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：随着外科手术趋于微创化，同心管机器人（CTRs）已被探索用于多种手术干预，包括脑部、眼部、胎儿镜、肺部、心脏及前列腺手术。各管道同轴排列，通过独立旋转和平移每根管道来调整机器人末端执行器位置，这使得运动学与控制极具挑战性。此前研究已探索了基于经典模型的算法，而基于深度学习的方法在正向运动学和形态估计方面均优于经典算法。我们提出了一种深度强化学习方法用于控制，该方法可泛化至二至四管系统——这是目前其他CTR深度学习方法尚未实现的技术。通过这种方式，我们探讨了控制方法的潜在鲁棒性。同时研究了管道驱动中的旋转约束对误差指标的影响。我们评估了逆向运动学误差与路径跟踪任务的跟踪误差，并将结果与使用最先进方法获得的数据进行对比。此外，由于当前结果均在仿真环境中实现，我们进一步研究了领域迁移方法（即领域随机化），并评估误差指标作为向硬件实现过渡的初步尝试。最后，我们将所提方法与文献中的雅可比方法进行了比较。