Multi-objective particle swarm optimization for the structural design of concentric tube continuum robots for medical applications

Concentric tube robots belong to the class of continuum robotic systems whose morphology is described by continuous tangent curvature vectors. They are composed of multiple, interacting tubes nested inside one another and are characterized by their inherent flexibility. Concentric tube continuum robots equipped with tools at their distal end have high potential in minimally invasive surgery. Their morphology enables them to reach sites within the body that are inaccessible with commercial tools or that require large incisions. Further, they can be deployed through a tight lumen or follow a nonlinear path. Fundamental research has been the focus during the last years bringing them closer to the operating room. However, there remain challenges that require attention. The structural synthesis of concentric tube continuum robots is one of these challenges, as these types of robots are characterized by their large parameter space. On the one hand, this is advantageous, as they can be deployed in different patients, anatomies, or medical applications. On the other hand, the composition of the tubes and their design is not a straightforward task but one that requires intensive knowledge of anatomy and structural behavior. Prior to the utilization of such robots, the composition of tubes (i.e. the selection of design parameters and application-specific constraints) must be solved to determine a robotic design that is specifically targeted towards an application or patient. Kinematic models that describe the change in morphology and complex motion increase the complexity of this synthesis, as their mathematical description is highly nonlinear. Thus, the state of the art is concerned with the structural design of these types of robots and proposes optimization algorithms to solve for a composition of tubes for a specific patient case or application. However, existing approaches do not consider the overall parameter space, cannot handle the nonlinearity of the model, or multiple objectives that describe most medical applications and tasks. This work aims to solve these fundamental challenges by solving the parameter optimization problem by utilizing a multi-objective optimization algorithm. The main concern of this thesis is the general methodology to solve for patient- and application-specific design of concentric tube continuum robots and presents key parameters, objectives, and constraints. The proposed optimization method is based on evolutionary concepts that can handle multiple objectives, where the set of parameters is represented by a decision vector that can be of variable dimension in multidimensional space. Global optimization algorithms specifically target the constrained search space of concentric tube continuum robots and nonlinear optimization enables to handle the highly nonlinear elasticity modeling. The proposed methodology is then evaluated based on three examples that include cooperative task deployment of two robotic arms, structural stiffness optimization under the consideration of workspace constraints and external forces, and laser-induced thermal therapy in the brain using a concentric tube continuum robot. In summary, the main contributions are 1) the development of an optimization methodology that describes the key parameters, objectives, and constraints of the parameter optimization problem of concentric tube continuum robots, 2) the selection of an appropriate optimization algorithm that can handle the multidimensional search space and diversity of the optimization problem with multiple objectives, and 3) the evaluation of the proposed optimization methodology and structural synthesis based on three real applications

Implicit active constraints for safe and effective guidance of unstable concentric tube robots

Safe and effective telemanipulation of concentric tube robots is hindered by their complex, non-intuitive kinematics. In order for clinicians to operate these robots naturally, guidance schemes in the form of attractive and repulsive constraints can simplify task execution. The real-time seamless calculation and application of guidance, however, requires computationally efficient algorithms that solve the non-linear inverse kinematics of the robot and guarantee that the commanded robot configuration is stable and sufficiently away from the anatomy. This paper presents a multi-processor framework that allows on-the-fly calculation of optimal safe paths based on rapid workspace and roadmap precomputation. The realtime nature of the developed software enables complex guidance constraints to be implemented with minimal computational overhead. A clinically challenging user study demonstrates that the incorporated guiding constraints are highly beneficial for fast and accurate navigation with concentric tube robots

Design optimization of a contact-aided continuum robot for endobronchial interventions based on anatomical constraints

