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title: 3D Motion
categories: lecture
geometry: margin=2cm
fontsize: 12pt
---
This is partly a recap of [3D Modelling](), but it also introduces the
exponential notation of continuous motion.
This is a recap of two previous talks
+ [Change of Basis]() from [3D Modelling]()
+ [Representations of 3D Motion]() from
[3D Objects in Python]()
# 3D Motion
## Rigid Body Motion
+ an object $O$ is a set of points $O\subset\mathbb{R}^3$
+ Movement (or **displacement**) is a map $g:\mathbb{R}^3\to\mathbb{R}^3$
acting on all points in $O$.
+ Motion is a displacement $g(t)$ for every point $t$ in time
- $g(t): \mathbb{R}^3\to\mathbb{R}^3; \mathbf{X}\mapsto g(t)(\mathbf{X})$
## Euclidean Transformation
+ A rigid body does not change shape when it moves.
+ In particular, the distance between two points does not change
$$|| \mathbf{X} - \mathbf{Y} || = ||g(\mathbf{X})-g(\mathbf{Y})||$$
If this identity is satisfied for all points $\mathbf{X},\mathbf{Y}$,
we have a **Euclidean Transformation** and write $g\in E(3)$
The map $g$ induces a map $g_*$ on free vectors,
$$g_*(v) = g(\mathbf{X}) - g(\mathbf{Y})\quad\text{where}\quad v = \mathbf{X}) -\mathbf{Y})$$
## Special Euclidean Transformation
+ Consider mirroring: $[X_1,X_2,X_3]\mapsto[X_1,X_2,-X_3]$
+ Euclidean transformation, but not a rigid body displacement
+ Rigid body motion preserves orientation, i.e.
+ For three points $\mathbf{X},\mathbf{Y},\mathbf{Z}$
+ define vectors $u=\mathbf{Y}-\mathbf{X}$ and $v=\mathbf{Z}-\mathbf{X}$
+ To preserve orientation we preserve the cross product
$$g_*(u)\times g_*(v) = g_*(u\times v),\quad\forall u,v\in\mathbb{R}^3$$
## Summary
A **Special Euclidean Transformation** $g\in SE(3)$ satisfies
$$\langle u,v\rangle = \langle g_*(u),g_*(v)\rangle$$
$$g_*(u)\times g_*(v) = g_*(u\times v),\quad\forall u,v\in\mathbb{R}^3$$
## Translation
+ Vectors act on points
$$v: x \mapsto x+v$$
$$[x_1,x_2,x_3]^\mathrm{T} + [v_1,v_2,v_3]^\mathrm{T} = [x_1+v_1,x_2+v_2,x_3+v_3]^\mathrm{T}$$
+ The vector defines a **translation**
+ Translate object by translating every point in the object
# Rotation
## Rotational Matrix
+ The Rotational Matrix is orthogonal: $R(t)\cdot R^\mathrm{T}(t)=I$
+ Differentiation gives
$$\dot R(t)R\mathrm{T}(t) + R(t)\dot R^\mathrm{T}=0
\Longrightarrow
\dot R(t)R\mathrm{T}(t) = -(\dot R(t) R^\mathrm{T})^\mathrm{T} $$
+ I.e. skew-symmetric $\dot R(t) = \hat\omega(t)R(t)$ for some $\omega$
+ This gives a first-order approximation $R(t_0+dt)\approx I + \hat\omega(t_0)dt$
## Matrix Exponential
ODE: $\dot x(t) = \hat\omega x(t)$
$$x(t) = e^{\hat\omega t}x(0)$$
$$e^{\hat\omega t} = I + \hat\omega t + \frac{(\hat\omega t)^2}{2!} + \cdots
\frac{(\hat\omega t)^n}{n!} + \cdots$$
+ Assume $||\omega||=1$. $R(t)=e^{\hat\omega t}$ rotates by $t$ radians around the axis $\omega$
# Additional material
## Velocity Transformations
**Adjoint Map**
$\mathrm{ad}_g: \mathrm{se}(3)\to\mathrm{se}(3);\quad \hat\xi\mapsto g\hat\xi g^{-1}$