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）是连续机器人的一种形式。它们是通过筑巢的许多预弯曲的Nitinol管来创建的。由于系统的尺寸较小，它们可能对许多干预措施非常有益。同样，由于该系统在末端效应器上的敏捷性高以及创建类似蛇的能力。相对于目前的最新状态，在特定干预措施中产生更高的有效性，例如在眼科领域。机器人系统通过以给定的方式转换或旋转试管来控制。然而，由于内部摩擦张力和其他力引起的运动学的复杂性。外力的增加也增加了所需的控制的复杂性。我建议研究使用许多不同的机器学习方法来替换当前使用的模型。这样做的第一步是能够记录准确数据集的系统的创建和验证。我将使用2种方法收集数据，立体视觉灯光摄像头和EM跟踪器。我还将致力于实施一种方法，以在CTR的长度上在多个位置施加力。在此方面，我将完成对该领域最新技术现状的研究。这包括对A. Kuntz，R。Grassmann和K. Iyengar模型的验证。每种方法都以不同的方法来查看CTR运动学，从增强学习到完全连接的层。然后，我将基于此建立一个网络，该网络也将与超级参数和外部力量一起作为输入。创建一个更完整的介入模型。从这两层中获取我的知识，我计划将模型转换为使用触觉设备实时控制CTR。这是电信系统的基本要求。这将用于诸如Viper之类的系统，该系统是由RVIM实验室创建的CTR。它的最终用途将用于眼内手术。