PURPOSE: A laser-profiled continuum robot (CR) with a series of interlocking joints has been developed in our center to reach deeper areas of the airways. However, it deflects with constant curvature, which thus increases the difficulty of entering specific bronchi without relying on the tissue reaction forces. This paper aims to propose an optimization framework to find the best design parameters for nonconstant curvature CRs to reach distal targets while attempting to avoid the collision with the surrounding tissue. METHODS: First, the contact-aided compliant mechanisms (CCMs) are integrated with the continuum robot to achieve the nonconstant curvature. Second, forward kinematics considering CCMs is built. Third, inverse kinematics is implemented to steer the robot tip toward the desired targets within the confined anatomy. Finally, an optimization framework is proposed to find the best robot design to reach the target with the least collision to the bronchi walls. RESULTS: Experiments are carried out to verify the feasibility of CCMs to enable the nonconstant curvature deflection, and simulations demonstrate a lower cost function value to reach a target for the nonconstant curvature optimized design with respect to the standard constant curvature robot (0.11 vs. 2.66). In addition, the higher capacity of the optimized design to complete the task is validated by interventional experiments using fluoroscopy. CONCLUSION: Results demonstrate the effectiveness of the proposed framework to find an optimized CR with nonconstant curvature to perform safer interventions to reach distal targets

Design and Modeling of Multi-Arm Continuum Robots

Continuum robots are snake-like systems able to deliver optimal therapies to pathologies deep inside the human cavity by following 3D complex paths. They show promise when anatomical pathways need to be traversed thanks to their enhanced flexibility and dexterity and show advantages when deployed in the field of single-port surgery. This PhD thesis concerns the development and modelling of multi-arm and hybrid continuum robots for medical interventions. The flexibility and steerability of the robot’s end-effector are achieved through concentric tube technology and push/pull technology. Medical robotic prototypes have been designed as proof of concepts and testbeds of the proposed theoretical works.System design considers the limitations and constraints that occur in the surgical procedures for which the systems were proposed for. Specifically, two surgical applications are considered. Our first prototype was designed to deliver multiple tools to the eye cavity for deep orbital interventions focusing on a currently invasive intervention named Optic Nerve Sheath Fenestration (ONSF). This thesis presents the end-to-end design, engineering and modelling of the prototype. The developed prototype is the first suggested system to tackle the challenges (limited workspace, need for enhanced flexibility and dexterity, danger for harming tissue with rigid instruments, extensive manipulation of the eye) arising in ONSF. It was designed taking into account the clinical requirements and constraints while theoretical works employing the Cosserat rod theory predict the shape of the continuum end-effector. Experimental runs including ex vivo experimental evaluations, mock-up surgical scenarios and tests with and without loading conditions prove the concept of accessing the eye cavity. Moreover, a continuum robot for thoracic interventions employing push/pull technology was designed and manufactured. The developed system can reach deep seated pathologies in the lungs and access regions in the bronchial tree that are inaccessible with rigid and straight instruments either robotically or manually actuated. A geometrically exact model of the robot that considers both the geometry of the robot and mechanical properties of the backbones is presented. It can predict the shape of the bronchoscope without the constant curvature assumption. The proposed model can also predict the robot shape and micro-scale movements accurately in contrast to the classic geometric model which provides an accurate description of the robot’s differential kinematics for large scale movements

Concentric Tube Robot Design and Optimization Based on Task and Anatomical Constraints

Concentric tube robots are catheter-sized continuum robots that are well suited for minimally invasive surgery inside confined body cavities. These robots are constructed from sets of pre-curved superelastic tubes and are capable of assuming complex 3D curves. The family of 3D curves that the robot can assume depends on the number, curvatures, lengths and stiffnesses of the tubes in its tube set. The robot design problem involves solving for a tube set that will produce the family of curves necessary to perform a surgical procedure. At a minimum, these curves must enable the robot to smoothly extend into the body and to manipulate tools over the desired surgical workspace while respecting anatomical constraints. This paper introduces an optimization framework that utilizes procedureor patient-specific image-based anatomical models along with surgical workspace requirements to generate robot tube set designs. The algorithm searches for designs that minimize robot length and curvature and for which all paths required for the procedure consist of stable robot configurations. Two mechanics-based kinematic models are used. Initial designs are sought using a model assuming torsional rigidity. These designs are then refined using a torsionally-compliant model. The approach is illustrated with clinically relevant examples from neurosurgery and intracardiac surgery

