When designing continuum robots for applications that require sensing, designers are faced with the problems of deciding what sensors to use, where they should be placed, and how best to use the information they provide. In this paper, we describe how a differential representation of a continuum robot's kinematic equations that govern its states (e.g., shape) can be used to simultaneously address these problems under the guidance of statistical state estimation. We identify how state estimation and sensing-system design (i.e., sensor selection and placement) are inherently coupled problems, which leads us to formulate sensing-system design as an optimization problem governed by the results of statistical estimation. As a case-study, the methods described herein are used to design a magnetic sensing system for concentric-tube robots.

We propose a new class of robotic device for minimally-invasive surgery that lies at the intersection of continuum, parallel, and reconfigurable robotics. This Continuum Reconfigurable Incisionless Surgical Parallel (CRISP) paradigm involves the use of multiple needle-diameter devices inserted through the skin and assembled into parallel structures inside the body. The parallel structure can be reconfigured inside the patient's body to satisfy changing task requirements such as reaching initially inaccessible locations or modifying mechanical stiffness for manipulation or palpation. Another potential advantage of the CRISP concept is that many small (needle-sized) entry points into the patient may be preferable in terms of both patient healing and cosmesis to the single (or multiple) larger ports needed to admit current surgical robots. This paper presents a mechanics-based model for CRISP forward and inverse kinematics, along with experimental validation.

Lung cancer is the most deadly form of cancer, and survival depends on early-stage diagnosis and treatment. Transoral access is preferable to traditional between-the-ribs needle insertion because it is less invasive and reduces risk of lung collapse. Yet many sites in the peripheral zones of the lung or distant from the bronchi cannot currently be accessed transorally, due to the relatively large diameter and lack of sufficient steerablity of current instrumentation. To remedy this, we propose a new robotic system that uses a tendon-actuated device (bronchoscope) as a first stage for deploying a concentric tube robot, which itself is a vehicle through which a bevel steered needle can be introduced into the soft tissue of the lung outside the bronchi. In this paper we present the various components of the system and the workflow we envision for deploying the robot to a target using image guidance. We describe initial validation experiments in which we puncture ex vivo bronchial wall tissue and also target a nodule in a phantom with an average final tip error of 0.72 mm.

Concentric tube robots are thin, tentacle-like devices that can move along curved paths and can potentially enable new, less invasive surgical procedures. Safe and effective operation of this type of robot requires that the robot's shaft avoid sensitive anatomical structures (e.g., critical vessels and organs) while the surgeon teleoperates the robot's tip. However, the robot's unintuitive kinematics makes it difficult for a human user to manually ensure obstacle avoidance along the entire tentacle-like shape of the robot's shaft. We present a motion planning approach for concentric tube robot teleoperation that enables the robot to interactively maneuver its tip to points selected by a user while automatically avoiding obstacles along its shaft. We achieve automatic collision avoidance by precomputing a roadmap of collision-free robot configurations based on a description of the anatomical obstacles, which are attainable via volumetric medical imaging. We also mitigate the effects of kinematic modeling error in reaching the goal positions by adjusting motions based on robot tip position sensing. We evaluate our motion planner on a teleoperated concentric tube robot and demonstrate its obstacle avoidance and accuracy in environments with tubular obstacles.

The needle-sized surgical tools used in arthroscopy, otolaryngology, and other surgical fields could become even more valuable to surgeons if endowed with the ability to navigate around sharp corners to manipulate or visualize tissue. We present a needle-sized wrist design that grants this ability. It can be easily interfaced with manual tools or concentric tube robots and is straightforward and inexpensive to manufacture. The wrist consists of a nitinol tube with several asymmetric cutouts, actuated by a tendon. Perhaps counter-intuitively, within this seemingly simple design concept, design optimization is challenging due to the number of parameters available and nonlinearities in material properties. In this paper, we examine a subset of possible geometries and derive kinematic and static models. Experimental results with a 1.16 mm diameter prototype validate the models. Lastly, we provide a discussion summarizing the lessons learned in our early experience designing and fabricating wrists of this type.

Concentric tube robots exhibit complex workspaces due to the way their component tubes bend and twist as they interact with one another. This paper explores ways to compute and characterize their workspaces. We use Monte Carlo random samples of the robot's joint space and a discrete volumetric workspace representation, which can describe both reachability and redundancy. Experiments on two physical prototypes are provided to illustrate the proposed approach.

The high incidence of intracerebral hemorrhages, together with a 40% mortality rate, provide strong motivation for enhancements in the treatment methods available to physicians. To minimize the disruption to healthy brain tissue associated with gaining access to the surgical site that is imposed by traditional open or endoscopic surgical intervention, we propose a new minimally-invasive, image-guided, robotic approach that provides articulation within the lesion at the tip of a needle. In this paper we present a biocompatible and sterilizable robot, together with an image-guidance approach designed to deliver the tip of the needle accurately to the blood clot and to move it within the clot, to aspirate it. An experimental evaluation demonstrates removal of 92% of the target clot tissue in a proof-of-concept phantom study.

Steerable needles can potentially increase the accuracy of needle-based diagnosis and therapy delivery, provided they can be adequately controlled based on medical image information. We propose a novel sliding mode control law that can be used to deliver the tip of a flexible asymmetric-tipped needle to a desired point, or to track a desired trajectory within tissue. The proposed control strategy requires no a priori knowledge of model parameters, has bounded input speeds, and requires little computational resources. We show that if the standard nonholonomic model for tip-steered needles holds, then the control law will converge to desired targets in a reachable workspace, within a tolerance that can be defined by the control parameters. Experimental results validate the control law for target points and trajectory following in phantom tissue and ex vivo liver. Experiments with targets that move during insertion illustrate robustness to disturbances caused by tissue deformation.

We describe transnasal skull base surgery, including the current clinical procedure and the ways in which a robotic system has the potential to enhance the current standard of care. The available workspace is characterized by segmenting medical images and reconstructing the available 3D geometry. We then describe thin, ¿tentacle-like¿ robotic tools with shafts constructed from concentric tube robots, and an actuation unit designed to robotically control them in a teleoperated setting. Lastly, we discuss the results of a proof-of-concept study in a cadaveric specimen, illustrating the ability of the robot to access clinically relevant skull base targets.