Journal of Medical Robotics Research

Retinal surgery can be performed only by surgeons possessing advanced surgical skills because of the small, confined intraocular space, and the restricted free motion of the instruments in contact with the sclera. Snake-like robots may be essential for use in retinal surgery to overcome this problem. Such robots can approach the target site from suitable directions and operate on delicate tissues during retinal vein cannulation, epiretinal membrane peeling, and so on. We propose an improved integrated robotic intraocular snake (I²RIS), which is a new version of our previous IRIS. This study focused on the analyses of the kinematics and drive mechanism of the dexterous distal unit. This unit consists of small elements with reduced contact stress achieved by changing wire-hole positions. The kinematic analysis of the dexterous distal unit shows that it is possible to control the bending angle and direction of the unit by using two pairs of drive wires. The proposed drive mechanism includes a new pull-and-release wire mechanism in which the drive pulley is mounted at a right angle relative to the actuation direction (also, relative to the conventional direction). Analysis of the drive mechanism shows that compared to the previous drive mechanism, the proposed mechanism is simpler and easier to assemble and yields higher accuracy and resolution. Furthermore, considering clinical use, the instrument of the I²RIS is detachable from the motor unit easily for cleaning, sterilization, and attachment of various surgical tools. Analyses of the kinematics and drive mechanism and the basic functions of the proposed mechanism were verified experimentally on actual-size prototypes of the instrument and motor units.

This paper presents the development of a magnetic resonance imaging (MRI)-conditional needle positioning robot designed for spinal cellular injection. High-accuracy targeting performance is achieved by the combination of a high precision, parallel-plane, needle-orientation mechanism utilizing linear piezoelectric actuators with an iterative super-resolution (SR) visual navigation algorithm using multi-planar MR imaging. In previous work, the authors have developed an MRI conditional robot with positioning performance exceeding the standard resolution of MRI, rendering the MRI resolution the limit for navigation. This paper further explores the application of SR to images for robot guidance, evaluating positioning performance through simulations and experimentally in benchtop and MRI experiments.

While robot-assisted minimally invasive surgery (RMIS) procedures afford a variety of benefits over open surgery and manual laparoscopic operations (including increased tool dexterity, reduced patient pain, incision size, trauma and recovery time, and lower infection rates [], lack of spatial awareness remains an issue. Typical laparoscopic imaging can lack sufficient depth cues and haptic feedback, if provided, rarely reflects realistic tissue–tool interactions. This work is part of a larger ongoing research effort to reconstruct 3D surfaces using multiple viewpoints in RMIS to increase visual perception. The manual placement and adjustment of multicamera systems in RMIS are nonideal and prone to error [], and other autonomous approaches focus on tool tracking and do not consider reconstruction of the surgical scene [,,]. The group’s previous work investigated a novel, context-aware autonomous camera positioning method [], which incorporated both tool location and scene coverage for multiple camera viewpoint adjustments. In this paper, the authors expand upon this prior work by implementing a streamlined deep reinforcement learning approach between optimal viewpoints calculated using the prior method [] which encourages discovery of otherwise unobserved and additional camera viewpoints. Combining the framework and robustness of the previous work with the efficiency and additional viewpoints of the augmentations presented here results in improved performance and scene coverage promising towards real-time implementation.

This paper presents a new mechanics model for unidirectional notched-tube continuum wrists, a class of mechanisms frequently used to implement distal steering in needle-sized surgical robotic instruments. Existing kinematic models available for these devices are based on the simplifying assumption that, during actuation, all the notches undergo the same amount of deflection, so that the shape of a wrist can be approximated by an arc of constant curvature. This approach is analytically attractive, but, as we show in this paper, it can sometimes fail to provide good tracking accuracy. In this paper, we provide a new model that relaxes the assumption above, and we report experimental evidence showing its superior accuracy. We model wrist deflection using Castigliano’s Second Theorem, with the addition of a capstan friction term that accounts for frictional losses on the actuation tendon. Because notched-tube wrists are typically made of Nickel-Titanium (Nitinol), which has nonlinear stress–strain characteristics, we use a technique to obtain a local linearized approximation of the material modulus, suitable for use in the deflection model. The result of our modeling is a system of nonlinear equations that can be solved numerically to predict the wrist configuration based on the applied actuation force. Experimental results on physical specimens show that this improved model provides a more accurate estimate of wrist kinematics than prior models assuming constant curvature bending.