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.. _gimbal-lock:
###########
Gimbal lock
###########
See also: http://en.wikipedia.org/wiki/Gimbal_lock
Euler angles have a major deficiency, and that is, that it is possible,
in some rotation sequences, to reach a situation where two of the three
Euler angles cause rotation around the same axis of the object. In the
case below, rotation around the $x$ axis becomes indistinguishable in
its effect from rotation around the $z$ axis, so the $z$ and $x$ axis
angles collapse into one transformation, and the rotation reduces from
three degrees of freedom to two.
*******
Example
*******
Imagine that we are using the Euler angle convention of starting with a
rotation around the $x$ axis, followed by the $y$ axis, followed by the
$z$ axis.
Here we see a Spitfire aircraft, flying across the screen. The $x$ axis
is left to right (tail to nose), the $y$ axis is from the left wing tip
to the right wing tip (going away from the screen), and the $z$ axis is
from bottom to top:
.. image:: images/spitfire_0.png
We want to rotate the aircraft to look something like this:
.. image:: images/spitfire_hoped.png
We might start by doing an x rotation, making a slight roll with the left wing
tilting down (rotation about $x$) like this:
.. image:: images/spitfire_x.png
Let's say that the x rotation is -0.2 radians.
Then we do a pitch so we are pointing straight up (rotation around $y$ axis).
This is a rotation by $-\pi/2$ radians.
.. image:: images/spitfire_y.png
To get to our desired position from here, we need to do a turn of something
like 0.2 radians of the nose towards the viewer (and the tail away from the
viewer). All we have left is our z rotation (rotation around the $z$ axis.
Unfortunately, the current result of a rotation around the $z$ axis has now
become the same as a previous rotation around the $x$ axis. To see this, look
at the result of the rotation around the $y$ axis. Notice that the $x$ axis,
as was, is now aligned with the $z$ axis, as it is now. Rotating around the
$z$ axis will have exactly the same effect as adding an extra rotation around
the $x$ axis at the beginning. That means that, when there is a $y$ axis
rotation that rotates the $x$ axis onto the $z$ axis (a rotation of $\pm\pi/2$
around the $y$ axis) - the $x$ and $y$ axes are "locked" together.
This does not mean that we cannot do the rotations we need, only that we can't
do them by starting with the most obvious x and y rotations. In fact what we
will have to do is first rotate around x by $\pi/2 - 0.2$ radians, then do
a y rotation of $-\pi/2 + 0.2$ radians, and finally a z rotation of $-\pi/2$
radians. See the code below for the details.
**************************
Mathematics of gimbal lock
**************************
See :mod:`transforms3d.derivations.eulerangles`.
We see gimbal lock for this type of Euler axis convention, when
$\cos(\beta) = 0$, where $\beta$ is the angle of rotation around the $y$
axis. By "this type of convention" we mean using rotation around all 3
of the $x$, $y$ and $z$ axes, rather than using the same axis twice -
e.g. the physics convention of $z$ followed by $x$ followed by $z$ axis
rotation (the physics convention has different properties to its gimbal
lock).
We can show how gimbal lock works by creating a rotation matrix for the
three component rotations. Recall that, for a rotation of $\alpha$
radians around $x$, followed by a rotation $\beta$ around $y$, followed
by rotation $\gamma$ around $z$, the rotation matrix $R$ is:
.. math::
R = \left[\begin{matrix}\cos{\left (\beta \right )} \cos{\left (\gamma \right )} & \sin{\left (\alpha \right )} \sin{\left (\beta \right )} \cos{\left (\gamma \right )} - \sin{\left (\gamma \right )} \cos{\left (\alpha \right )} & \sin{\left (\alpha \right )} \sin{\left (\gamma \right )} + \sin{\left (\beta \right )} \cos{\left (\alpha \right )} \cos{\left (\gamma \right )}\\\sin{\left (\gamma \right )} \cos{\left (\beta \right )} & \sin{\left (\alpha \right )} \sin{\left (\beta \right )} \sin{\left (\gamma \right )} + \cos{\left (\alpha \right )} \cos{\left (\gamma \right )} & - \sin{\left (\alpha \right )} \cos{\left (\gamma \right )} + \sin{\left (\beta \right )} \sin{\left (\gamma \right )} \cos{\left (\alpha \right )}\\- \sin{\left (\beta \right )} & \sin{\left (\alpha \right )} \cos{\left (\beta \right )} & \cos{\left (\alpha \right )} \cos{\left (\beta \right )}\end{matrix}\right]
