From 5cb3e5d1810986d2db5eebec55e0e6828e9f379a Mon Sep 17 00:00:00 2001
From: Guilhem Saurel <guilhem.saurel@gmail.com>
Date: Fri, 8 Feb 2019 19:54:23 +0100
Subject: [PATCH] add CI and badges
---
.gitlab-ci.yml | 1 +
README.md | 18 +++++++++++-------
2 files changed, 12 insertions(+), 7 deletions(-)
create mode 100644 .gitlab-ci.yml
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
new file mode 100644
index 0000000..932c3e6
--- /dev/null
+++ b/.gitlab-ci.yml
@@ -0,0 +1 @@
+include: http://rainboard.laas.fr/project/hpp-bezier-com-traj/.gitlab-ci.yml
diff --git a/README.md b/README.md
index a914ee5..974db28 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,9 @@
# bezier_COM_Traj
+[](https://gepgitlab.laas.fr/humanoid-path-planner/hpp-bezier-com-traj/commits/master)
+[](http://projects.laas.fr/gepetto/doc/humanoid-path-planner/hpp-bezier-com-traj/master/coverage/)
+
+
Copyright 2018 LAAS-CNRS
Authors: Pierre Fernbach and Steve Tonneau
@@ -11,7 +15,7 @@ The trajectories are genererated through the resolution of convex optimization (
The library is implemented in C++, but also provides Python bindings.
-Two types of applications can be used so far:
+Two types of applications can be used so far:
- First, zero step capturability: Given the centroidal state of a robot, determines whether it is possible for the robot to come to a stop without violating frictional constraints. In this formulation, the problem can be solved continuously, and angular momentum constraints can be used.
- Second, the general case (which encompasses zero step capturability):
@@ -79,7 +83,7 @@ from bezier_com_traj import * #the actual library
# create an Equilibrium solver, for a robot of 54 kilos. We linearize the friction cone to four generating rays
-eq = Equilibrium("test", 54., 4)
+eq = Equilibrium("test", 54., 4)
# Now define some contact points ...
from numpy import array, asmatrix, matrix
@@ -91,7 +95,7 @@ P = asmatrix(array([array([x,y,0]) for x in [-0.05,0.05] for y in [-0.1,0.1]]))
z = array([0.,0.,1.])
N = asmatrix(array([z for _ in range(4)]))
-#setting contact positions and normals, as well as friction coefficient of 0.3
+#setting contact positions and normals, as well as friction coefficient of 0.3
#EQUILIBRIUM_ALGORITHM_PP is the algorithm that will always be used for our problems
eq.setNewContacts(asmatrix(P),asmatrix(N),0.3,EquilibriumAlgorithm.EQUILIBRIUM_ALGORITHM_PP)
@@ -105,13 +109,13 @@ c0 = matrix([0.,0.,1.]) .T
#we set the inital speed dc0 to a rather slow 10 cm / s along the x axis
dc0 = matrix([0.1,0.,0.]).T
-l0 = matrix([0.,0.,0.]).T
+l0 = matrix([0.,0.,0.]).T
T = 1.2
tstep = -1.
```
And finally, some optimization parameters:
-The total duration of the trajectory, as well as
+The total duration of the trajectory, as well as
the discretization step. If the discretization step is < 0,
then the continuous formulation is used
@@ -133,7 +137,7 @@ print result.success
```
The found centroidal trajectory is accessible from the returned object, only if the problem
-was feasible
+was feasible
```
res.c_of_t # a bezier curve object describing the com trajectory
@@ -145,6 +149,6 @@ print np.linalg.norm(dc_of_t(dc_of_t.max()))
```
refer to the [test file](https://gitlab.com/stonneau/bezier_COM_traj/blob/master/python/test/binding_tests.py) for more advanced problems, including kinematic constraints,
-mutiple contact phases handling and angular momentum
+mutiple contact phases handling and angular momentum
--
GitLab