Revision as of 10:00, 11 May 2009 by imported>Paul Wormer
A rotation of a 3-dimensional rigid body is a motion of the body that leaves one point, O, fixed. By Euler's theorem follows that then not only the point is fixed but also an axis—the rotation axis— through the fixed point. Write
for the unit vector along the rotation axis and φ for the angle over which the body is rotated, then the rotation is written as
Erect three Cartesian coordinate axes with the origin in the fixed point O and take unit vectors
along the axes, then the rotation matrix
is defined by its elements
:
![{\displaystyle {\mathcal {R}}(\varphi ,{\hat {n}})({\hat {e}}_{i})=\sum _{j=x,y,x}{\hat {e}}_{j}R_{ji}(\varphi ,{\hat {n}})\quad {\hbox{for}}\quad i=x,y,z.}](https://wikimedia.org/api/rest_v1/media/math/render/svg/249f17a2689b5e261ba0c1a3fd9dd2741322f052)
In a more condensed notation this equation is written as
![{\displaystyle {\mathcal {R}}(\varphi ,{\hat {n}})\left({\hat {e}}_{x},\;{\hat {e}}_{y},\;{\hat {e}}_{z}\right)=\left({\hat {e}}_{x},\;{\hat {e}}_{y},\;{\hat {e}}_{z}\right)\;\mathbf {R} (\varphi ,{\hat {n}}).}](https://wikimedia.org/api/rest_v1/media/math/render/svg/d47a6476a334e9f62e60e4a51d4c91692ccdf049)
Given a basis of a linear space, the association between a linear map and its matrix is one-to-one.
Properties of matrix
Since rotation conserves the shape of a rigid body, it leaves angles and distances invariant. In other words, for any pair of vectors
and
in
the inner product is invariant,
![{\displaystyle \left({\mathcal {R}}({\vec {a}}),\;{\mathcal {R}}({\vec {b}})\right)=\left({\vec {a}},\;{\vec {b}}\right).}](https://wikimedia.org/api/rest_v1/media/math/render/svg/4d25e0dd8228e24b13a574173923cbaa5ea71dc7)
A linear map with this property is called orthogonal. It is easily shown that a similar vector/matrix relation holds. First we define
![{\displaystyle {\vec {a}}=\left({\hat {e}}_{x},\;{\hat {e}}_{y},\;{\hat {e}}_{z}\right){\begin{pmatrix}a_{x}\\a_{y}\\a_{z}\end{pmatrix}}\equiv \left({\hat {e}}_{x},\;{\hat {e}}_{y},\;{\hat {e}}_{z}\right)\mathbf {a} \quad {\hbox{and}}\quad {\vec {b}}=\left({\hat {e}}_{x},\;{\hat {e}}_{y},\;{\hat {e}}_{z}\right){\begin{pmatrix}b_{x}\\b_{y}\\b_{z}\end{pmatrix}}\equiv \left({\hat {e}}_{x},\;{\hat {e}}_{y},\;{\hat {e}}_{z}\right)\mathbf {b} }](https://wikimedia.org/api/rest_v1/media/math/render/svg/fa6d12d471c0acdd76485fdb0bee988557d05806)
and observe that the inner product becomes by virtue of the orthonormality of the basis vectors
![{\displaystyle \left({\vec {a}},\;{\vec {b}}\right)=\mathbf {a} ^{\mathrm {T} }\mathbf {b} \equiv \left(a_{x},\;a_{y},\;a_{z}\right){\begin{pmatrix}b_{x}\\b_{y}\\b_{z}\end{pmatrix}}\equiv a_{x}b_{x}+a_{y}b_{y}+a_{z}b_{z}.}](https://wikimedia.org/api/rest_v1/media/math/render/svg/4e99aa04968be265744577e3a62fb572970df9dc)
The invariance of the inner product under
leads to
![{\displaystyle {\big (}\mathbf {R} \mathbf {a} {\big )}^{\mathrm {T} }\;\mathbf {R} \mathbf {b} =\mathbf {a} ^{\mathrm {T} }\mathbf {R} ^{\mathrm {T} }\;\mathbf {R} \mathbf {b} }](https://wikimedia.org/api/rest_v1/media/math/render/svg/a8623a661c165b199e605a408821c301974d3105)
since this holds for any pair a and b it follows that a rotation matrix satisfies
![{\displaystyle \mathbf {R} ^{\mathrm {T} }\mathbf {R} =\mathbf {E} }](https://wikimedia.org/api/rest_v1/media/math/render/svg/aba50dd70d0cf226465b4775852fe7af1c4279a2)
where E is the 3×3 identity matrix.
For finite-dimensional matrices one shows easily
![{\displaystyle \mathbf {R} ^{\mathrm {T} }\mathbf {R} =\mathbf {E} \quad \Longleftrightarrow \quad \mathbf {R} \mathbf {R} ^{\mathrm {T} }=\mathbf {E} .}](https://wikimedia.org/api/rest_v1/media/math/render/svg/d8eb13b3cf803f58756e871e9010c4798951ba79)