A Millimeter-Scale Soft Robot for Tissue Biopsy Procedures

While interest in soft robotics as surgical tools has grown due to their inherently safe interactions with the body, their feasibility is limited in the amount of force that can be transmitted during procedures. This is especially apparent in minimally invasive procedures where millimeter-scale devices are necessary for reaching the desired surgical site, such as in interventional bronchoscopy. To leverage the benefits of soft robotics in minimally invasive surgery, a soft robot with integrated tip steering, stabilization, and needle deployment capabilities is proposed for lung tissue biopsy procedures. Design, fabrication, and modeling of the force transmission of this soft robotic platform allows for integration into a system with a diameter of 3.5 mm. Characterizations of the soft robot are performed to analyze bending angle, force transmission, and expansion during needle deployment. In-vitro experiments of both the needle deployment mechanism and fully integrated soft robot validate the proposed workflow and capabilities in a simulated surgical setting.

Closed-form Kinematic Model and Workspace Characterization for Magnetic Ball Chain Robots

Magnetic ball chains are well suited to serve as the steerable tips of endoluminal robots. While it has been demonstrated that these robots produce a larger reachable workspace than magnetic soft continuum robots designed using either distributed or lumped magnetic material, here we investigate the orientational capabilities of these robots. To increase the range of orientations that can be produced at each point in the workspace, we introduce a comparatively-stiff outer sheath from which the steerable ball chain is extended. We present an energy-based kinematic model and also derive an approximate expression for the range of achievable orientations at each point in the workspace. Experiments are used to validate these results.

Brain-Mimicking Phantom for Photoablation and Visualization

While the use of tissue-mimicking (TM) phantoms has been ubiquitous in surgical robotics, the translation of technology from laboratory experiments to equivalent intraoperative tissue conditions has been a challenge. The increasing use of lasers for surgical tumor resection has introduced the need to develop a modular, low-cost, functionally relevant TM phantom to model the complex laser-tissue interaction. In this paper, a TM phantom with mechanically and thermally similar properties as human brain tissue suited for photoablation studies and subsequent visualization is developed. The proposed study demonstrates the tuned phantom response to laser ablation for fixed laser power, time, and angle. Additionally, the ablated crater profile is visualized using optical coherence tomography (OCT), enabling high-resolution surface profile generation.

Control of Magnetic Surgical Robots With Model-Based Simulators and Reinforcement Learning

Magnetically manipulated medical robots are a promising alternative to current robotic platforms, allowing for miniaturization and tetherless actuation. Controlling such systems autonomously may enable safe, accurate operation. However, classical control methods require rigorous models of magnetic fields, robot dynamics, and robot environments, which can be difficult to generate. Model-free reinforcement learning (RL) offers an alternative that can bypass these requirements. We apply RL to a robotic magnetic needle manipulation system. Reinforcement learning algorithms often require long runtimes, making them impractical for many surgical robotics applications, most of which require careful, constant monitoring. Our approach first constructs a model-based simulation (MBS) on guided real-world exploration, learning the dynamics of the environment. After intensive MBS environment training, we transfer the learned behavior from the MBS environment to the real-world. Our MBS method applies RL roughly 200 times faster than doing so in the real world, and achieves a 6 mm root-mean-square (RMS) error for a square reference trajectory. In comparison, pure simulation-based approaches fail to transfer, producing a 31 mm RMS error. These results demonstrate that MBS environments are a good solution for domains where running model-free RL is impractical, especially if an accurate simulation is not available.

ColibriDoc: An Eye-in-Hand Autonomous Trocar Docking System

Retinal surgery is a complex medical procedure that requires exceptional expertise and dexterity. For this purpose, several robotic platforms are currently under development to enable or improve the outcome of microsurgical tasks. Since the control of such robots is often designed for navigation inside the eye in proximity to the retina, successful trocar docking and insertion of the instrument into the eye represents an additional cognitive effort, and is therefore one of the open challenges in robotic retinal surgery. For this purpose, we present a platform for autonomous trocar docking that combines computer vision and a robotic setup. Inspired by the Cuban Colibri (hummingbird) aligning its beak to a flower using only vision, we mount a camera onto the endeffector of a robotic system. By estimating the position and pose of the trocar, the robot is able to autonomously align and navigate the instrument towards the Trocar Entry Point (TEP) and finally perform the insertion. Our experiments show that the proposed method is able to accurately estimate the position and pose of the trocar and achieve repeatable autonomous docking. The aim of this work is to reduce the complexity of the robotic setup prior to the surgical task and therefore, increase the intuitiveness of the system integration into clinical workflow.

Adaptive Surgical Robotic Training Using Real-Time Stylistic Behavior Feedback Through Haptic Cues

Surgical skill directly affects surgical procedure outcomes; thus, effective training is needed to ensure satisfactory results. Many objective assessment metrics have been developed that provide the trainee with descriptive feedback about their performance however, often lack feedback on how to improve performance. The most effective training method is one that is intuitive, easy to understand, personalized to the user,and provided in a timely manner. We propose a framework to enable user-adaptive training using near real-time detection of performance, based on intuitive styles of surgical movements, and design a haptic feedback framework to assist with correcting styles of movement. We evaluate the ability of three types of force feedback (spring, damping, and spring plus damping feedback), computed based on prior user positions, to improve different stylistic behaviors of the user during kinematically constrained reaching movement tasks. The results indicate that five out of six styles studied here were improved using at least one of the three types of force feedback. Task performance metrics were compared in the presence of the three types of feedback. Task time was statistically significantly lower when applying spring feedback, compared to the other two types of feedback. Path straightness and targeting error were statistically significantly improved when using spring-damping feedback compared to the other two types of feedback. This study presents a groundwork for adaptive training in robotic surgery based on near real-time human-centric models of surgical behavior.

Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue

Real-time visual localization of needles is necessary for various surgical applications, including surgical automation and visual feedback. In this study we investigate localization and autonomous robotic control of needles in the context of our magneto-suturing system. Our system holds the potential for surgical manipulation with the benefit of minimal invasiveness and reduced patient side effects. However, the nonlinear magnetic fields produce unintuitive forces and demand delicate position-based control that exceeds the capabilities of direct human manipulation. This makes automatic needle localization a necessity. Our localization method combines neural network-based segmentation and classical techniques, and we are able to consistently locate our needle with 0.73 mm RMS error in clean environments and 2.72 mm RMS error in challenging environments with blood and occlusion. The average localization RMS error is 2.16 mm for all environments we used in the experiments. We combine this localization method with our closed-loop feedback control system to demonstrate the further applicability of localization to autonomous control. Our needle is able to follow a running suture path in (1) no blood, no tissue; (2) heavy blood, no tissue; (3) no blood, with tissue; and (4) heavy blood, with tissue environments. The tip position tracking error ranges from 2.6 mm to 3.7 mm RMS, opening the door towards autonomous suturing tasks.

Backward Planning for a Multi-Stage Steerable Needle Lung Robot

Lung cancer is one of the deadliest types of cancer, and early diagnosis is crucial for successful treatment. Definitively diagnosing lung cancer typically requires biopsy, but current approaches either carry a high procedural risk for the patient or are incapable of reaching many sites of clinical interest in the lung. We present a new sampling-based planning method for a steerable needle lung robot that has the potential to accurately reach targets in most regions of the lung. The robot comprises three stages: a transorally deployed bronchoscope, a sharpened piercing tube (to pierce into the lung parenchyma from the airways), and a steerable needle able to navigate to the target. Planning for the sequential deployment of all three stages under health safety concerns is a challenging task, as each stage depends on the previous one. We introduce a new backward planning approach that starts at the target and advances backwards toward the airways with the goal of finding a piercing site reachable by the bronchoscope. This new strategy enables faster performance by iteratively building a single search tree during the entire computation period, whereas previous forward approaches have relied on repeating this expensive tree construction process many times. Additionally, our method further reduces runtime by employing biased sampling and sample rejection based on geometric constraints. We evaluate this approach using simulation-based studies in anatomical lung models. We demonstrate in comparison with existing techniques that the new approach (i) is more likely to find a path to a target, (ii) is more efficient by reaching targets more than 5 times faster on average, and (iii) arrives at lower-risk paths in shorter time.

Beyond Constant Curvature: A New Mechanics Model for Unidirectional Notched-Tube Continuum Wrists

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 article, 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.

Decoupling Steerability from Diameter: Helical Dovetail Laser Patterning for Steerable Needles

The maximum curvature of a steerable needle in soft tissue is highly sensitive to needle shaft stiffness, which has motivated use of small diameter needles in the past. However, desired needle payloads constrain minimum shaft diameters, and shearing along the needle shaft can occur at small diameters and high curvatures. We provide a new way to adjust needle shaft stiffness (thereby enhancing maximum curvature, i.e. "steerability") at diameters selected based on needle payload requirements. We propose helical dovetail laser patterning to increase needle steerability without reducing shaft diameter. Experiments in phantoms and ex vivo animal muscle, brain, liver, and inflated lung tissues demonstrate high steerability in soft tissues. These experiments use needle diameters suitable for various clinical scenarios, and which have been previously limited by steering challenges without helical dovetail patterning. We show that steerable needle targeting remains accurate with established controllers and demonstrate interventional payload delivery (brachytherapy seeds and radiofrequency ablation) through the needle. Helical dovetail patterning decouples steerability from diameter in needle design. It enables diameter to be selected based on clinical requirements rather than being carefully tuned to tissue properties. These results pave the way for new sensors and interventional tools to be integrated into high-curvature steerable needles.

Through the Eustachian Tube and Beyond: A New Miniature Robotic Endoscope to See Into The Middle Ear

This paper presents a novel miniature robotic endoscope that is small enough to pass through the Eustachian tube and provide visualization of the middle ear (ME). The device features a miniature bending tip previously conceived of as a small-scale robotic wrist that has been adapted to carry and aim a small chip-tip camera and fiber optic light sources. The motivation for trans-Eustachian tube ME inspection is to provide a natural-orifice-based route to the ME that does not require cutting or lifting the eardrum, as is currently required. In this paper, we first perform an analysis of the ME anatomy and use a computational design optimization platform to derive the kinematic requirements for endoscopic inspection of the ME through the Eustachian tube. Based on these requirements, we fabricate the proposed device and use it to demonstrate the feasibility of ME inspection in an anthropomorphic model, i.e. a 3D-printed ME phantom generated from patient image data. We show that our prototype provides > 74% visibility coverage of the sinus tympani, a region of the ME crucial for diagnosis, compared to an average of only 6.9% using a straight, non-articulated endoscope through the Eustachian Tube.