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

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

Modeling, Sensorization and Control of Concentric-Tube Robots

Since the concept of the Concentric-Tube Robot (CTR) was proposed in 2006, CTRs have been a popular research topic in the field of surgical robotics. The unique mechanical design of this robot allows it to navigate through narrow channels in the human anatomy and operate in highly constrained environments. It is therefore likely to become the next generation of surgical robots to overcome the challenges that cannot be addressed by current technologies. In CSTAR, we have had ongoing work over the past several years aimed at developing novel techniques and technologies for CTRs. This thesis describes the contributions made in this context, focusing primarily on topics such as modeling, sensorization, and control of CTRs. Prior to this work, one of the main challenges in CTRs was to develop a kinematic model that achieves a balance between the numerical accuracy and computational efficiency for surgical applications. In this thesis, a fast kinematic model of CTRs is proposed, which can be solved at a comparatively fast rate (0.2 ms) with minimal loss of accuracy (0.1 mm) for a 3-tube CTR. A Jacobian matrix is derived based on this model, leading to the development of a real-time trajectory tracking controller for CTRs. For tissue-robot interactions, a force-rejection controller is proposed for position control of CTRs under time-varying force disturbances. In contrast to rigid-link robots, instability of position control could be caused by non-unique solutions to the forward kinematics of CTRs. This phenomenon is modeled and analyzed, resulting in design criteria that can ensure kinematic stability of a CTR in its entire workspace. Force sensing is another major difficulty for CTRs. To address this issue, commercial force/torque sensors (Nano43, ATI Industrial Automation, United States) are integrated into one of our CTR prototypes. These force/torque sensors are replaced by Fiber-Bragg Grating (FBG) sensors that are helically-wrapped and embedded in CTRs. A strain-force calculation algorithm is proposed, to convert the reflected wavelength of FBGs into force measurements with 0.1 N force resolution at 100 Hz sampling rate. In addition, this thesis reports on our innovations in prototyping drive units for CTRs. Three designs of CTR prototypes are proposed, the latest one being significantly more compact and cost efficient in comparison with most designs in the literature. All of these contributions have brought this technology a few steps closer to being used in operating rooms. Some of the techniques and technologies mentioned above are not merely limited to CTRs, but are also suitable for problems arising in other types of surgical robots, for example, for sensorizing da Vinci surgical instruments for force sensing (see Appendix A)

Deep Reinforcement Learning for Inverse Kinematics and Path Following for Concentric Tube Robots

Concentric tube robots (CTRs) are continuum robots that allow for bending and twisting motions unattainable by traditional rigid link robots. The curvilinear backbones can benefit surgical applications by improving dexterity, enlarging the workspace, and reducing trauma at the entry point of the instrument. The curvilinear backbone that is attributed is the result of pre-curved, super-elastic tubes arranged concentrically. Each tube has a straight and pre-curved section and is actuated in rotation and translation from the tube base with the neighboring tube interactions producing the curvilinear backbone. The modeling of the neighboring tube interactions is non-trivial, and an explored topic in CTR literature. However, model-based kinematics and control can be inaccurate due to inherent manufacturing errors of the tubes, permanent deformation over time, and unmodelled physical interactions. This thesis proposes a model-free control method using deep reinforcement learning (DRL). The DRL framework aims to control the end-effector of the CTR with limited modeling information by leveraging simulation data, which is much less costly than hardware data. To develop a DRL framework, a Markov Decision Process (MDP) with states, actions, and rewards needs to be defined for the inverse kinematics task. First, action exploration was investigated with this MDP in a simpler simulation as CTRs have a unique extension degree of freedom per tube. Next, state representation, curriculum reward, and adaptation methods over multiple CTR systems were developed in a more accurate simulation. To validate the work in simulation, a noise-induced simulation environment was utilized to demonstrate the initial robustness of the learned policy. Finally, a hardware system was developed where a workspace characterization was performed to determine simulation to hardware differences. By using Sim2Real domain transfer, a simulation policy was successfully transferred to hardware for inverse kinematics and path following, validating the approach

Adaptive locally linear kinematic modelling of concentric tube robots

Concentric tube robots comprise telescopic precurved elastic tubes. The robot's tip and shape are controlled via relative tube motions, i.e. tube rotations and translations. Non-linear interactions between the tubes, e.g. friction and torsion, as well as uncertainty in the physical properties of the tubes themselves, e.g. the Young’s modulus, curvature, or stiffness, hinder accurate kinematic modelling. In this paper, we present a machine-learning-based methodology for kinematic modelling of concentric tube robots and in situ model adaptation. Our approach is based on Locally Weighted Projection Regression (LWPR). The model comprises an ensemble of linear models, each of which locally approximates the original complex kinematic relation. LWPR can accommodate for model deviations by adjusting the respective local models at run-time, resulting in an adaptive kinematics framework. We evaluated our approach on data gathered from a three-tube robot, and report high accuracy across the robot's configuration space

