Commit 4b9a8aeb authored by stevet's avatar stevet
Browse files

update notebook

parent 029ada66
......@@ -30,22 +30,9 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"# importing classical numpy objects\n",
"from numpy import zeros, array, identity, dot\n",
......@@ -61,7 +48,7 @@
"from curves import (bezier)\n",
"\n",
"#importing tools to plot bezier curves\n",
"from .plot_bezier import plotBezier\n",
"from curves.plot import (plotBezier)\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
......@@ -643,9 +630,20 @@
"execution_count": 19,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "No to_python (by-value) converter found for C++ type: boost::shared_ptr<curves::curve_abc<double, double, true, Eigen::Matrix<double, -1, 1, 0, -1, 1>, Eigen::Matrix<double, -1, 1, 0, -1, 1> > >",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-a42564ae9410>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m#first, plotting the complete piecewiseCurve is equivalent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mplotBezier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpiecewiseCurve\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mplotBezier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpiecewiseCurve\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurve_at_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mplotBezier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpiecewiseCurve\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurve_at_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"orange\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: No to_python (by-value) converter found for C++ type: boost::shared_ptr<curves::curve_abc<double, double, true, Eigen::Matrix<double, -1, 1, 0, -1, 1>, Eigen::Matrix<double, -1, 1, 0, -1, 1> > >"
]
},
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -685,22 +683,9 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"#first, split the variable curve\n",
"piecewiseCurve = variableBezier.split(array([[0.4, 0.8]]).T)\n",
......@@ -778,6 +763,13 @@
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
......
%% Cell type:markdown id: tags:
# Curve optimization with the curves library
%% Cell type:markdown id: tags:
The [curve library](https://github.com/loco-3d/curves) is a header-only C++ library (also binded in python) that allows you
to create curves, in arbitrary dimensions (2, 3, n).
Originally, the library focused on spline curves, but it has now been extended to generic polynomials, cubic hermite splines, Bezier curves and more.
A nice upcoming extension is the ability to design curves in the Special Euclidian group SE3.
However in this tutorial we are going to focus on a rather unique trait of the library, which is the ability to work with variable control points. Rather than being given a constant value, the control points can be expressed as the linear combination of one or several variables. The main advantage of this representation is that variable curves
can be automatically derivated or integrated with any effort.
The other interest of variable curves is the ability to easily formulate optimization problems, which will be the focus of this tutorial. We will use the python bindings of the curve library to go step-by-step to formulating and solving an optimization problem.
## The problem: trajectory fitting
We start with a simple, unconstrained problem.
Let us first consider a 3D curve:
%% Cell type:code id: tags:
``` python
# importing classical numpy objects
from numpy import zeros, array, identity, dot
from numpy.linalg import norm
import numpy as np
np.set_printoptions(formatter={'float': lambda x: "{0:0.1f}".format(x)})
#use array representation for binding eigen objects to python
import eigenpy
eigenpy.switchToNumpyArray()
#importing the bezier curve class
from curves import (bezier)
#importing tools to plot bezier curves
from .plot_bezier import plotBezier
from curves.plot import (plotBezier)
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
#We describe a degree 3 curve as a Bezier curve with 4 control points
waypoints = array([[1., 2., 3.], [-4., -5., -6.], [4., 5., 6.], [7., 8., 9.]]).transpose()
ref = bezier(waypoints)
#plotting the curve with its control points
plotBezier(ref,showControlPoints = True, color="g")
```
%%%% Output: display_data
%% Cell type:markdown id: tags:
We now assume that we only have partial information about this curve, and that we want to reconstruct it.
We will first generate a discretization of the curve to represent a temporal sampling:
%% Cell type:code id: tags:
``` python
numSamples = 10; fNumSamples = float(numSamples)
ptsTime = [ (ref(float(t) / fNumSamples), float(t) / fNumSamples) for t in range(numSamples+1)]
for el in ptsTime:
print el
```
%%%% Output: stream
(array([1.0, 2.0, 3.0]), 0.0)
(array([-0.1, 0.4, 0.9]), 0.1)
(array([-0.6, -0.4, -0.1]), 0.2)
(array([-0.5, -0.4, -0.2]), 0.3)
(array([0.1, 0.2, 0.4]), 0.4)
(array([1.0, 1.2, 1.5]), 0.5)
(array([2.2, 2.6, 3.0]), 0.6)
(array([3.4, 4.1, 4.7]), 0.7)
(array([4.7, 5.6, 6.4]), 0.8)
(array([6.0, 6.9, 7.9]), 0.9)
(array([7.0, 8.0, 9.0]), 1.0)
%% Cell type:markdown id: tags:
Each entry of ptsTime is a couple (position, time) that describes our input data.
