Tendon-driven snake-like arms have been used to create highly dexterous continuum robots so that they can bend around anatomical obstacles to access clinical targets. In this paper, we propose a design algorithm for developing patient-specific surgical continuum manipulators optimized for oriental dexterity constrained by task-space obstacles. The algorithm uses a sampling-based approach to finding the dexterity distribution in the workspace discretized by voxels. The oriental dexterity measured in the region of interest in the task-space formed a fitness function to be optimized through differential evolution. This was implemented in the design of a tendon-driven manipulator for knee arthroscopy. The results showed a feasible design that achieves significantly better dexterity than a rigid tool. This highlights the potential of the proposed method to be used in the process of designing dexterous surgical manipulators in the field.
Later in the field, new algorithms appeared that focused more towards on task space reachability in a volume rather than navigation to a point. Among those works were methods that discretised the workspace into voxels and generated an objective function to maximize the coverage of the concentric tube workspace in the region of interest @cite_12 . Other algorithms turned this approach into an occupancy grid map where obstacles were dilated and sampled configurations of a centre-line representation of the robot were used to develop a cost function for motion planning @cite_13 . One algorithm maximized reachability using a sampling-based motion planner to enable motions to a variety of target points in the lung @cite_1 . All of these methods for optimizing continuum robots involve defining a cost function that would be optimized using generic optimization algorithms like generalized pattern search, the Nelder-Mead simplex algorithm and adaptive simulated annealing. These studies provided continuum robot algorithms for better navigation and reachability in patient-specific anatomies but none of them focused on optimizing for dexterity.
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We propose a novel systematic approach to optimizing the design of concentric tube robots for neurosurgical procedures. These procedures require that the robot approach specified target sites while navigating and operating within an anatomically constrained work space. The availability of preoperative imaging makes our approach particularly suited for neurosurgery, and we illustrate the method with the example of endoscopic choroid plexus ablation. A novel parameterization of the robot characteristics is used in conjunction with a global pattern search optimization method. The formulation returns the design of the least-complex robot capable of reaching single or multiple target points in a confined space with constrained optimization metrics. A particular advantage of this approach is that it identifies the need for either fixed-curvature versus variable-curvature sections. We demonstrate the performance of the method in four clinically relevant examples. Concentric tube continuum robots provide an infinite-dimensional design space, consisting of individual tube space curves and other tube parameters. Even when design choices are made to restrict the design space to a small number of discrete parameters, ad hoc selection of parameter values to achieve coverage of a desired volume, in the presence of geometric workspace constraints, is essentially impossible - even for experienced researchers. General design algorithms proposed to date have focused on reaching a discrete set of specific points, and have made non-physical approximations in the robot model (most significantly assuming infinite torsional rigidity), to speed up model computation. In this paper, we extend prior algorithms to use more accurate models and incorporate volume-based objectives. These extensions are illustrated in a case study on the design of a concentric tube robot for endonasal pituitary surgery. We show that volume-based design optimization increases the reachable percentage of the surgical workspace by an average of approximately 50 , in comparison to various sets of manually selected design parameters. We conclude that volume-based objectives should be included in future multi-objective design optimization procedures for concentric tube continuum robots. 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 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 pa- rameters 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. I. INTRODUCTION Concentric tube robots are tentacle-like medical robots that can potentially enable safer minimally invasive interventions at many sites in the human body, including the lungs, the skull base, and the heart (1). These robots are composed of nested nitinol tubes that each are precurved, typically with a straight segment followed by a curved segment. To perform a task, the robot axially rotates and translates each tube relative to one another, causing the entire device's shape to change. Concentric tube robots act like shape-changing robotic needles that can curve around anatomical obstacles (e.g., bones, blood vessels, critical nerves, etc.) to reach clinical targets not easily accessed using traditional straight medical instruments. The curvilinear shapes achievable by concentric tube robots are highly dependent on the component tubes' physi- cal specifications. The design of the concentric tubes, includ- ing the tubes' lengths and precurvatures, affects the robot's workspace and the space of the robot's attainable shapes. Consequently, the design of the concentric tubes determines the set of clinical targets that the robot can safely reach. Even with the shape-changing capabilities of a concentric tube robot (as shown in Fig. 2), due to kinematic constraints