We present a prototype of a flexible nitinol needle (φ 1.0 mm and length 172 mm) integrated with an array of 12 Fiber Bragg Grating (FBG) sensors. These sensors measure the axial strain, which enables the computation of the needle curvature. We reconstruct the three-dimensional (3-D) needle shape from the curvature. Experiments are performed where the needle is deflected in free space. The maximum errors between the experiments and beam theory-based model are 0.20 mm (in-plane deflection with single bend), 0.51 mm (in-plane deflection with double bend), and 1.66 mm (out-of-plane). We also describe kinematics-based and mechanics-based models for predicting the 3-D needle shape during insertion into soft tissue. We perform experiments where the needle is inserted into a soft-tissue simulant, and the 3-D needle shape is reconstructed using the FBG sensors. We compare the reconstructed needle shape to deflection obtained from camera images and our models. The maximum error between the experiments and the camera images is 0.74 mm. The maximum errors between the kinematics-based and mechanics-based models and the camera images are 3.77 mm and 2.20 mm, respectively. This study demonstrates that deflection models and needles integrated with FBG sensors have the potential to be used in combination with clinical imaging modalities in order to enable accurate needle steering.

Needle insertion procedures are commonly used for diagnostic and therapeutic purposes. In this paper, an imageguided control system is developed to robotically steer flexible needles with an asymmetric tip. Knowledge about needle deflection is required for accurate steering. Two different models to predict needle deflection are presented. The first is a kinematics-based model, and the second model predicts needle deflection that is based on the mechanics of needle-tissue interaction. Both models predict deflection of needles that undergo multiple bends. The maximum targeting errors of the kinematics-based and the mechanics-based models for 110-mm insertion distance using a φ 0.5-mm needle are 0.8 and 1.7 mm, respectively. The kinematics-based model is used in the proposed image-guided control system. The control system accounts for target motion during the insertion procedure by detecting the target position in each image frame. Five experimental cases are presented to validate the real-time control system using both camera and ultrasound images as feedback. The experimental results show that the targeting errors of camera and ultrasound image-guided steering toward a moving target are 0.35 and 0.42 mm, respectively. The targeting accuracy of the algorithm is sufficient to reach the smallest lesions (φ 2 mm) that can be detected using the state-of-the-art ultrasound imaging systems.

Accurate closed-loop control of continuum manipulators requires integration of both models that describe their motion and methods to evaluate manipulator shape. This work presents a model that approximates the continuous shape of a continuum manipulator by a serial chain of rigid links, connected by flexible rotational joints. This rigid-link model permits a description of manipulator shape under different loading conditions. A kinematic controller, based on the manipulator Jacobian of the proposed rigid-link model, is implemented and realizes trajectory tracking, while using the kinematic redundancy of the manipulator to perform a secondary task of avoiding obstacles. The controller is evaluated on an experimental testbed, consisting of a planar tendon-driven continuum manipulator with two bending segments. Fiber Bragg grating (FBG) sensors are used to reconstruct 3-D manipulator shape, and is used as feedback for closed-loop control of the manipulator. Manipulator steering is evaluated for two cases: the first case involving steering around a static obstacle and the second case involving steering along a straight path while avoiding a moving obstacle. Mean trajectory tracking errors are 0.24 and 0.09 mm with maximum errors of 1.37 and 0.52 mm for the first and second cases, respectively. Finally, we demonstrate the possibility of FBG sensors to measure interaction forces, while simultaneously using them for shape sensing.

Flexible minimally invasive surgical instruments can be used to target difficult-to-reach locations within the human body. Accurately steering these instruments requires information about the three-dimensional shape of the instrument. In the current study, we use an array of Fiber Bragg Grating (FBG) sensors to reconstruct the shape of a flexible instrument. FBG sensors have several advantages over existing imaging modalities, which makes them well-suited for use in a clinical environment. An experimental testbed is presented in this study, which includes a tendon-driven manipulator. A nitinol FBG-wire is fabricated, on which an array of twelve FBG sensors are integrated, and distributed over four different sets. This wire is positioned in the backbone of the manipulator. Axial strains are measured using the FBG sensors, from which the curvature of the manipulator is calculated. The three-dimensional manipulator shape is reconstructed from the curvature, which is used to steer the manipulator tip. We are able to steer the manipulator along various trajectories (twodimensional and three-dimensional), and also reject disturbance loads. We observe a minimum mean tracking error of 0.67 mm for the circular trajectory in closed-loop control. This study demonstrates the potential of steering flexible minimally invasive surgical instruments using an array of FBG sensors.

Needle insertions are common during surgical procedures. Accurately delivering the needle at a specific location in the human body is of importance for the clinical outcome of the procedure. Studies have already shown that robotically inserting traditional needles with a bevel tip can improve targeting accuracy. However, steering of such needles requires spinning the needle, which may lead to additional tissue damage. Therefore, we propose a novel design consisting of a flexible needle with a tendon-driven actuated-tip. Changing the orientation of the actuated-tip allows to control the steering direction of the needle and the amount of deflection. We derive the kinematic model which describes the needle path given the actuated-tip orientation based on nonholonomic kinematics. We present a method for steering the needle towards a target location in soft tissue. This method incorporates online parameter estimation in order to adapt for changes in tissue stiffness. Needle insertion experiments are performed in softtissue simulants, made from porcine gelatin. Needle tip pose is measured during insertion using Fiber Bragg Grating (FBG) based shape reconstruction. Results show that the needle can be steered towards targets located at 20 mm from the initial insertion axis, at a depth of 100 mm with a mean targeting error of 2.02 mm.