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

Improving Strength and Stability in Continuum Robots

Continuum robots, which are bio-inspired ’trunk-like’ robots, are characterized for their inherent compliance and range of motion. One of the key challenges in continuum robotics research is developing robots with sufficient strength and stability without adding additional weight or complexity to the design. The research conducted in this dissertation encompasses design and modeling strategies that address these challenges in strength and stability. This work improves three continuum robot actuation paradigms: (1) tendon-driven continuum robots (TDCR), (2) concentric tube robots (CTR), and (3) concentric push-pull robots (CPPR). The first chapter of contribution covers strategies for improving strength in TDCRs. The payload capacity and torsional stiffness of the robot can be improved by leveraging the geometry of the backbone design and tendon routing, with design choices experimentally validated on a robot prototype. The second chapter covers a new bending actuator, concentric precurved bellows (CPB), that are based upon CTR actuation. The high torsional stiffness of bellows geometry virtually eliminates the torsional compliance instability found in CTRs. Two bellows designs are developed for 3D printing and the mechanical properties of these designs are characterized through experiments on prototypes and in static finite element analysis. A torsionally rigid kinematic model is derived and validated on 3D printed prototypes. The third chapter of contribution covers the development and validation of a mechanics-based CPPR kinematics model. CPPRs are constructed from concentrically nested, asymmetrically patterned tubes that are fixed together at their distal tips. Relative translations between the tubes induces bending shapes from the robot. The model expands the possible design space of CPPRs by enabling the modeling of external loads, non-planar bending shapes, and CPPRs with more than two tubes. The model is validated on prototypes in loaded and unloaded experiments

Data-driven modelling and control of concentric tube robots with application in distal lung sampling

This research aims to explore the use of Concentric Tube Robots (CTRs) as a novel alternative to needle-based interventions in order to make these procedures more accurate and repeatable. CTRs due to their small footprint, compliance, and dexterity have been proposed for several minimally-invasive robotic surgeries. As a novel flexible robot, it has the potential to reach distal parts of the human lung that are difficult or impossible to reach with conventional needle-based interventions. There are, however, still significant challenges associated with the motion and position control of CTRs. Commonly used model-based control approaches are computationally expensive to solve and often employ simplified geometric/dynamic assumptions, which could be inaccurate in the presence of unmodelled disturbances and external interaction forces. Consequently, this work explores different control strategies to overcome these limitation. This is achieved by first building a simulation environment based on a computationally improved kinematic model that enables real-time control. Then, data-driven control approaches are investigated in order to provide accurate position control in the presence of uncertainties in the system. Finally, a three-phase affordance-aware motion planner is proposed to demonstrate the feasibility of using CTRs for percutaneous lung biopsy. According to this, the first part of this work concentrates on computationally efficient real-time modelling and simulation of CTRs. In order to achieve this, two approaches are taken. The first one introduces a method that can rapidly estimate the solution of the kinematic model, while the second approach focuses on implementing the existing model in a computationally efficient way in Robot Operating System (ROS) using C++. Second, this work explores data-driven solutions to control the robot without relying on the kinematic model. Consequently, two data-driven solutions are proposed, namely the Hybrid Dual Jacobian approach and the Extended Dynamic Mode Decomposition (EDMD) algorithm. The hybrid controller combines the advantages of model-based and data-driven control approaches, while the EDMD provides a completely model-free solution to control the robot. Both controllers are capable of rapidly predicting the robot’s nonlinear dynamics from a limited data set and offer consistent control under external loading and in the presence of obstacles. The third part of the thesis explores the use of CTRs in the context of distal lung sampling. This work demonstrates that CTRs are suitable for Needle-Based Optical Endomicroscopy where a CTR steers a fluorescent imaging probe with cellular and bacterial imaging capability inside soft tissue. Then, it is also demonstrated that a CTR can be used as a Steerable Needle to reach a target deep inside the tissue. To achieve these tasks, a motion planner is essential due to the fact that a CTR is only capable of reaching specific points in its workspace and there are a number of configurations where the robot becomes unstable. Based on this, a threeii phase affordance-aware motion planner algorithm is developed. The motion planner selects the best entry point for a specific task. Based on the selected entry point it first generates a stable trajectory from the robot’s initial configuration to the selected entry point. Then, a feasible trajectory is generated from the entry point to the target. Finally, the proposed datadriven control algorithm is applied to autonomously steer the robot on the generated trajectory toward the target region for endomicroscopic imaging