In our case the y rotation $\beta = -\pi / 2, \cos(\beta) = 0, \sin(\beta) =
-1$:
.. math::
R = \left[\begin{matrix}0 & - \sin{\left (\alpha \right )} \cos{\left (\gamma \right )} - \sin{\left (\gamma \right )} \cos{\left (\alpha \right )} & \sin{\left (\alpha \right )} \sin{\left (\gamma \right )} - \cos{\left (\alpha \right )} \cos{\left (\gamma \right )}\\0 & - \sin{\left (\alpha \right )} \sin{\left (\gamma \right )} + \cos{\left (\alpha \right )} \cos{\left (\gamma \right )} & - \sin{\left (\alpha \right )} \cos{\left (\gamma \right )} - \sin{\left (\gamma \right )} \cos{\left (\alpha \right )}\\1 & 0 & 0\end{matrix}\right]
From the `angle sum and difference identities
<http://en.wikipedia.org/wiki/List_of_trigonometric_identities#Angle_sum_and_difference_identities>`_
(see also `geometric proof
<http://www.themathpage.com/atrig/sum-proof.htm>`_, `Mathworld treatment
<http://mathworld.wolfram.com/TrigonometricAdditionFormulas.html>`_) we
remind ourselves that, for any two angles $\alpha$ and $\beta$:
.. math::
\sin(\alpha \pm \beta) = \sin \alpha \cos \beta \pm \cos \alpha \sin \beta \,
\cos(\alpha \pm \beta) = \cos \alpha \cos \beta \mp \sin \alpha \sin \beta
We can rewrite $R$ as:
.. math::
R = \left[\begin{matrix}0 & - W_{1} & - W_{2}\\0 & W_{2} & - W_{1}\\1 & 0 & 0\end{matrix}\right]
where:
.. math::
W_1 = \sin{\left (\alpha \right )} \cos{\left (\gamma \right )} +
\sin{\left (\gamma \right )} \cos{\left (\alpha \right )}
= \sin(\alpha + \gamma) \,
W_2 = - \sin{\left (\alpha \right )} \sin{\left (\gamma \right )} +
\cos{\left (\alpha \right )} \cos{\left (\gamma \right )}
= \cos(\alpha + \gamma)
We immediately see that $\alpha$ and $\gamma$ are going to lead to the same
transformations - the mathematical expression of the observation on the
spitfire above, that rotation around the $x$ axis is equivalent to rotation
about the $z$ axis.
It's easy to do the same set of reductions for the case where $\sin(\beta) =
1$; see http://www.gregslabaugh.name/publications/euler.pdf.
*******************
The example in code
*******************
Here is what our gimbal lock looks like in code:
>>> import numpy as np
>>> np.set_printoptions(precision=3, suppress=True) # neat printing
>>> from transforms3d.euler import euler2mat, mat2euler
>>> x_angle = -0.2
>>> y_angle = -np.pi / 2
>>> z_angle = -0.2
>>> R = euler2mat(x_angle, y_angle, z_angle, 'sxyz')
>>> R
array([[ 0. , 0.389, -0.921],
[-0. , 0.921, 0.389],
[ 1. , -0. , 0. ]])
This isn't the transformation we actually want because of the gimbal lock.
The gimbal lock means that ``x_angle`` and ``z_angle`` result in rotations
about the same axis of the object. So, we can add something to the
``x_angle`` and subtract the same value from ``z_angle`` to get the same
result:
>>> R = euler2mat(x_angle + 0.1, y_angle, z_angle - 0.1, 'sxyz')
>>> R
array([[ 0. , 0.389, -0.921],
[-0. , 0.921, 0.389],
[ 1. , -0. , 0. ]])
In fact, we could omit the z rotation entirely and put all the rotation into
the original x axis rotation and still get the same rotation matrix:
>>> R_dash = euler2mat(x_angle + z_angle, y_angle, 0, 'sxyz')
>>> np.allclose(R, R_dash)
True
So, there is no future in doing our transformations starting with this x and
y rotation, if we are rotating with this axis order. We can get the
transformation we actually want by doing the rotations in the order x, then z
then y, like this:
>>> R = euler2mat(x_angle, z_angle, y_angle, 'sxzy')
>>> R
array([[ 0. , 0.199, -0.98 ],
[-0.199, 0.961, 0.195],
[ 0.98 , 0.195, 0.039]])
We can get this same transformation using our original x, y, z rotation order,
but using different rotation angles:
>>> x_dash, y_dash, z_dash = mat2euler(R, 'sxyz')
>>> np.array((x_dash, y_dash, z_dash)) # np.array for print neatness
array([ 1.371, -1.371, -1.571])
>>> R = euler2mat(x_dash, y_dash, z_dash, 'sxyz')
>>> R
array([[ 0. , 0.199, -0.98 ],
[-0.199, 0.961, 0.195],
[ 0.98 , 0.195, 0.039]])
|
