High-performance image-guided continuum robots

高性能图像引导连续体机器人

• 批准号: RGPIN-2017-06930
• 负责人: JanabiSharifi, Farrokh
• 金额: $ 2.4万
• 依托单位: Ryerson University
• 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=759659
• 关键词: performance; image; guided; continuum; robots

Conventional robotic manipulators with finite-number of serially-connected rigid links have not been successful in tackling complex tasks in unstructured and confined environments. Examples include many minimally-invasive surgery applications, search and rescue operations in confined environments, and various maintenance operations in nuclear and aerospace industries. Alternatively, continuum robots (CRs) have been proposed that, due to their “continuous backbone structures,” present a “compliant” interface to the environment. Thus CRs offer a number of advantages over traditional manipulators, such as lower cost, safe maneuverability in confined environments, and adaptability to variations in environmental contact conditions. Therefore, CRs are expected to have a significant impact on the cost and applicability of robots in various domains. However, research on autonomous CRs is fairly new and many issues remain to be addressed to overcome their shortcomings, such as low stiffness, small payload, low accuracy, poor manipulability, and often limited workspace and dexterity. We have recently proposed the new concept of cooperative CRs (CCRs) to not only overcome the aforementioned limitations of CRs but also assimilate reconfigurability for enhancing their adaptation capability to new tasks. However, many theoretical and practical aspects of autonomous CCRs are remaining largely untreated. With a focus on image-based techniques for CCRs, we expect to make significant advancements in the foundations of CCRs by merging our on-going synergetic research on (i) vision-based control of robots, and (ii) CR modeling and control. The objectives of this program include the development and validation of: (1) advanced kineto-static models; (2) novel methods for real-time image-based shape and force sensing; (3) robust and generic visual-servo control techniques; (4) the first robust and active non-rigid structure-from-motion frameworks; and (5) the use of the control results in robot-assisted interventions for medical and security-defence applications. The proposed multidisciplinary research program is anticipated to produce the first kinematic modeling, sensing, and control techniques for CCRs, enabling many important capabilities, such as enhanced efficiency and accuracy of navigation, improved dexterity and stiffness, and increased robustness of operation in unstructured and dynamic environments. These techniques will open up a host of new robot applications design opportunities, supporting Canadian industries in aerospace, defence, service, manufacturing, energy, and healthcare systems. Furthermore, this research will lead to new research directions and benefit overlapping research areas in robotics, control, computer vision, biomedical engineering, and autonomous systems.

传统的机器人操纵器具有有限的串行刚性链接的有限数量，尚未成功地解决非结构化和密闭环境中的复杂任务。例子包括许多最小侵入性的手术应用，在受限环境中的搜索和救援操作以及核和航空航天行业的各种维护操作。另外，已经提出了持续的机器人（CRS），由于其“连续的骨干结构”，因此对环境提出了“合规”的界面。 CRS比传统的操纵器具有许多优势，例如成本较低，安全环境中的安全可操作性以及对环境接触条件的变化的适应性。因此，预计CRS将对机器人在各个领域的成本和适用性产生重大影响。但是，对自主CRS的研究是相当新的，并且仍有许多问题要克服其缺点，例如刚度低，有效载荷较小，准确性低，可操作性差，并且通常有限的工作空间和敏捷性。我们最近提出了合作CRS（CCR）的新概念，不仅要克服CRS的Priore限制，而且还吸收了重新配置，以增强其适应能力对新任务。但是，自主CCR的许多理论和实际方面仍然很大程度上没有治疗。通过关注CCR的基于图像的技术，我们希望通过合并我们对基于（i）基于视觉的机器人控制的持续协同研究，以及（ii）CR建模和控制，在CCR的基础上取得了重大进步。该程序的目标包括：（1）高级Kineto静态模型的开发和验证； （2）实时基于图像的形状和力传感的新方法； （3）强大而通用的视觉效果控制技术； （4）第一个坚固且活跃的非刚性结构框架框架； （5）使用控制导致机器人辅助干预措施对医疗和安全防御应用程序。预计拟议的多学科研究计划将为CCR提供第一个运动学建模，传感和控制技术，从而实现许多重要的功能，例如提高导航的效率和准确性，提高的灵活性和僵硬，以及在非结构化和动态环境中的运行鲁棒性提高。这些技术将为许多新的机器人应用设计机会开放，并在航空航天，国防，服务，制造，能源和医疗系统中为加拿大行业提供支持。此外，这项研究将为机器人技术，控制，计算机视觉，生物医学工程和自治系统的新研究方向和利益重叠的研究领域。