Design and Control of Robotic Systems and Devices for Medical Applications

医疗应用机器人系统和设备的设计和控制

• 批准号: RGPIN-2019-06742
• 负责人: Patel, Rajnikant
• 金额: $ 5.54万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752224
• 关键词: Design; Control; Robotic; Systems; Devices

The proposed research program is aimed at developing advanced robotic and intelligent systems for the next generation of minimally invasive medical interventions that will minimize invasiveness and trauma while enhancing safety, efficiency, reliability and cost-effectiveness. There is therefore a need to examine the current state of robotics-assisted medical interventions, for example by using recent developments in continuum robotics, machine learning, and "smart" actuation and sensing technologies, coupled with advances in haptics and teleoperation. The proposed research program will address this need through the development of various technologies and techniques in the context of the following applications: Framework for the Design and Real-Time Control of Concentric-Tube Robots (CTRs): A CTR is a type of continuum robotic device consisting of a set of thin concentric tubes, shaped in different curved profiles. By rotating and translating the tubes with respect to each other, their curvatures can be used to obtain different curved profiles for the overall robot and to position and orient the robot's tip while avoiding sensitive regions in and around the surgical workspace. The CTR's size constraints and shape make force sensing challenging. The flexibility and changing shape introduce significant complexity in the design of a control architecture as well as in workspace determination and path planning. Furthermore, flexibility in the CTR makes the problem of haptics-enabled teleoperation particularly challenging. The proposed framework will provide a methodology to address issues such as anatomical and other constraints imposed by particular interventions, the kinematic dexterity and workspace requirements, the imaging and other sensing modalities to be used, control issues (such as position and force control, robustness to modeling and measurement uncertainty), and haptics and teleoperation. Autonomous Systems for Surgical Training and Skills Assessment for (RA)MIS: This program of research will build on recent preliminary work conducted in controlled environments that has demonstrated the possibility of introducing autonomy in suturing tasks during Robotics-Assisted Minimally Invasive Surgery (RAMIS). The proposed research will explore how a robotic platform and a variety of sensing modalities can be used to analyze the performance of various robotics-assisted or conventional minimally invasive surgery tasks by experts. Machine learning approaches can then use the resulting data to train a surgical robotic system to serve as an "Intelligent Virtual Expert (IVE)" so that it can be used in place of an actual expert surgeon for offline (RA)MIS training. Application of approaches such as Deep Neural Networks, Recurrent Neural Networks, Reinforcement Learning and their variations will be investigated.

拟议的研究计划旨在为下一代微创医疗干预开发先进的机器人和智能系统，以最大限度地减少侵入性和创伤，同时提高安全性，效率，可靠性和成本效益。因此，有必要研究机器人辅助医疗干预的现状，例如通过使用连续体机器人，机器学习和“智能”驱动和传感技术的最新发展，以及触觉和远程操作的进步。拟议的研究计划将通过以下应用程序的背景下开发各种技术和工艺来满足这一需求：同心管机器人（CTR）的设计和实时控制框架：CTR是一种连续体机器人设备，由一组薄同心管组成，形状不同的弯曲轮廓。通过相对于彼此旋转和平移管，它们的曲率可以用于获得整个机器人的不同弯曲轮廓，并且定位和定向机器人的尖端，同时避开手术工作空间中和周围的敏感区域。CTR的尺寸限制和形状使得力感测具有挑战性。灵活性和形状变化在控制架构的设计以及工作空间确定和路径规划中引入了显著的复杂性。此外，CTR的灵活性使得支持触觉的遥操作问题特别具有挑战性。拟议的框架将提供一种方法，以解决问题，如解剖和其他限制所施加的特定干预措施，运动灵活性和工作空间的要求，成像和其他传感方式使用，控制问题（如位置和力控制，鲁棒性建模和测量不确定性），触觉和遥操作。用于（RA）MIS的手术培训和技能评估的自主系统：该研究计划将建立在最近在受控环境中进行的初步工作的基础上，这些工作已经证明了在机器人辅助微创手术（RAMIS）期间在手术任务中引入自主权的可能性。拟议的研究将探索如何使用机器人平台和各种传感模式来分析专家进行的各种机器人辅助或传统微创手术任务的性能。然后，机器学习方法可以使用所得到的数据来训练手术机器人系统，以充当“智能虚拟专家（IVE）”，使得它可以代替实际的专家外科医生用于离线（RA）MIS训练。将研究深度神经网络，递归神经网络，强化学习及其变体等方法的应用。