论文标题
操纵器差异运动学：第1部分：运动学，速度和应用
Manipulator Differential Kinematics: Part 1: Kinematics, Velocity, and Applications
论文作者
论文摘要
操纵器运动学与操纵器中每个链路的运动有关，而无需考虑质量或力。在本文中，这是两部分教程中的第一个，我们使用基本变换序列（ETS）为建模操纵器运动学提供了介绍。然后，我们制定了一阶差异运动学，该运动学导致操纵器雅各布式，这是速度控制和逆运动学的基础。我们描述了基本的古典技术，这些技术依赖于操纵器雅各布，在展示一些当代应用之前。本教程的第二部分提供了第二和高阶差异运动学的表述，介绍了操纵器Hessian，并说明了先进的技术，其中一些提高了部分I中所示的技术性能。 我们提供了Jupyter笔记本，以伴随本教程中的每个部分。这些笔记本是用Python代码编写的，并为Python使用Robotics Toolbox，以及Swift Simulator提供算法的示例和实现。虽然不是绝对必要的，但对于最吸引人和最有用的经验，我们建议您在阅读本文时使用Jupyter笔记本。可以在https://github.com/jhavl/dkt上访问笔记本和设置说明。
Manipulator kinematics is concerned with the motion of each link within a manipulator without considering mass or force. In this article, which is the first in a two-part tutorial, we provide an introduction to modelling manipulator kinematics using the elementary transform sequence (ETS). Then we formulate the first-order differential kinematics, which leads to the manipulator Jacobian, which is the basis for velocity control and inverse kinematics. We describe essential classical techniques which rely on the manipulator Jacobian before exhibiting some contemporary applications. Part II of this tutorial provides a formulation of second and higher-order differential kinematics, introduces the manipulator Hessian, and illustrates advanced techniques, some of which improve the performance of techniques demonstrated in Part I. We have provided Jupyter Notebooks to accompany each section within this tutorial. The Notebooks are written in Python code and use the Robotics Toolbox for Python, and the Swift Simulator to provide examples and implementations of algorithms. While not absolutely essential, for the most engaging and informative experience, we recommend working through the Jupyter Notebooks while reading this article. The Notebooks and setup instructions can be accessed at https://github.com/jhavl/dkt.
