Learning Based Estimation of 7 DOF Instrument and Grasping Forces on the da Vinci Research Kit
Learning Based Estimation of 7 DOF Instrument and Grasping Forces on the da Vinci Research Kit
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DOI：
10.1109/ismr48347.2022.9807525
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Jintan Zhang;Nural Yilmaz;U. Tumerdem;P. Kazanzides
中科院分区：
文献类型：
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作者：
Jintan Zhang;Nural Yilmaz;U. Tumerdem;P. Kazanzides
目前的微创手术机器人，例如da芬奇®，不能直接感测机器人器械与患者解剖结构之间的相互作用。这包括器械夹持力和器械与环境之间的6自由度（DOF）力/扭矩（扳手）。以前的工作已经研究了基于模型或数据驱动的方法，这些方法使用可用的测量值，例如关节位置，速度和扭矩，来估计抓取力或6自由度扳手。本文通过开发和评估一种数据驱动（基于学习）的方法来同时估计抓取力和外部扳手，从而扩展了先前的工作。由于夹持器和其他腕关节之间的机械耦合，这项任务变得复杂，但该网络能够同时估计外力，扭矩和夹持力，RMS误差分别为1.4N，0.04Nm和0.1N。此外，迁移学习可以使神经网络快速适应不同的仪器。
Present-day minimally-invasive surgical robots, such as the da Vinci®, cannot directly sense interaction between the robotic instruments and the patient anatomy. This includes the instrument grasping force and the 6 degree-of-freedom (DOF) force/torque (wrench) between the instrument and the environment. Previous works have investigated model-based or data-driven methods that use available measurements, such as joint positions, velocities and torques, to estimate either the grasping force or the 6 DOF wrench. This paper extends prior work by developing and evaluating a data-driven (learning-based) method to simultaneously estimate the grasping force and external wrench. This task is complicated by the mechanical coupling between the gripper and other wrist joints, but the network is able to simultaneously estimate external forces, torques, and gripper force with RMS errors of 1.4N, 0.04Nm, and 0.1N, respectively. In addition, transfer learning is shown to enable the neural network to quickly adapt to different instruments.