The development of autonomous or semiautonomous surgical robots stands to improve the performance of existing teleoperated equipment but requires fine hand-eye calibration between the free-moving endoscopic camera and patient-side manipulator arms (PSMs). A novel method of solving this problem for the da Vinci robotic surgical system and kinematically similar systems is presented. First, a series of image-processing and optical-tracking operations are performed to compute the coordinate transformation between the endoscopic camera view frame and an optical-tracking marker permanently affixed to the camera body. Then, the kinematic properties of the PSM are exploited to compute the coordinate transformation between the kinematic base frame of the PSM and an optical marker permanently affixed thereto. Using these transformations, it is then possible to compute the spatial relationship between the PSM and the endoscopic camera using only one tracker snapshot of the two markers. The effectiveness of this calibration is demonstrated by successfully guiding the PSM end-effector to points of interest identified through the camera. Additional tests on a surgical task, namely, grasping a surgical needle, are also performed to validate the proposed method. The resulting visually guided robot positioning accuracy is better than the earlier hand-eye calibration results reported in the literature for the da Vinci system while supporting the intraoperative update of the calibration and requiring only devices that are already commonly used in the surgical environment. Note to Practitioners —The problem of hand-eye calibration for the da Vinci robotic surgical system and kinematically similar systems is addressed in this article. Existing approaches have insufficient accuracy to automate low-level surgical subtasks and often require external patterns or subjective human intervention, none of which are applicable to practical robotic minimally invasive surgery (RMIS) scenarios. This article breaks down the calibration procedure into systematic steps to reduce error accumulation. Most of the time-consuming steps are performed offline, allowing them to be retained between movements. Each time the passive joints of the manipulator or the endoscope move, all that needs to be done is to refresh the transformation between the fixed markers. This key idea enables intraoperative updates of the hand-eye calibration to be performed online without sacrificing precision. The calibration method presented here demonstrates that the achieved accuracy is sufficient for automating basic surgical manipulation tasks, such as grasping a suturing needle. The hand-eye calibration will be incorporated into a visually guided manipulation framework to perform high-precision autonomous surgical tasks.

中文翻译：

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达芬奇机器人手术系统的相机-机器人校准

自主或半自主手术机器人的发展将提高现有遥控设备的性能，但需要在自由移动的内窥镜相机和患者侧机械臂 (PSM) 之间进行精细的手眼校准。提出了一种解决达芬奇机器人手术系统和运动学类似系统的这个问题的新方法。首先，执行一系列图像处理和光学跟踪操作，以计算内窥镜相机视图框架与永久固定在相机机身上的光学跟踪标记之间的坐标变换。然后，利用 PSM 的运动学特性来计算 PSM 的运动学基础框架和永久固定在其上的光学标记之间的坐标变换。使用这些转换，然后可以仅使用两个标记的一个跟踪器快照来计算 PSM 和内窥镜相机之间的空间关系。通过成功地将 PSM 末端执行器引导到通过相机识别的兴趣点，证明了这种校准的有效性。还对手术任务进行了额外的测试，即抓住手术针，以验证所提出的方法。由此产生的视觉引导机器人定位精度优于达芬奇系统文献中报道的早期手眼校准结果，同时支持校准的术中更新，并且只需要手术环境中已经常用的设备。通过成功地将 PSM 末端执行器引导到通过相机识别的兴趣点，证明了这种校准的有效性。还对手术任务进行了额外的测试，即抓住手术针，以验证所提出的方法。由此产生的视觉引导机器人定位精度优于达芬奇系统文献中报道的早期手眼校准结果，同时支持校准的术中更新，并且只需要手术环境中已经常用的设备。通过成功地将 PSM 末端执行器引导到通过相机识别的兴趣点，证明了这种校准的有效性。还对手术任务进行了额外的测试，即抓住手术针，以验证所提出的方法。由此产生的视觉引导机器人定位精度优于达芬奇系统文献中报道的早期手眼校准结果，同时支持校准的术中更新，并且只需要手术环境中已经常用的设备。从业者须知 —本文解决了达芬奇机器人手术系统和运动学类似系统的手眼校准问题。现有方法的准确性不足以自动执行低级手术子任务，并且通常需要外部模式或主观人为干预，这些都不适用于实际的机器人微创手术 (RMIS) 场景。本文将校准程序分解为系统步骤，以减少误差累积。大多数耗时的步骤都是离线执行的，允许它们在动作之间保留。每次机械手或内窥镜的被动关节移动时，只需刷新固定标记之间的转换即可。这一关键思想使手眼校准的术中更新能够在线执行，而不会牺牲精度。此处介绍的校准方法表明，所达到的精度足以自动执行基本的手术操作任务，例如抓取缝合针。手眼校准将被纳入视觉引导操作框架，以执行高精度自主手术任务。