As surgical interventions trend towards minimally invasive approaches, Concentric Tube Robots (CTRs) have been explored for various interventions such as brain, eye, fetoscopic, lung, cardiac and prostate surgeries. Arranged concentrically, each tube is rotated and translated independently to move the robot end-effector position, making kinematics and control challenging. Classical model-based approaches have been previously investigated with developments in deep learning based approaches outperforming more classical approaches in both forward kinematics and shape estimation. We propose a deep reinforcement learning approach to control where we generalise across two to four systems, an element not yet achieved in any other deep learning approach for CTRs. In this way we explore the likely robustness of the control approach. Also investigated is the impact of rotational constraints applied on tube actuation and the effects on error metrics. We evaluate inverse kinematics errors and tracking error for path following tasks and compare the results to those achieved using state of the art methods. Additionally, as current results are performed in simulation, we also investigate a domain transfer approach known as domain randomization and evaluate error metrics as an initial step towards hardware implementation. Finally, we compare our method to a Jacobian approach found in literature.

翻译：随着外科手术趋向微创化，同心管机器人（CTR）已被探索用于多种手术干预，如脑部、眼科、胎儿镜、肺部、心脏和前列腺手术。这些管道以同心方式排列，每根管道独立旋转和平移以改变机器人末端执行器的位置，这使得运动学和控制具有挑战性。此前已有基于经典模型的方法研究，而基于深度学习的方法在前向运动学和形状估计方面均超越了经典方法。我们提出一种深度强化学习方法以实现控制，该方法能够推广到两至四管系统，这一特性尚未在其他任何CTR深度学习方法中实现。通过这种方式，我们探索了该控制方法可能的鲁棒性。同时，我们还研究了施加于管道驱动的旋转约束对误差指标的影响。我们评估了路径跟踪任务的逆运动学误差和跟踪误差，并将结果与使用最先进方法获得的结果进行比较。此外，由于当前结果基于仿真环境，我们还研究了一种称为域随机的域迁移方法，并评估误差指标作为向硬件实现迈出的初步步骤。最后，我们将我们的方法与文献中的雅可比方法进行了对比。