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

The i²Snake robot for endoscopic surgery

Minimally invasive surgery and more specifically endoscopic surgery is a surgical technique aiming to improve patient treatment and recovery by using natural orifices rather than incisions. However, this is at the cost of increased complexity for the surgeon and limits the number of procedures that can be performed. The recent introduction of robotic technology in the operating room has allowed to overcome most of the challenges faced during minimally invasive surgery, but further research needs to be done to develop robotic endoscopy. This thesis introduces a novel endoscopic robot called the i²Snake, an acronym for intuitive, imaging, sensing, navigated, and kinetically enhanced. The i²Snake is a snake-like robot with a fully actuated body and four instrument channels: 1x for a camera with light, 2x for a pair of robotic instruments and 1x for suction/irrigation. Several aspects of the design and control of the system are tackled in this thesis. Firstly, the state-of-the-art in medical robotics is studied in detail to determine the current trends and identify the existing gaps and needs for such robotic platforms. This is followed by a chapter on the mechanical design of the i²Snake. The design proposes an optimized rolling-joint to improve stability and joint range. The robot also uses a novel tendon routing to minimize cross-talk between joints and to allow formation of complex configurations such as S-shapes. The third chapter focuses on the design of the robotic instruments. This is accomplished by using recorded surgical trajectories combined with a genetic algorithm to automatically design an optimized pair of instruments. The fourth and fifth chapters focus on teleoperation and navigation to ensure intuitive full-body shape control and navigation. Finally, the last chapter discusses the operating room ergonomics and introduces novel features for safe and intuitive manipulation of the robotic platform.Open Acces