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.
翻译:手动管理器的运动与操纵器内部每个链接的动作有关, 而不考虑质量或力量。 在本文中, 这是两部分教程中的第一个, 我们介绍使用基本变换序列( ETS) 模拟操纵机动运动。 然后我们制定第一级差异运动图, 导致操控者Jacobian, 这是速度控制和反动运动学的基础。 我们描述了依赖操纵者Jacobian 的基本古典技术, 在展示当代一些应用之前, 我们描述了这些古典技术。 本教程的第二部分提供了二级和更高级差异性能的配方, 引入了操纵者Hessian, 并展示了先进的技术, 其中一些技术改进了第一部分所展示的技术的性能。 我们提供了Juppyter笔记本本本本, 并用 httpthon 代码编写, 并使用 Python 的机器人工具箱, Swift Simulator 提供一些范例和实施算法。 虽然不是绝对必要, 但是对于最有吸引力和知识性的指示, 我们建议通过Juppycompat/ Protoblears。