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

同心管机器人和其他连续体机器人控制的深度学习研究

• 批准号: 2338607
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2338607
• 关键词: Research; into; Deep; Learning; Control

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.

同心管机器人（CTR）是连续体机器人的一种形式。它们是通过嵌套许多预弯曲的镍钛诺管而创建的。由于系统规模较小，它们可能对许多干预措施非常有益。还因为系统末端执行器的高度灵巧性以及创建蛇状形状的能力。相对于当前的技术水平，在特定干预措施（例如眼科领域）中产生更高的有效性。机器人系统通过以给定方式平移或旋转管来控制。然而，由于内摩擦张力和其他力导致运动学的复杂性。此外，外力的增加也增加了所需控制的复杂性。我建议研究使用多种不同的机器学习方法来取代当前使用的模型。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，立体可见光相机和电磁跟踪器。我还将致力于实施一种在 CTR 沿线的多个位置施加力的方法。除此之外，我还将完成对该领域当前最新技术水平的研究。这包括验证 A. Kuntz、R. Grassmann 和 K. Iyengar 的模型。每个人都以不同的方法看待 CTR 运动学，从强化学习到完全连接的层。然后，我将在此基础上创建一个网络，该网络也可以使用超参数和外力作为输入。创建一个更完整的介入模型。利用从这两层中学到的知识，我计划将我的模型转换为使用触觉设备实时控制 CTR。这是远程操作系统的基本要求。这将用于 VIPER 等系统，这是 RVIM 实验室创建的 CTR。其最终用途将用于眼间手术。