Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialities. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to re-purpose decommissioned da Vinci surgical systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this paper, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain this initiative and build upon it

CathSim: An Open-source Simulator for Autonomous Cannulation

Autonomous robots in endovascular operations have the potential to navigate circulatory systems safely and reliably while decreasing the susceptibility to human errors. However, there are numerous challenges involved with the process of training such robots such as long training duration due to sample inefficiency of machine learning algorithms and safety issues arising from the interaction between the catheter and the endovascular phantom. Physics simulators have been used in the context of endovascular procedures, but they are typically employed for staff training and generally do not conform to the autonomous cannulation goal. Furthermore, most current simulators are closed-source which hinders the collaborative development of safe and reliable autonomous systems. In this work, we introduce CathSim, an open-source simulation environment that accelerates the development of machine learning algorithms for autonomous endovascular navigation. We first simulate the high-fidelity catheter and aorta with the state-of-the-art endovascular robot. We then provide the capability of real-time force sensing between the catheter and the aorta in the simulation environment. We validate our simulator by conducting two different catheterisation tasks within two primary arteries using two popular reinforcement learning algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). The experimental results show that using our open-source simulator, we can successfully train the reinforcement learning agents to perform different autonomous cannulation tasks

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

Reliability Assessment Model for Industrial Control System Based on Belief Rule Base

This paper establishes a novel reliability assessment method for industrial control system (ICS). Firstly, the qualitative and quantitative information were integrated by evidential reasoning(ER) rule. Then, an ICS reliability assessment model was constructed based on belief rule base (BRB). In this way, both expert experience and historical data were fully utilized in the assessment. The model consists of two parts, a fault assessment model and a security assessment model. In addition, the initial parameters were optimized by covariance matrix adaptation evolution strategy (CMA-ES) algorithm, making the proposed model in line with the actual situation. Finally, the proposed model was compared with two other popular prediction methods through case study. The results show that the proposed method is reliable, efficient and accurate, laying a solid basis for reliability assessment of complex ICSs

Robot Assisted Object Manipulation for Minimally Invasive Surgery

Robotic systems have an increasingly important role in facilitating minimally invasive surgical treatments. In robot-assisted minimally invasive surgery, surgeons remotely control instruments from a console to perform operations inside the patient. However, despite the advanced technological status of surgical robots, fully autonomous systems, with decision-making capabilities, are not yet available. In 2017, a structure to classify the research efforts toward autonomy achievable with surgical robots was proposed by Yang et al. Six different levels were identified: no autonomy, robot assistance, task autonomy, conditional autonomy, high autonomy, and full autonomy. All the commercially available platforms in robot-assisted surgery is still in level 0 (no autonomy). Despite increasing the level of autonomy remains an open challenge, its adoption could potentially introduce multiple benefits, such as decreasing surgeons’ workload and fatigue and pursuing a consistent quality of procedures. Ultimately, allowing the surgeons to interpret the ample and intelligent information from the system will enhance the surgical outcome and positively reflect both on patients and society. Three main aspects are required to introduce automation into surgery: the surgical robot must move with high precision, have motion planning capabilities and understand the surgical scene. Besides these main factors, depending on the type of surgery, there could be other aspects that might play a fundamental role, to name some compliance, stiffness, etc. This thesis addresses three technological challenges encountered when trying to achieve the aforementioned goals, in the specific case of robot-object interaction. First, how to overcome the inaccuracy of cable-driven systems when executing fine and precise movements. Second, planning different tasks in dynamically changing environments. Lastly, how the understanding of a surgical scene can be used to solve more than one manipulation task. To address the first challenge, a control scheme relying on accurate calibration is implemented to execute the pick-up of a surgical needle. Regarding the planning of surgical tasks, two approaches are explored: one is learning from demonstration to pick and place a surgical object, and the second is using a gradient-based approach to trigger a smoother object repositioning phase during intraoperative procedures. Finally, to improve scene understanding, this thesis focuses on developing a simulation environment where multiple tasks can be learned based on the surgical scene and then transferred to the real robot. Experiments proved that automation of the pick and place task of different surgical objects is possible. The robot was successfully able to autonomously pick up a suturing needle, position a surgical device for intraoperative ultrasound scanning and manipulate soft tissue for intraoperative organ retraction. Despite automation of surgical subtasks has been demonstrated in this work, several challenges remain open, such as the capabilities of the generated algorithm to generalise over different environment conditions and different patients

Large space structures and systems in the space station era: A bibliography with indexes (supplement 03)

Bibliographies and abstracts are listed for 1221 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1991 and June 30, 1991. Topics covered include large space structures and systems, space stations, extravehicular activity, thermal environments and control, tethering, spacecraft power supplies, structural concepts and control systems, electronics, advanced materials, propulsion, policies and international cooperation, vibration and dynamic controls, robotics and remote operations, data and communication systems, electric power generation, space commercialization, orbital transfer, and human factors engineering