Diversity Supplement: Enabling advanced endovascular beating-heart procedures through soft robotics

项目摘要/摘要 在心脏跳动过程中，如三尖瓣环减少术，只有视觉反馈提供给 外科医生通过影像和手术工具的准确性依赖于术中的质量 成像。因此，需要安全、先进的方法来监控 带有心脏组织的机器人，确保安全和准确地操作微妙的心脏结构。这项建议 旨在开发一种外部感知探头，检测软机器人施加在心脏结构上的压力 在父母的恩赐和内心的接触力中发展起来。这样的感知能力将补充 在提高机器人在跳动的心脏内定位的准确性方面，传统成像。它还将允许 监测与心脏解剖结构的相互作用力和接触。机器学习(ML)方法将是 被调查以区分和分离各种原始感觉输入，并使用它们来识别组织 探头正在接触中，以便更好地定位、安全，并指导手术。 拟议的研究有两个目标：目标3重点是传感器的设计和探头的制造；目标3 4处理传感器与软可展开机器人的集成、基于ML的感官解释和功能 通过体外和体外试验进行验证。结合外部和内部成像，我们假设 这种传感器的加入将提高心脏不停跳手术过程中的准确性和安全性。至 监测机器人远端施加的压力，将开发一种软压力传感器。小型化软触点 传感器也将集成在机器人上。这些传感器的灵感来自于观察到的毛状纤毛 生物学。类似于悬臂梁的作用，纤毛状延伸部分可以通过流体阻力来偏转，从而产生 并产生与方向相关的电势，以检测流速和方向。我们会 在压力传感器周围开发以纤毛为灵感的传感器，以监控机器人所经历的接触。拿走 多感知模式在外感证明中的优势，我们将研究ML的使用 应用程序可增强软机器人的感知能力，并为外科医生提供组织识别和触觉反馈。 这些额外的部件将改善机器人对周围环境的感知。这将导致1) 通过将车载传感信息与外部成像相结合来提高定位精度，2) 通过监测与周围心脏结构的接触和交换力量，并潜在地提高安全性 为触觉反馈的集成铺平了道路，以及3)进一步提高了控制的准确性 机器人通过利用传感器数据来修正模型估计误差。 1

Project Summary/Abstract In beating heart procedures such as tricuspid annulus reduction, only visual feedback is provided to the surgeon via imaging and the accuracy in operating surgical tools relies on the quality of the intraoperative imaging. Hence, there is a need for safe, advanced methods to monitor position and interaction forces of the robot with the heart tissues to ensure safe and accurate manipulation of delicate heart structures. This proposal aims at developing an exteroceptive probe that detects pressures applied to the heart structures by the soft robot developed in the parent grant and contact forces within the heart. Such sensing capabilities will complement conventional imaging in increasing accuracy in positioning the robot inside the beating heart. It will also allow to monitor interaction forces and contacts with the heart anatomy. Machine learning (ML) methods will be investigated to discriminate and decouple the various raw sensory inputs and use them to identify tissues the probe is in contact with for better localization, safety, and to guide the procedure. The proposed research has two aims: Aim 3 focuses on sensor design and fabrication of the probe; Aim 4 handles sensor integration with the soft deployable robot, sensory interpretation based on ML, and function validation through in-vitro and ex-vivo testing. Combined with external and internal imaging, we hypothesize that the addition of this sensor will increase the accuracy and safety during beating heart surgical procedures. To monitor pressures applied distally by the robot a soft pressure sensor will be developed. Miniaturized soft contact sensors will also be integrated on the robot. These sensors will be inspired by the hair-like cilia observed in biology. Acting similarly to cantilever beams, cilia-like extensions can deflect via fluidic drag forces producing piezoelectric charges and generating a direction-dependent potential to sense flow rate and direction. We will develop cilia-inspired sensors around the pressure sensor to monitor contacts experienced by the robot. Taking advantage of the multiple sensing modalities on the exteroceptive prove, we will investigate the use of ML application to enhance soft robot awareness and provide tissue recognition and haptic feedback to the surgeon. These additional components will improve the robot perception of the surroundings. This will lead to 1) increased accuracy in positioning by integrating the information from onboard sensing with external imaging, 2) increase safety via monitoring contact and exchanged forces with surrounding heart structures and potentially paving the way towards integration of haptic feedback, and 3) further increase the accuracy in the control of the robot by exploiting sensor data to correct model estimation errors. 1