Integrating Optimization and Sampling for Robot Motion Planning with Applications in Healthcare

Robots deployed in human-centric environments, such as a person's home in a home-assistance setting or inside a person's body in a surgical setting, have the potential to have a large, positive impact on human quality of life. However, for robots to operate in such environments they must be able to move efficiently while avoiding colliding with obstacles such as objects in the person's home or sensitive anatomical structures in the person's body. Robot motion planning aims to compute safe and efficient motions for robots that avoid obstacles, but home assistance and surgical robots come with unique challenges that can make this difficult. For instance, many state of the art surgical robots have computationally expensive kinematic models, i.e., it can be computationally expensive to predict their shape as they move. Some of these robots have hybrid dynamics, i.e., they consist of multiple stages that behave differently. Additionally, it can be difficult to plan motions for robots while leveraging real-world sensor data, such as point clouds. In this dissertation, we demonstrate and empirically evaluate methods for overcoming these challenges to compute high-quality and safe motions for robots in home-assistance and surgical settings. First, we present a motion planning method for a continuum, parallel surgical manipulator that accounts for its computationally expensive kinematics. We then leverage this motion planner to optimize its kinematic design chosen prior to a surgical procedure. Next, we present a motion planning method for a 3-stage lung tumor biopsy robot that accounts for its hybrid dynamics and evaluate the robot and planner in simulation and in inflated porcine lung tissue. Next, we present a motion planning method for a home-assistance robot that leverages real-world, point-cloud obstacle representations. We then expand this method to work with a type of continuum surgical manipulator, a concentric tube robot, with point-cloud anatomical representations. Finally, we present a data-driven machine learning method for more accurately estimating the shape of concentric tube robots. By effectively addressing challenges associated with home assistance and surgical robots operating in human-centric environments, we take steps toward enabling robots to have a positive impact on human quality of life.Doctor of Philosoph

Design Optimization Algorithms for Concentric Tube Robots

Concentric tube robots are tentacle-like surgical robots that can bend around anatomical obstacles to access hard-to-reach surgical targets. These robots have potential to enable minimally invasive surgical procedures by allowing physicians to access clinical regions that were previously unreachable using traditional instruments. Concentric tube robots are composed of nested, customizable tubes which undergo complicated mechanical interactions that generate tentacle-like motion. As a consequence of this intricate kinematic mechanism, the physical specifications of each of the robots tubes, i.e. the robot’s design, significantly affect the shapes that the robot can undertake and the regions it can reach. Customizing the design of these robots can potentially facilitate successful surgical procedures on a variety of patients. In this thesis, we present design optimization algorithms to generate appropriate design parameters on an application- and patient-specific basis. We consider three design optimization problems. First, we present a design optimization algorithm that generates a concentric tube robot design under which the robot can maximize the reachable volume of a given goal region in the human body. We provide analysis establishing that our design optimization algorithm for generating a single design is asymptotically optimal. Second, we present an algorithm that computes sets of concentric tube robot designs that can collectively maximize the reachable volume of a given goal region in the human body. Third, we introduce an algorithm that generates the set of designs of minimal size such that the designs in the set can collectively reach a physician-specified percentage of the goal region. Each of our algorithms combines a search in the design space of a concentric tube robot using Adaptive Simulated Annealing with a sampling-based motion planner in the robot’s configuration space in order to find a single or sets of designs that enable paths to the goal regions while avoiding contact with anatomical obstacles. We demonstrate the effectiveness of each of our algorithms in a simulated scenario based on lung anatomy and compare our algorithms’ performance with that of current state-of-the-art design optimization algorithms.Bachelor of Scienc