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.

Concentric tube robots are tentacle-like medical robots that can bend around anatomical obstacles to access hard-to-reach clinical targets. The component tubes of these robots can be swapped prior to performing a task in order to customize the robot's behavior and reachable workspace. Optimizing a robot's design by appropriately selecting tube parameters can improve the robot's effectiveness on a procedure-and patient-specific basis. In this paper, we present an algorithm that generates sets of concentric tube robot designs that can collectively maximize the reachable percentage of a given goal region in the human body. Our algorithm combines a search in the design space of a concentric tube robot using a global optimization method with a sampling-based motion planner in the robot's configuration space in order to find sets of designs that enable motions to goal regions while avoiding contact with anatomical obstacles. We demonstrate the effectiveness of our algorithm in a simulated scenario based on lung anatomy.

Lung cancer is the leading cause of cancer-related death, and early-stage diagnosis is critical to survival. Biopsy is typically required for a definitive diagnosis, but current low-risk clinical options for lung biopsy cannot access all biopsy sites. We introduce a motion planner for a multilumen transoral lung access system, a new system that has the potential to perform safe biopsies anywhere in the lung, which could enable more effective early-stage diagnosis of lung cancer. The system consists of three stages in which a bronchoscope is deployed transorally to the lung, a concentric tube robot pierces through the bronchial tubes into the lung parenchyma, and a steerable needle deploys through a properly oriented concentric tube and steers through the lung parenchyma to the target site while avoiding anatomical obstacles such as significant blood vessels. A sampling-based motion planner computes actions for each stage of the system and considers the coupling of the stages in an efficient manner. We demonstrate the motion planner's fast performance and ability to compute plans with high clearance from obstacles in simulated anatomical scenarios.

Concentric tube robots may enable new, safer minimally invasive surgical procedures by moving along curved paths to reach difficult-to-reach sites in a patient's anatomy. Operating these devices is challenging due to their complex, unintuitive kinematics and the need to avoid sensitive structures in the anatomy. In this paper, we present a motion planning method that computes collision-free motion plans for concentric tube robots at interactive rates. Our method's high speed enables a user to continuously and freely move the robot's tip while the motion planner ensures that the robot's shaft does not collide with any anatomical obstacles. Our approach uses a highly accurate mechanical model of tube interactions, which is important since small movements of the tip position may require large changes in the shape of the device's shaft. Our motion planner achieves its high speed and accuracy by combining offline precomputation of a collision-free roadmap with online position control. We demonstrate our interactive planner in a simulated neurosurgical scenario where a user guides the robot's tip through the environment while the robot automatically avoids collisions with the anatomical obstacles.

We present a new method for continuously and accurately estimating the shape of a continuum robot during a medical procedure using a small number of X-ray projection images (e.g., radiographs or fluoroscopy images). Continuum robots have curvilinear structure, enabling them to maneuver through constrained spaces by bending around obstacles. Accurately estimating the robot's shape continuously over time is crucial for the success of procedures that require avoidance of anatomical obstacles and sensitive tissues. Online shape estimation of a continuum robot is complicated by uncertainty in its kinematic model, movement of the robot during the procedure, noise in X-ray images, and the clinical need to minimize the number of X-ray images acquired. Our new method integrates kinematics models of the robot with data extracted from an optimally selected set of X-ray projection images. Our method represents the shape of the continuum robot over time as a deformable surface which can be described as a linear combination of time and space basis functions. We take advantage of probabilistic priors and numeric optimization to select optimal camera configurations, thus minimizing the expected shape estimation error. We evaluate our method using simulated concentric tube robot procedures and demonstrate that obtaining between 3 and 10 images from viewpoints selected by our method enables online shape estimation with errors significantly lower than using the kinematic model alone or using randomly spaced viewpoints.

In this paper, a physics-based framework is presented to visualize the human tongue deformation. The tongue is modeled with the Finite Element Method (FEM) and driven by the motion capture data gathered during speech production. Several novel deformation visualization techniques are presented for in-depth data analysis and exploration. To reveal the hidden semantic information of the tongue deformation, we present a novel physics-based volume segmentation algorithm. This is accomplished by decomposing the tongue model into segments based on its deformation pattern with the computation of deformation subspaces and fitting the target deformation locally at each segment. In addition, the strain energy is utilized to provide an intuitive low-dimensional visualization for the high-dimensional sequential motion. Energy-interpolation-based morphing is also equipped to effectively highlight the subtle differences of the 3D deformed shapes without any visual occlusion. Our experimental results and analysis demonstrate the effectiveness of this framework. The proposed methods, though originally designed for the exploration of the tongue deformation, are also valid for general deformation analysis of other shapes.

We introduce a method for task-oriented design of concentric tube robots, which are tentacle-like robots with the potential to enable new minimally invasive surgical procedures. Our objective is to create a robot design on a patient-specific and surgery-specific basis to enable the robot to reach multiple clinically relevant sites while avoiding anatomical obstacles. Our method uses a mechanically accurate model of concentric tube robot kinematics that considers a robot's time-varying shape throughout the performance of a task. Our method combines a search over a robot's design space with sampling-based motion planning over its configuration space to compute a design under which the robot can feasibly perform a specified task without damaging surrounding tissues. To accelerate the algorithm, we leverage design coherence, the observation that collision-free configuration spaces of robots of similar designs are similar. If a solution exists, our method is guaranteed, as time is allowed to increase, to find a design and corresponding feasible motion plan. We provide examples illustrating the importance of using mechanically accurate models during design and motion planning and demonstrating our method's effectiveness in a medically motivated simulated scenario involving navigation through the lung.

Concentric tube robots have the potential to enable new minimally invasive surgical procedures by curving around anatomical obstacles to reach difficult-to-reach sites in body cavities. Planning motions for these devices is challenging in part due to their complex kinematics; concentric tube robots are composed of thin, pre-curved, telescoping tubes that can achieve a variety of shapes via extension and rotation of each of their constituent tubes. We introduce a new motion planner to maneuver these devices to clinical targets while minimizing the probability of colliding with anatomical obstacles. Unlike prior planners for these devices, we more accurately model device shape using mechanics-based models that consider torsional interaction between the tubes. We also account for the effects of uncertainty in actuation and predicted device shape. We integrate these models with a sampling-based approach based on the Rapidly-Exploring Roadmap to guarantee finding optimal plans as computation time is allowed to increase. We demonstrate our motion planner in simulation using a variety of evaluation scenarios including an anatomy-based neurosurgery case that requires maneuvering to a difficult-to-reach brain tumor at the skull base.

We presents a spectral approach to simulate hybrid objects with deformable and rigid materials in real time. This framework is able to handle large-scale model under the help of GPU's parallel computation.

Abstract A novel framework is proposed in this paper to simulate hybrid solids with deformable and rigid materials in real‐time. Both types of materials are uniformly integrated into one spectral simulator. Based on the modal warping technique, we employ a new constraint strategy which eliminates the accumulation of approximation errors at the boundary interfaces, thus naturally gluing different materials. We also utilize the GPU to accelerate the run‐time computation when updating the geometry of the hybrid solid—the most expensive step in this framework. This work provides a general‐purpose solution of simulating hybrid objects in real‐time, even for large‐scale models. Copyright © 2010 John Wiley & Sons, Ltd.