### Sanity check
Let's first solve a trivial problem, to see if we can reconstruct the curve with a polynomial
of same degree.
To achieve this we will use the problemDefinition class, which will automatically generate the variable expression of the curve
%% Cell type:code id: tags:
``` python
from curves.optimization import (problem_definition, setup_control_points)
#dimension of our problem (here 3 as our curve is 3D)
dim = 3
refDegree = 3
pD = problem_definition(dim)
pD.degree = refDegree #we want to fit a curve of the same degree as the reference curve for the sanity check
#generates the variable bezier curve with the parameters of problemDefinition
problem = setup_control_points(pD)
#for now we only care about the curve itself
variableBezier = problem.bezier()
```
%% Cell type:markdown id: tags:
The evaluation of a variable Bezier returns a matrix B and a vector c, such
that B x + c , with x a vector variable, defines the value of the curve
%% Cell type:code id: tags:
``` python
linearVariable = variableBezier(0.)
print "B: \n", linearVariable.B()
print "c:\n",linearVariable.c()
print "Shape of B: ", linearVariable.B().shape
```
%%%% Output: stream
B:
[[1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]
[0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]
[0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]]
c:
[0.0 0.0 0.0]
Shape of B: (3, 12)
%% Cell type:markdown id: tags:
B has 3 rows and 12 columns. Because the fitting curve is of degree 3, it has 4 control points of dimension 3, which gives a variable of size 12. The row number also matches the dimension of the problem.
Then A is zero everywhere, expect for the first 3 columns that contain the identity. This is expected as the start of a Bezier curve is equal to the first control point.
If we evaluate variableBezier at t = 0.2 for instance, we get a more complex expression:
%% Cell type:code id: tags:
``` python
print "B: \n", variableBezier(0.2).B()
```
%%%% Output: stream
B:
[[0.5 0.0 0.0 0.4 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0]
[0.0 0.5 0.0 0.0 0.4 0.0 0.0 0.1 0.0 0.0 0.0 0.0]
[0.0 0.0 0.5 0.0 0.0 0.4 0.0 0.0 0.1 0.0 0.0 0.0]]
%% Cell type:markdown id: tags:
With variableBezier, we can easily define a least square problem to reconstruct the original curve.
We just have to formulate a cost function that, for each sample in ptsTime minimizes the distance between the evaluation of variableBezier and the sampled point. We define it as follows:
%% Cell type:code id: tags:
``` python
#least square form of ||Ax-b||**2
def to_least_square(A, b):
return dot(A.T, A), - dot(A.T, b)
def genCost(variableBezier, ptsTime):
#first evaluate variableBezier for each time sampled
allsEvals = [(variableBezier(time), pt) for (pt,time) in ptsTime]
#then compute the least square form of the cost for each points
allLeastSquares = [to_least_square(el.B(), el.c() + pt) for (el, pt) in allsEvals]
#and finally sum the costs
Ab = [sum(x) for x in zip(*allLeastSquares)]
return Ab[0], Ab[1]
A, b = genCost(variableBezier, ptsTime)
```
%% Cell type:markdown id: tags:
Here we use quadprog to solve the least square. Because there are no constraint this might seem overkill, however we will introduce them soon enough.
%% Cell type:code id: tags:
``` python
from qp import quadprog_solve_qp
res = quadprog_solve_qp(A, b)
```
%% Cell type:markdown id: tags:
Let's check whether our optimization worked !
We can transform the variable Bezier as a regular Bezier curve as follows, and plot the result to verify that the curves match.
%% Cell type:code id: tags:
``` python
def evalAndPlot(variableBezier, res):
fitBezier = variableBezier.evaluate(res.reshape((-1,1)) )
#plot reference curve in blue, fitted curve in green
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
plotBezier(ref, ax = ax, linewidth=4.) #thicker line to visualize overlap
plotBezier(fitBezier, ax = ax, color ="g", linewidth=3.)
plt.show()
return fitBezier
fitBezier = evalAndPlot(variableBezier, res)
```
%%%% Output: display_data
%% Cell type:markdown id: tags:
### initial and terminal constraints
Let's try to fit the reference curve with a curve of lesser degree
%% Cell type:code id: tags:
``` python
pD.degree = refDegree - 1
problem = setup_control_points(pD)
variableBezier = problem.bezier()
A, b = genCost(variableBezier, ptsTime)
res = quadprog_solve_qp(A, b)
fitBezier = evalAndPlot(variableBezier, res)
```
%%%% Output: display_data
%% Cell type:markdown id: tags:
We can see that the initial and goal positions are not reached.
