Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure
Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure
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使用两阶段校准程序，通过电缆驱动机器人进行快速、可靠的自主外科清创
DOI：
10.1109/icra.2018.8460583
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Ken Goldberg
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文献类型：
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作者：
Daniel Seita;S. Krishnan;Roy Fox;Stephen McKinley;J. Canny;Ken Goldberg
由于线缆驱动系统中固有的非线性，使用机器人手术助理（RSA）（如da芬奇Research Kit（dVRK））自动执行清创术（去除死亡或患病组织碎片）等精确子任务具有挑战性。我们提出并评估了一种新的两阶段粗到精校准方法。在第一阶段（粗略），我们在末端执行器上放置一个红色校准标记，并让它随机移动通过一组开环轨迹，以获得相机像素和内部机器人末端执行器配置的大样本集。然后，这些粗略的数据用于训练深度神经网络（DNN）以学习粗略的变换偏差。在阶段II（精细）中，施加来自阶段I的偏置以使末端执行器朝向印刷纸张上的一小组特定目标点移动。对于每个目标，人类操作员通过直接接触（而不是通过远程操作）手动调整末端执行器位置，并且记录残余补偿偏差。然后使用这些精细数据来训练随机森林（RF）以学习精细变换偏差。随后的实验表明，在没有校准的情况下，位置误差平均为4.55mm。阶段I可以将平均误差减小到2.14mm，并且阶段I和阶段II的组合可以将平均误差减小到1.08mm。我们将这些结果应用于葡萄干和南瓜籽作为碎片幻影的清创。使用具有标准边缘检测的内窥镜立体摄像机，120次试验的平均成功率为94.5%，超过了之前使用更大碎片的结果（89.4%），并实现了2.1倍的加速，将每个碎片的时间从15.8秒减少到7.3秒。源代码、数据和视频可在https://sites.google.com/view/calib-icra/上获得。
Automating precision subtasks such as debridement (removing dead or diseased tissue fragments) with Robotic Surgical Assistants (RSAs) such as the da Vinci Research Kit (dVRK) is challenging due to inherent nOnlinearities in cable-driven systems. We propose and evaluate a novel two-phase coarse-to-fine calibration method. In Phase I (coarse), we place a red calibration marker on the end effector and let it randomly move through a set of open-loop trajectories to obtain a large sample set of camera pixels and internal robot end-effector configurations. This coarse data is then used to train a Deep Neural Network (DNN) to learn the coarse transformation bias. In Phase II (fine), the bias from Phase I is applied to move the end -effector toward a small set of specific target points on a printed sheet. For each target, a human operator manually adjusts the end -effector position by direct contact (not through teleoperation) and the residual compensation bias is recorded. This fine data is then used to train a Random Forest (RF) to learn the fine transformation bias. Subsequent experiments suggest that without calibration, position errors average 4.55mm. Phase I can reduce average error to 2.14mm and the combination of Phase I and Phase II can reduces average error to 1.08mm. We apply these results to debridement of raisins and pumpkin seeds as fragment phantoms. Using an endoscopic stereo camera with standard edge detection, experiments with 120 trials achieved average success rates of 94.5 %, exceeding prior results with much larger fragments (89.4%) and achieving a speedup of 2.1x, decreasing time per fragment from 15.8 seconds to 7.3 seconds. Source code, data, and videos are available at https://sites.google.com/view/calib-icra/.