Craniosynostosis involves premature fusion of the cranial sutures resulting in abnormal skull morphology and elevated intracranial pressure. Surgical intervention is necessary to correct the skull shape and to allow for unrestricted brain growth. This study presents a novel snake robot designed for minimally invasive cranial osteotomies featuring two articulating bending segments. The end-effector comprises a bone-punch for bone-cutting, a dural and scalp retractor, as well as channels for an endoscope and an instrument. The robot's bending mechanism is driven by tendons and utilizes geared linkages to facilitate a smooth curved shape. Pre-tensioned antagonistic tendons allow the robot to modulate its stiffness to adapt to external loads. A follow-the-leader algorithm was implemented to guide the robot along a skull cutting path. Experimental results demonstrated that at maximum bending of$60^{\circ}$for segment 1 and$90^{\circ}$for segment 2 there was a$15.9^{\circ}$and$11.5^\circ$error, respectively. Position errors ranged from 2.5 to 21.5 mm when tracing a curved path. The tool increased stiffness with tendon pre-tensioning from 20–100 N during bent configurations$q_{1}$and$q_{2}$for segments 1 and 2, respectively, at$[q_{1},q_{2}]=[0^{\mathrm{o}},30^{\mathrm{o}}]$and$[30^{\circ},60^{\circ}]$. Tip deflection reduced from 0.42 to 0.03 cm and 0.37 to 0.10 cm during axial loading and from 11.40 to 3.88 cm and 3.62 to 0.48 cm during radial loading for each configuration, respectively. Ex vitro trials demonstrated the robots ability to perform simulated osteotomies on skull models to 68–73% of desired path lengths with a maximum deviation of 8 mm.

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.

Cutting is a challenging area in the field of autonomous robotics but is especially interesting for applications such as surgery. One large challenge is the lack of simulations for cutting with surgical robots that can transfer to the real robot. In this work, we create a surgical robotic simulation of rope cutting with realistic visual and physics behavior using the da Vinci Research Kit (dVRK). We learn a cutting policy purely from simulation and sim2real transfer our learned models to real experiments by leveraging Domain Randomization. We find that cutting with surgical instruments such as the EndoWrist Round Tip Scissors comes with certain challenges such as deformations, cutting forces along the jaw, fine positioning, and tool occlusion. We overcome these challenges by designing a reward function that promotes successful cutting behavior through fine positioning of the jaws directly from image inputs. Policies are transferred using a custom sim2real pipeline based on a modular teleoperation framework for identical execution in simulation and the real robot. We achieve a 97.5% success rate in real cutting experiments with our 2D model and a 90% success rate in 3D after the sim2real transfer of our model. We showcase the need for Domain Randomization and a specialized reward function to achieve successful cutting behavior across different material conditions through optimal fine positioning. Our experiments cover varying rope thicknesses and tension levels and show that our final policy can successfully cut the rope across different scenarios by learning entirely from our simulation. Further project information is available athttps://medcvr.utm.utoronto.ca/tase2024-cutrope.htmlNote to Practitioners—Cutting is one of many repetitive tasks during a surgical procedure that can be automated to reduce a surgeon’s fatigue. This paper presents an autonomous approach to cutting that can be learned from simulation. The approach builds a surgical robotic rope environment in simulation that mimics the visuals and physics of real-life rope cutting. An autonomous agent, trained with learning-based methods, uses images to position the surgical scissors and controls the jaw to perform a cut on the rope. The agent creates quality cuts by learning to finely position the jaws optimally near the scissor joint, which is learned through rewards in simulation. Agents from the simulation are transferred to the real robot setup using a custom modular ROS teleoperation framework, treating deep learning-based autonomous agents as input into a teleoperation scheme. Experiments were conducted to examine the success of the method with different material conditions and the effect of fine positioning on cut success.

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.

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

Robot Dynamic Simulators offer convenient implementation and testing of physical robots, thus accelerating research and development. While existing simulators support most real-world robots with serially linked kinematic and dynamic chains, they offer limited or conditional support for complex closed-loop robots. On the other hand, many of the underlying physics computation libraries that these simulators employ support closed-loop kinematic chains and redundant mechanisms. Such mechanisms are often utilized in surgical robots to achieve constrained motions (e.g., the remote center of motion (RCM)). To deal with such robots, we propose a new simulation framework based on a front-end description format and a robust real-time dynamic simulator. Although this study focuses on surgical robots, the proposed format and simulator are applicable to any type of robot. In this manuscript, we describe the philosophy and implementation of the front-end description format and demonstrate its performance and the simulator’s capabilities using simulated models of real-world surgical robots.

The use of magnetic resonance imaging (MRI) for guiding robotic surgical devices has shown great potential for performing precisely targeted and controlled interventions. To fully realize these benefits, devices must work safely within the tight confines of the MRI bore without negatively impacting image quality. Here we expand on previous work exploring MRI guided robots for neural interventions by presenting the mechanical design and assessment of a device for positioning, orienting, and inserting an interstitial ultrasound-based ablation probe. From our previous work we have added a 2 degree of freedom (DOF) needle driver for use with the aforementioned probe, revised the mechanical design to improve strength and function, and performed an evaluation of the mechanism's accuracy and effect on MR image quality. The result of this work is a 7-DOF MRI robot capable of positioning a needle tip and orienting it's axis with accuracy of 1.37 ± 0.06mm and 0.79° ± 0.41°, inserting it along it's axis with an accuracy of 0.06 ± 0.07mm, and rotating it about it's axis to an accuracy of 0.77° ± 1.31°. This was accomplished with no significant reduction in SNR caused by the robot's presence in the MRI bore, <; 10.3% reduction in SNR from running the robot's motors during a scan, and no visible paramagnetic artifacts.