The da Vinci Research Kit (dVRK) is a teleoperated surgical robotic system. For dynamic simulations and modelbased control, the dynamic model of the dVRK is required. We present an open-source dynamic model identification package for the dVRK, capable of modeling the parallelograms, springs, counterweight, and tendon couplings, which are inherent to the dVRK. A convex optimization-based method is used to identify the dynamic parameters of the dVRK subject to physical consistency. Experimental results show the effectiveness of the modeling and the robustness of the package. Although this software package is originally developed for the dVRK, it is feasible to apply it on other similar 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.

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Research in surgical robotics and automation has made remarkable advancements in recent years thanks to new methods in computer vision, control, and deep learning. Autonomous end-effector manipulation is a challenging task in surgical robotics, and cutting with scissor tools is largely unexplored. A concurrent work explored path and trajectory generation for cutting deformable materials using the da Vinci Research Kit (dVRK) [1]. However, an efficient and realistic simulation is necessary for methods such as reinforcement learning (RL) or learned trajectory planning. Our previous work built a simulation for the dVRK in Unity for training RL algorithms on rigid body tasks [2]. To our knowledge, there is no dVRK simulation available that includes the cutting of deformable materials. This paper introduces a cutting simulation of a deformable mesh, which can represent a tissue layer, built onto our Unity dVRK simulation.