Learning-based Inverse Kinematics from Shape as Input for Concentric Tube Continuum Robots
Learning-based Inverse Kinematics from Shape as Input for Concentric Tube Continuum Robots
复制标题
基于学习的逆运动学，将形状作为同心管连续体机器人的输入
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
J. Burgner
中科院分区：
文献类型：
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作者：
Nan Liang;R. Grassmann;S. Lilge;J. Burgner
我们介绍了一种方法来计算逆运动学同心管连续体机器人从所需的形状作为输入。我们证明，它是可以准确地学习关节参数使用神经网络的离散点式形状表示与不同的离散化。与普通数值方法相比，基于学习的方法在联合空间和计算的准确性方面是优选的。用20个等距点表示形状，获得了误差为2.22°和1.45 mm的形状到关节的逆运动学。此外，我们将形状到关节的逆运动学扩展到图像到关节的逆运动学，利用多视图图像作为形状表示。这种基于图像的方法实现了6.02°和2.76 mm的误差。形状到关节和图像到关节，与仅考虑尖端姿势的基于学习的最新方法相比，导致更高的准确性。
We introduce a methodology to compute the inverse kinematics for concentric tube continuum robots from a desired shape as input. We demonstrate that it is possible to accurately learn joint parameters using neural networks for a discrete point-wise shape representation with different discretization. In comparison to a vanilla numerical method, the learning-based method is preferred in terms of accuracy in joint space and computation. Representing the shape with up to 20 equidistant points, a shape-to-joint inverse kinematics with errors of 2.22° and 1.45 mm is obtained. Further, we extend the shape-to-joint inverse kinematics to image-to-joint inverse kinematics utilizing multi-view images as shape representation. This image-based method achieves errors of 6.02° and 2.76 mm. Both approaches, i.e., shape-to-joint and image-to-joint, result in higher accuracy compared to the learning-based state-of-the-art approach which only considers the tip pose.
影响因子：
7.8
作者：
Burgner-Kahrs, Jessica;Rucker, D. Caleb;Choset, Howie
通讯作者：
Choset, Howie