High-performance image-guided continuum robots

传统的具有有限数量连续连接刚性连杆的机械臂在非结构化和受限环境中不能成功地完成复杂任务。例子包括许多微创手术应用，密闭环境中的搜索和救援行动，以及核工业和航空航天工业中的各种维护操作。另外，连续体机器人（cr）由于其“连续的骨干结构”而被提出，呈现出与环境“兼容”的接口。因此，CRs提供了许多优于传统机械手的优点，例如成本更低，在受限环境中的安全可操作性，以及对环境接触条件变化的适应性。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。然而，自主CRs的研究相对较新，存在刚度低、载荷小、精度低、操纵性差、工作空间和灵巧性受限等缺点，有待解决。近年来，我们提出了协作型协同反应系统的新概念，以克服协同反应系统的上述局限性，并吸收协同反应系统的可重构性，从而提高协同反应系统对新任务的适应能力。然而，自主ccr的许多理论和实践方面仍未得到很大程度的治疗。重点关注基于图像的ccr技术，我们希望通过合并我们正在进行的(i)基于视觉的机器人控制和（ii） CR建模和控制的协同研究，在ccr基础上取得重大进展。该计划的目标包括开发和验证：(1)先进的动静态模型；(2)基于图像的实时形状和力传感新方法；(3)鲁棒和通用视觉伺服控制技术；(4)第一种鲁棒主动非刚体运动框架结构；(5)控制的使用导致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提供第一个运动学建模、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稳健性。这些技术将开辟一系列新的机器人应用设计机会，支持加拿大在航空航天、国防、服务、制造、能源和医疗保健系统方面的工业。此外，这项研究将为机器人、控制、计算机视觉、生物医学工程和自主系统的交叉研究领域带来新的研究方向和利益。

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.