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 and safety issues arising from the interaction between the catheter and the aorta. Recently, endovascular simulators have been employed for medical training but generally do not conform to autonomous catheterization. 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 a state-of-the-art endovascular robot. We then provide the capability of real-time force sensing between the catheter and the aorta in simulation. Furthermore, we validate our simulator by conducting two different catheterization tasks using two popular reinforcement learning algorithms. The experimental results show that our open-source simulator can mimic the behaviour of real-world endovascular robots and facilitate the development of different autonomous catheterization tasks. Our simulator is publicly available at https://github.com/robotvisionlabs/cathsim.

翻译：自主机器人在血管内手术中有望安全可靠地导航循环系统，同时降低人为失误的风险。然而，训练这类机器人面临诸多挑战，例如训练周期长以及导管与主动脉交互引发的安全问题。近年来，血管内模拟器已用于医学培训，但通常不兼容自主导管插入操作。此外，现有模拟器大多为闭源，阻碍了安全可靠自主系统的协作开发。本研究提出CathSim，一个加速自主血管内导航机器学习算法开发的开源仿真环境。我们首先采用最先进的血管内机器人技术，对高保真导管和主动脉进行仿真建模。随后，在仿真中实现了导管与主动脉之间实时力感知功能。进一步地，我们通过两种主流强化学习算法执行两项不同的导管插入任务来验证模拟器性能。实验结果表明，本开源模拟器能有效复现真实血管内机器人的行为特性，并促进不同自主导管插入任务的开发。该模拟器已在https://github.com/robotvisionlabs/cathsim公开提供。