A constraint_flag can be used to impose constraints on the initial/goal positions
and derivatives if required.
Let's rewrite simplefit to handle such case
%% Cell type:code id: tags:
``` python
from curves.optimization import constraint_flag
pD.flag = constraint_flag.INIT_POS | constraint_flag.END_POS
#set initial position
pD.init_pos = array([ptsTime[ 0][0]]).T
#set end position
pD.end_pos = array([ptsTime[-1][0]]).T
problem = setup_control_points(pD)
variableBezier = problem.bezier()
```
%% Cell type:markdown id: tags:
By imposing the initial and final position, we effectively reduce the number of variables by 6:
%% Cell type:code id: tags:
``` python
print "Shape of B: ", variableBezier(0).B().shape
```
%%%% Output: stream
Shape of B: (3, 3)
%% Cell type:markdown id: tags:
The least squares problem then has the following solution
%% Cell type:code id: tags:
``` python
prob = setup_control_points(pD)
variableBezier = prob.bezier()
A, b = genCost(variableBezier, ptsTime)
res = quadprog_solve_qp(A, b)
_ = evalAndPlot(variableBezier, res)
```
%%%% Output: display_data
%% Cell type:markdown id: tags:
To impose constraints on the derivatives, we can activate the appropriate constraint flags as follows.
Note that derivatives constraints on velocities will only be considered if the constraints on position are also active.
For instance to impose a 0 velocity and acceleration at the initial and goal states we can proceed as follows:
%% Cell type:code id: tags:
``` python
#values are 0 by default, so if the constraint is zero this can be skipped
pD.init_vel = array([[0., 0., 0.]]).T
pD.init_acc = array([[0., 0., 0.]]).T
pD.end_vel = array([[0., 0., 0.]]).T
pD.end_acc = array([[0., 0., 0.]]).T
pD.flag = constraint_flag.END_POS | constraint_flag.INIT_POS | constraint_flag.INIT_VEL | constraint_flag.END_VEL | constraint_flag.INIT_ACC | constraint_flag.END_ACC
```
%% Cell type:markdown id: tags:
However, the definition of the variable problem will result in an error. Do you know why ?
%% Cell type:code id: tags:
``` python
try:
prob = setup_control_points(pD)
except RuntimeError,e:
print e
```
%%%% Output: stream
In setup_control_points; too many constraints for the considered degree
%% Cell type:markdown id: tags:
Indeed, there are not enough variables left in the problem to satisfy the constraints. We need to increase the degree of the curve:
%% Cell type:code id: tags:
``` python
pD.degree = refDegree + 4
prob = setup_control_points(pD)
variableBezier = prob.bezier()
A, b = genCost(variableBezier, ptsTime)
res = quadprog_solve_qp(A, b)
fitBezier = evalAndPlot(variableBezier, res)
```
%%%% Output: display_data
%% Cell type:markdown id: tags:
We can check that the derivatives of the curve are 0 at start and end
%% Cell type:code id: tags:
``` python
print "initial velocity", fitBezier.derivate(fitBezier.min(),1)
print "initial acceleration", fitBezier.derivate(fitBezier.min(),2)
print "end velocity", fitBezier.derivate(fitBezier.max(),1)
print "end acceleration", fitBezier.derivate(fitBezier.max(),2)
```
%%%% Output: stream
initial velocity [0.0 0.0 0.0]
initial acceleration [0.0 0.0 0.0]
end velocity [0.0 0.0 0.0]
end acceleration [0.0 0.0 0.0]
%% Cell type:markdown id: tags:
Of course, with such constraints the curve does not really look like the original one anymore.
Although it is not recommended, the library is robust enough to allow for adding an arbitrary number of control points.
Just for fun, let's add 60 more control points and check that the curve is matched better
%% Cell type:code id: tags:
``` python
pD.degree = refDegree + 60
prob = setup_control_points(pD)
variableBezier = prob.bezier()
A, b = genCost(variableBezier, ptsTime)
#regularization matrix
reg = identity(A.shape[1]) * 0.001
res = quadprog_solve_qp(A + reg, b)
fitBezier = evalAndPlot(variableBezier, res)
```
%%%% Output: display_data
%% Cell type:markdown id: tags:
## Adding equality and inequality constraints
Suppose we want to add specific constraint.
For instance, we want that the velocity be exactly 0 at t = 0.8, additionally to the start and goal positions being satisfied. This can be done easily by obtaining the variable equation for the variable curve at that time.
