机器人坐标系转换

SE2 Representation of 2D rigid-body motion

This subclasss of RTBPose is an object that represents rigid-body motion in 2D.

Internally this is a 3x3 homogeneous transformation matrix (3x3) belonging to

the group SE(2).

Constructor methods::

SE2 general constructor

SE2.exp exponentiate an se(2) matrix

SE2.rand random transformation

new new SE2 object

Display and print methods::

animate ^graphically animate coordinate frame for pose

display ^print the pose in human readable matrix form

plot ^graphically display coordinate frame for pose

print ^print the pose in single line format

Group operations::

* ^mtimes: multiplication (group operator, transform point)

/ ^mrdivide: multiply by inverse

^ ^mpower: exponentiate (integer only):

inv inverse

prod ^product of elements

Methods::

det determinant of matrix component

eig eigenvalues of matrix component

log logarithm of rotation matrix

inv inverse

simplify* apply symbolic simplication to all elements

interp interpolate between poses

theta rotation angle

Information and test methods::

dim ^returns 2

isSE ^returns true

issym ^test if rotation matrix has symbolic elements

SE2.isa test if matrix is SE(2)

Conversion methods::

char* convert to human readable matrix as a string

SE2.convert convert SE2 object or SE(2) matrix to SE2 object

double convert to rotation matrix

R convert to rotation matrix

SE3 convert to SE3 object with zero translation

SO2 convert rotational part to SO2 object

T convert to homogeneous transformation matrix

Twist convert to Twist object

t get.t: convert to translation column vector

Compatibility methods::

isrot2 ^returns false

ishomog2 ^returns true

tr2rt ^convert to rotation matrix and translation vector

t2r ^convert to rotation matrix

transl2 ^translation as a row vector

trprint2 ^print single line representation

trplot2 ^plot coordinate frame

tranimate2 ^animate coordinate frame

^ inherited from RTBPose class.

【机器人工具箱学习笔记】第二章 位置与姿态描述_tr2angvec-CSDN博客文章浏览阅读752次,点赞4次,收藏11次。二维{B}相对于A的相对位姿/对{A}施加平移和旋转使它转化为{B}2.2矩阵指数由【3B1B笔记】e的矩阵指数------怎么算?为什么?知:,即R = expm(skew(θ) ),也即2.4旋转RX绕原点旋转,而XR绕X点旋转。而对于绕C旋转的XC,从右向左读,先将C点转换到原点,绕C旋转,然后再将坐标系平移回C二维twist的中心思想即为:任何坐标变换均为绕某点的旋转?欧拉角:ZYZ序列 eul2r横滚-俯仰-偏航角(卡尔丹角/泰特-布莱恩角/导航角): XYZ序列 rpy2r双向_tr2angvechttps://blog.csdn.net/qq_46142162/article/details/129547023

相关推荐
renhongxia14 小时前
原生多模态对应用架构的重塑
人工智能·深度学习·机器学习·自然语言处理·架构·机器人
LCG米5 小时前
机器人控制系统与运动规划:从RRT算法到ROS move_base实战
算法·机器人
梦想的旅途27 小时前
基于RPA技术的企业微信自动化接口设计思路与应用实践
人工智能·机器人·自动化·企业微信·rpa
zzzzzz31012 小时前
别争了,OpenClaw 和国产龙虾我全都要:一个 AI Agent 混合部署实战
机器学习·机器人·api
应用市场12 小时前
从测距公式到 SLAM:激光雷达在物联网与机器人中的应用原理详解
物联网·机器人
zzzzzz31017 天前
假如我是掘金管理员,我先给评论区装个'代码审查'系统
python·程序员·机器人
通信小呆呆17 天前
当算法有了“五感”:多模态数据融合如何向人体感官协同学习?
人工智能·学习·算法·机器学习·机器人
生成论实验室17 天前
机器人:一个自主运动的系统
人工智能·算法·语言模型·机器人·自动驾驶·agi·安全架构