利用三维检测和定位网络为手术工具实时生成和执行主动约束

S. Souipas, Anh Nguyen, Stephen Laws, Brian L. Davies, Ferdinando M. Rodriguez y Baena
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摘要

简介协作机器人的设计目的是与人类一起操纵末端执行器,主动约束的实施将使协作机器人受益匪浅。这一过程包括定义边界,然后在机器人工具提示与生成的边界发生交互时执行某种控制算法。与约束边界的接触会通过各种潜在的反馈形式传达给人类操作员。在外科手术机器人等领域,病人的安全是最重要的,实施主动约束可以防止机器人与病人解剖结构中不应该进行手术的部分发生交互。尽管骨科手术机器人有所改进,但在具有触觉反馈功能的笨重系统和只能进行边界控制的小型化系统之间仍存在差距,在边界控制中,与主动约束边界的交互会中断机器人的功能。一般来说,主动约束生成依赖于光学跟踪系统和术前成像技术:本文介绍了 Signature 机器人的改进版,这是一种用于矫形外科手术的三自由度动手协作系统。此外,本文还介绍了一种利用我们之前推出的基于 RGB 摄像头的单目网络 SimPS-Net "即时 "生成和执行主动约束的方法。该网络是实时部署的,用于定义边界。该边界随后被用于约束执行测试。机器人被用来测试两种不同的主动约束:安全区域和限制区域:根据计算,网络成功率为 54.7% ± 5.2%,即正确定位结果与总定位结果之比。在安全区域的情况下,触觉反馈阻止了超出活动约束边界的工具提示操作,与边界的平均距离为 2.70 毫米 ± 0.37 毫米,平均退出持续时间为 0.76 秒 ± 0.11 秒:本文展示了所建议的机器人平台的可行性,并介绍了多功能约束生成和执行管道的可喜成果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time active constraint generation and enforcement for surgical tools using 3D detection and localisation network
Introduction: Collaborative robots, designed to work alongside humans for manipulating end-effectors, greatly benefit from the implementation of active constraints. This process comprises the definition of a boundary, followed by the enforcement of some control algorithm when the robot tooltip interacts with the generated boundary. Contact with the constraint boundary is communicated to the human operator through various potential forms of feedback. In fields like surgical robotics, where patient safety is paramount, implementing active constraints can prevent the robot from interacting with portions of the patient anatomy that shouldn’t be operated on. Despite improvements in orthopaedic surgical robots, however, there exists a gap between bulky systems with haptic feedback capabilities and miniaturised systems that only allow for boundary control, where interaction with the active constraint boundary interrupts robot functions. Generally, active constraint generation relies on optical tracking systems and preoperative imaging techniques.Methods: This paper presents a refined version of the Signature Robot, a three degrees-of-freedom, hands-on collaborative system for orthopaedic surgery. Additionally, it presents a method for generating and enforcing active constraints “on-the-fly” using our previously introduced monocular, RGB, camera-based network, SimPS-Net. The network was deployed in real-time for the purpose of boundary definition. This boundary was subsequently used for constraint enforcement testing. The robot was utilised to test two different active constraints: a safe region and a restricted region.Results: The network success rate, defined as the ratio of correct over total object localisation results, was calculated to be 54.7% ± 5.2%. In the safe region case, haptic feedback resisted tooltip manipulation beyond the active constraint boundary, with a mean distance from the boundary of 2.70 mm ± 0.37 mm and a mean exit duration of 0.76 s ± 0.11 s. For the restricted-zone constraint, the operator was successfully prevented from penetrating the boundary in 100% of attempts.Discussion: This paper showcases the viability of the proposed robotic platform and presents promising results of a versatile constraint generation and enforcement pipeline.
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