  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.
