Research into Deep Learning for the Control of Concentric Tube Robots and other Continuum Based Robots

同心管机器人（CTR）是连续体机器人的一种形式。它们是通过嵌套多个预弯镍钛合金管而形成的。由于系统规模小，它们可能对一些干预措施非常有益。还因为该系统在末端执行器处的高度灵活性和创建蛇形形状的能力。相对于现有技术，在特定的介入中，例如在眼科领域中，产生更高的效率。通过以给定的方式平移或旋转管来控制机器人系统。但是由于运动学的复杂性，由于内部摩擦力、张力等力的作用.此外，外力的增加增加了所需控制的复杂性。我建议研究使用一些不同的机器学习方法来取代目前使用的模型。第一步是创建和验证能够记录准确数据集的系统。我将使用两种方法来收集数据，立体可见光相机和EM跟踪器。我还将致力于实施一种方法，在多个位置施加力沿着的CTR长度。沿着这一边，我将完成对这一领域目前最先进技术的研究。这包括A的验证。孔茨河Grassmann和K.艾扬格的模特每个人都以不同的方法查看CTR Kinetics，从强化学习到完全连接的层。然后，我将在此基础上构建一个网络，该网络也将使用超参数和外部力量作为输入。创建一个更完整的模型用于介入治疗。利用我从这2层学到的知识，我计划将我的模型转换为使用触觉设备实时控制CTR。这是遥操作系统的基本要求。这将用于VIPER等系统，VIPER是RVIM实验室创建的CTR。其最终用途将用于眼间手术。

Concentric Tube Robots (CTRs) are a form of continuum robotics. They are created by nesting a number of pre-curved Nitinol tubes. They could be highly beneficial for a number of interventions due to the small size of the system. Also because of the system's high dexterity at the end effector and ability to create a snake like shape. Producing a higher effectiveness in particular interventions, such as in the field of ophthalmology, relative to the current state of the art. The robotic system is controlled by translating or rotating the tubes in a given manner. However due to the complexity of the kinematics due to internal friction tension and other forces. Also the addition of external forces increases the complexity of the control needed. I am proposing research into the use of a number of different Machine Learning methodologies to replace the current models used. The first steps of this is the creation and validation of a system which is able to record accurate datasets. I will use 2 methods to collect data, stereoscopic visual light camera and EM trackers. I will also work on the implementation of a method to apply forces at multiple locations along the length of the CTR. Along side this, I will be completing research into the current state of the art in this field. This includes the validation of A. Kuntz, R. Grassmann and K. Iyengar's models. Each looking at the CTR Kinematics in a different method ranging from reinforcement learning to fully connected layers. I will then build on this to make a network which will also work with hyper parameters and external forces as an input. Creating a more complete model for interventional use.Taking my knowledge learned from these 2 layers I plan to translate my models to control a CTR in realtime with the use of a haptic device. This is the base requirement of tele-operated system. This will be used for such systems as VIPER, which is a CTR created by RVIM lab. Its end usage will be for interocular surgery.