Neural Network based Inverse Dynamics Identification and External Force Estimation on the da Vinci Research Kit

Neural Network based Inverse Dynamics Identification and External Force Estimation on the da Vinci Research Kit
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达芬奇研究套件上基于神经网络的逆动力学识别和外力估计

DOI:
10.1109/icra40945.2020.9197445
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发表时间:
2020
期刊:
IEEE International Conference on Robotics and Automation (ICRA
影响因子:
--
通讯作者:
Tumerdem, Ugur
Tumerdem, Ugur
中科院分区:
--
文献类型:
--
作者:
Yilmaz, Nural;Wu, Jie Ying;Kazanzides, Peter;Tumerdem, Ugur

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目前大多数手术机器人系统缺乏感知工具/组织相互作用力的能力,这促使研究人员通过其他可用的测量方法(主要是关节扭矩)来估计这些作用力。这些方法需要从测量的关节力矩中减去由于机器人逆动力学引起的关节内力矩。本文提出了利用神经网络估计达芬奇手术机器人的逆动力学,从而可以估计外部环境力。自由空间运动实验表明,神经网络可以在10%的归一化均方根误差(NRMSE)内估计关节内部扭矩,优于文献中基于模型的方法。与外力传感器的比较表明,该方法能够在NRMSE约10%的范围内估计环境力。
Most current surgical robotic systems lack the ability to sense tool/tissue interaction forces, which motivates research in methods to estimate these forces from other available measurements, primarily joint torques. These methods require the internal joint torques, due to the robot inverse dynamics, to be subtracted from the measured joint torques. This paper presents the use of neural networks to estimate the inverse dynamics of the da Vinci surgical robot, which enables estimation of the external environment forces. Experiments with motions in free space demonstrate that the neural networks can estimate the internal joint torques within 10% normalized rootmean-square error (NRMSE), which outperforms model-based approaches in the literature. Comparison with an external force sensor shows that the method is able to estimate environment forces within about 10% NRMSE.
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发表时间: 2010-03-01
影响因子: 3
作者:
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