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.
Optimizing the design for snake-like robotic arms has often involved finding trade-offs in relationships with the mechanical design and its task space performance. The initial studies for optimal snake-like robots involved trade-offs with the workspace and certain mechanical properties like the stiffness in a flexible backbone joint @cite_8 . When the patient-specific paradigm emerged, many literature in the field developed cost-functions to describe trade-offs for designing continuum robots for better workspaces and path-planning in patient-specific anatomical environments. This was strongly evident in the design of concentric tube robots where patient 3D anatomies were collected by Magnetic Resonance Images (MRI) and optimizations were done to find the minimal tube lengths and their curvatures while penalizing collisions in a simulated navigation task to some desired targets @cite_5 @cite_9 . In other literature, a sampling-based motion planning approach to the problem was proposed to avoid complex inverse kinematics calculations @cite_2 .
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Concentric tube robots are a novel continuum robot technology that is well suited to minimally invasive surgeries inside small body cavities such as the heart. These robots are constructed of concentrically combined pre-curved elastic tubes to form 3D curves. Each telescopic section of the robot is either of fixed or variable curvature. One advantage of this approach is that the component tube curvatures, lengths and stiffnesses can easily be fabricated to be procedure- and patient-specific. This paper proposes an optimization framework for solving the robot design problem. Given a 3D description of the constraining anatomy, the number of fixed and variable curvature robot sections and a tip workspace description, the algorithm solves for the robot design that possesses the desired workspace, remains inside the anatomical constraints and minimizes the curvature and length of all sections. The approach is illustrated in the context of beating-heart closure of atrial septal defects. 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. 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.