%% Cell type:code id: tags:
``` python
#set initial / terminal constraints
pD.flag = constraint_flag.END_POS | constraint_flag.INIT_POS
pD.degree = refDegree
prob = setup_control_points(pD)
variableBezier = prob.bezier()
#get value of the curve first order derivative at t = 0.8
t08Constraint = variableBezier.derivate(0.8,1)
target = zeros(3)
A, b = genCost(variableBezier, ptsTime)
#solve optimization problem with quadprog
res = quadprog_solve_qp(A, b, C=t08Constraint.B(), d=target - t08Constraint.c())
fitBezier = evalAndPlot(variableBezier, res)
assert norm(fitBezier.derivate(0.8,1) - target) <= 0.001
```
%%%% Output: display_data
%% Cell type:markdown id: tags:
Of course, inequality constraints can be added in a similar way
## Constraining sub-parts of the curve
Now suppose we want to constrain entirely parts of a curve. One common way to address this is to discretize the curve, and write as many constraints as discretization points.
Alternatively, this can be achieved continuously by splitting the Bezier curve continuously, and putting constraints on the control points of the relevant parts.
let's first explain how curve splitting works before writing a problem.
Here is the code that splits our reference curve into two distinct curves at a time t = 0.6
%% Cell type:code id: tags:
``` python
#returns a curve composed of the split curves, 2 in our case
piecewiseCurve = ref.split(array([[0.6]]).T)
#displaying the obtained curves
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
#first, plotting the complete piecewiseCurve is equivalent
plotBezier(piecewiseCurve, ax = ax, linewidth=10., color = "b")
plotBezier(piecewiseCurve.curve_at_index(0), ax = ax, linewidth=4., color = "r")
plotBezier(piecewiseCurve.curve_at_index(1), ax = ax, linewidth=4., color = "orange")
```
%%%% Output: error
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-19-a42564ae9410> in <module>()
9 #first, plotting the complete piecewiseCurve is equivalent
10 plotBezier(piecewiseCurve, ax = ax, linewidth=10., color = "b")
---> 11 plotBezier(piecewiseCurve.curve_at_index(0), ax = ax, linewidth=4., color = "r")
12 plotBezier(piecewiseCurve.curve_at_index(1), ax = ax, linewidth=4., color = "orange")
TypeError: No to_python (by-value) converter found for C++ type: boost::shared_ptr<curves::curve_abc<double, double, true, Eigen::Matrix<double, -1, 1, 0, -1, 1>, Eigen::Matrix<double, -1, 1, 0, -1, 1> > >
%%%% Output: display_data
%% Cell type:markdown id: tags:
The split is achieved by the De Casteljau algorithm. The continuity at the split location is infinite.
Of course, the split will also work for variable Bezier curves.
We can exploit the convexity of Bezier curves to continuously impose constraints on a given interval of the curve.
If the control points of the sub curve satisfy a set of linear constraints, then the entire sub-curve satisfies the constraint.
For instance, let us impose the z value to be negative between t = 0.4 and t= O.8
%% Cell type:code id: tags:
``` python
#first, split the variable curve
piecewiseCurve = variableBezier.split(array([[0.4, 0.8]]).T)
constrainedCurve = piecewiseCurve.curve_at_index(1)
#find the number of variables
problemSize = prob.numVariables * dim
#find the number of constraints, as many as waypoints
nConstraints = constrainedCurve.nbWaypoints
waypoints = constrainedCurve.waypoints()
ineqMatrix = zeros((nConstraints, problemSize))
ineqVector = zeros(nConstraints)
#finding the z equation of each control point
for i in range(nConstraints):
wayPoint = constrainedCurve.waypointAtIndex(i)
ineqMatrix[i,:] = wayPoint.B()[2,:]
ineqVector[i] = -wayPoint.c()[2]
res = quadprog_solve_qp(A, b, G=ineqMatrix, h = ineqVector)
fitBezier = variableBezier.evaluate(res.reshape((-1,1)) )
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
#now plotting the obtained curve, in red the concerned part
piecewiseFit = fitBezier.split(array([[0.4, 0.8]]).T)
plotBezier(piecewiseFit.curve_at_index(0), ax = ax, linewidth=4., color = "b")
plotBezier(piecewiseFit.curve_at_index(1), ax = ax, linewidth=4., color = "r")
plotBezier(piecewiseFit.curve_at_index(2), ax = ax, linewidth=4., color = "b")
#plotting the plane z = 0
xx, yy = np.meshgrid(range(20), range(20))
# calculate corresponding z
z = (0 * xx - 0 * yy )
# plot the surface
ax.plot_surface(xx, yy, z, alpha=0.2)
plt.show()
```
%%%% Output: display_data
%% Cell type:code id: tags: