NRI: Collaborative Research: Software Framework for Research in Semi-Autonomous Teleoperation

NRI：协作研究：半自主远程操作研究的软件框架

• 批准号: 1637759
• 负责人: Gregory Fischer
• 金额: $ 55.02万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1637759&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Software; Framework

Telemanipulation systems consist of a human interacting with a mechanical device on the master side to operate a robot at the remote side. They provide natural opportunities for research in intelligent human/robot collaboration, but existing commercial systems, used in areas such as telesurgery, are not intelligent and therefore only replicate the actions of the human operator. These systems are also proprietary, expensive, and not available for modification by researchers. The goal of this NRI project is to provide an open-source software infrastructure that is designed to work with a broad range of hardware and simulated devices to enable a larger community to pursue research and education in intelligent telemanipulation at a lower cost.The increasing pace of robotics research can be attributed, at least in part, to the increasing availability of software infrastructure, such as Robot Operating System (ROS), and open hardware platforms. This NRI project focuses on providing a software infrastructure for research in intelligent telemanipulation, leveraging infrastructure developed for the Raven II robot and the da Vinci Research Kit (dVRK) and continuing to extend it to other systems, including simulated robots. The three main tasks are to: (1) engage the community to guide development, (2) develop and implement a common API for the diverse hardware platforms, and (3) provide a set of high-level, platform-independent software modules. The goal is to support research towards semi-autonomous telerobotic systems that can more effectively combine the knowledge, reasoning, and decision-making capabilities of a human with the sensing and manipulation capabilities of a robot.

远程操控系统包括人类与主控端的机械设备交互，以操作远程端的机器人。它们为智能人类/机器人协作的研究提供了自然的机会，但用于远程外科等领域的现有商业系统并不智能，因此只能复制人类操作员的行动。这些系统也是专有的，价格昂贵，研究人员无法对其进行修改。该NRI项目的目标是提供一个开源软件基础设施，旨在与广泛的硬件和模拟设备一起工作，使更大的社区能够以更低的成本从事智能远程操控方面的研究和教育。机器人研究的速度不断加快，至少部分可以归因于软件基础设施的日益可用，如机器人操作系统(ROS)和开放硬件平台。该NRI项目专注于为智能遥控操控研究提供软件基础设施，利用为Raven II机器人和达芬奇研究套件(DVRK)开发的基础设施，并继续将其扩展到其他系统，包括模拟机器人。三项主要任务是：(1)让社区参与指导开发，(2)为不同的硬件平台开发和实现通用API，以及(3)提供一套高级别的、独立于平台的软件模块。其目标是支持对半自动遥控机器人系统的研究，这种系统可以更有效地将人类的知识、推理和决策能力与机器人的感知和操作能力结合起来。

项目成果

Towards a haptic feedback framework for multi-DOF robotic laparoscopic surgery platforms

面向多自由度机器人腹腔镜手术平台的触觉反馈框架

• DOI: 10.1109/iros.2016.7759188
• 发表时间: 2016
• 期刊: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
• 作者: Munawar, Adnan;Fischer, Gregory
• 通讯作者: Fischer, Gregory

Collaborative Suturing: A Reinforcement Learning Approach to Automate Hand-off Task in Suturing for Surgical Robots

协作缝合：一种强化学习方法，用于自动化手术机器人缝合中的交接任务

• DOI: 10.1109/ro-man47096.2020.9223543
• 发表时间: 2020
• 期刊: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN
• 作者: Varier, Vignesh Manoj;Rajamani, Dhruv Kool;Goldfarb, Nathaniel;Tavakkolmoghaddam, Farid;Munawar, Adnan;Fischer, Gregory S
• 通讯作者: Fischer, Gregory S

A sEMG Proportional Control for the Gripper of Patient Side Manipulator in da Vinci Surgical System

达芬奇手术系统患者侧机械手夹具的表面肌电比例控制

• DOI: 10.1109/embc48229.2022.9871664
• 发表时间: 2022
• 期刊: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society
• 作者: Yang, Kehan;Meier, Tess B.;Zhou, Haoying;Fischer, Gregory S.;Nycz, Christopher J.
• 通讯作者: Nycz, Christopher J.

An Approach to Modeling Closed-Loop Kinematic Chain Mechanisms, Applied to Simulations of the da Vinci Surgical System

闭环运动链机构建模方法，应用于达芬奇手术系统的模拟

• DOI: 10.12700/aph.16.8.2019.8.3
• 发表时间: 2019
• 期刊: Acta Polytechnica Hungarica
• 影响因子: 1.7
• 作者: Gondokaryono RA, Agrawal A

AMBF-RL: A real-time simulation based Reinforcement Learning toolkit for Medical Robotics

• DOI: 10.1109/ismr48347.2022.9807609
• 发表时间: 2022-04
• 期刊: 2022 International Symposium on Medical Robotics (ISMR)
• 作者: Vignesh Manoj Varier;Dhruv Kool Rajamani;Farid Tavakkolmoghaddam;A. Munawar;G. Fischer