ratio, the stability margin of concentric-tube robots can be improved up to 40%. This will allow concentric-tube robots to achieve even a higher dexterity within a more confined space.

In this paper, a novel closed-loop inverse kinematics scheme that is capable of handling inequality constraints is proposed. By a proper adoption of slack variables and using the task priority concept, the proposed method allows for the accommodation of inequality constraints in a closed-form inverse kinematics solution for robotic manipulators. Unlike other formulations, the proposed scheme does not require solving a sequence of constrained optimization problems in real time and in this sense is faster. The performance of the proposed scheme is investigated through an implementation for real-time control of a concentric-tube robot with a restricted operational space. The experimental results demonstrate the efficacy of the scheme in handling inequality constraints in real-time closed-loop inverse kinematics.

In this paper, a novel approach for automated suturing is introduced and experimental results are presented. Unlike other similar works, the proposed approach adopts a single arm to implement a suturing task with a standard laparoscopic needle holder and curved suture needle. 3D information is obtained from a clinical (single camera) endoscope through an elliptical/circular pose measurement algorithm, which dynamically tracks the suture needle and surface markers. This drives robotic needle steering through a set of surgeon-defined entry/exit points on a tissue pad phantom. Implementation results indicate good depth resolution (1.5mm) and task repeatability (85%) for a variety of consistency, lighting, and location variation scenarios.

Minimally invasive surgery (MIS) revolutionized surgery by drastically reducing patient recovery times by allowing surgeons to perform procedures through a series of small incisions. However, MIS has also increased the complexity of the tasks as tools did not have the same degrees of freedom and dexterity compared to open procedures. In particular, pediatric patients pose a unique challenge as they have smaller volumes and different tissue properties. Our group designed KidsArm, an image-guided pediatric surgical robot, to automate anastomosis. KidsArm is single port anastomosis tool that uses a pair of stereo cameras to generate a 3D point cloud to guide the tool tip and apply a series of sutures. The system was designed to be minimally invasive and constrained by standard pediatric trocar sizes while also being automated. An image processing system was created to extract and track surface features on the simulated tissue samples while providing feedback to the robot controller. The system was tested on two scenarios: side-to-side and end-to-end silicone samples. KidsArm successfully applied 3 sutures autonomously on the side-to-side scenario however the end-to-end scenario proved to be more difficult due to greater deformation and workspace restrictions. However, KidsArm demonstrates that it is feasible for a robot to autonomously perform anastomosis. More work will be required to accelerate the process and characterize the behavior with tissue samples.