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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=684752
• 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.

传统的机器人机械手与有限数量的串联连接的刚性连杆没有成功地解决复杂的任务，在非结构化和受限的环境。例子包括许多微创手术应用，在密闭环境中的搜索和救援行动，以及核和航空航天工业中的各种维护操作。或者，连续体机器人（CR）已被提出，由于其“连续的骨干结构”，目前的“兼容”的环境接口。因此，CR提供了许多优势，传统的机械手，如成本较低，安全的可操作性在有限的环境中，并适应环境接触条件的变化。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。然而，对自主CR的研究是相当新的，许多问题仍有待解决，以克服其缺点，如刚度低，有效载荷小，精度低，可操作性差，往往有限的工作空间和灵活性。** 我们最近提出了协作CR（CCR）的新概念，不仅克服了CR的上述局限性，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处理。我们专注于基于图像的CCR技术，希望通过合并我们正在进行的协同研究（i）基于视觉的机器人控制，以及（ii）CR建模和控制，在CCR的基础上取得重大进展。该计划的目标包括开发和验证：（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;（3）鲁棒和通用的视觉伺服控制技术;（4）第一个鲁棒和主动的非刚性结构从运动框架;以及（5）控制的使用导致用于医疗和安全防御应用的机器人辅助干预。** 拟议的多学科研究计划预计将为CCR产生第一个运动学建模，传感和控制技术，实现许多重要的功能，例如提高导航的效率和准确性，提高灵活性和刚度，以及增加非结构化和动态环境中操作的鲁棒性。这些技术将开辟一系列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，这项研究将导致新的研究方向，并有利于机器人，控制，计算机视觉，生物医学工程和自主系统的重叠研究领域。