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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712386
• 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.

具有有限数量串联刚性连杆的传统机器人操纵器在非结构化和密闭环境中的复杂任务中并未取得成功，示例包括许多微创手术应用、密闭环境中的搜索和救援操作以及核能和核能领域的各种维护操作。另外，连续体机器人 (CR) 因其“连续的主干结构”而被提出，可以提供与环境“兼容”的界面，因此与传统机器人相比，连续体机器人具有许多优势。机械手，例如较低的成本、在有限环境中的安全机动性以及对环境接触条件变化的适应性，因此，CR 预计将对机器人在各个领域的成本和适用性产生重大影响。为了克服它们的缺点，仍有许多新问题需要解决，例如刚度低、有效载荷小、精度低、可操作性差以及工作空间和灵巧性往往有限。 我们最近提出了协作 CR（CCR）的新概念，不仅克服了前面提到的 CR 的局限性，而且还吸收了可重构性，以增强其对新任务的适应能力。然而，自主 CCR 的许多理论和实践方面在很大程度上仍未得到解决。重点关注基于图像的 CCR 技术，我们希望通过融合我们正在进行的协同研究（i）基于视觉的机器人控制，在 CCR 的基础上取得重大进展。 (ii) CR 建模和控制，包括开发和验证：(1) 先进的运动静态模型；(2) 基于图像的实时形状和力传感的新方法；和通用视觉伺服控制技术；（4）第一个稳健且主动的非刚性运动结构框架；（5）在医疗和安全防御应用的机器人辅助干预中使用控制结果。 拟议的多学科研究计划预计将产生第一个用于 CCR 的运动学建模、传感和控制技术，从而实现许多重要功能，例如提高导航效率和准确性、提高灵活性和刚度以及提高非结构化和动态操作的鲁棒性这些技术将开辟许多新的机器人应用设计机会，支持加拿大航空航天、国防、服务、制造、能源和医疗保健系统等行业。此外，这项研究将带来新的研究方向，并有利于重叠研究领域。机器人技术、控制、计算机视觉、生物医学工程和自主系统。