diff --git a/crocoddyl/__init__.py b/crocoddyl/__init__.py
index ecca59a228e0eca8b602c8cb1b3442cc7442c218..7483b5aaa5c3760bc8a71e8892d68b8b334cb12b 100644
--- a/crocoddyl/__init__.py
+++ b/crocoddyl/__init__.py
@@ -35,3 +35,4 @@ from .solver import SolverAbstract
from .state import StateAbstract, StateNumDiff, StatePinocchio, StateVector
from .unicycle import ActionModelUnicycle, ActionModelUnicycleVar, StateUnicycle
from .utils import a2m, absmax, absmin, m2a
+from .plots import *
diff --git a/crocoddyl/plots.py b/crocoddyl/plots.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf821b95d5918426790dd70b899d02418ab23621
--- /dev/null
+++ b/crocoddyl/plots.py
@@ -0,0 +1,56 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+class PlotUAM:
+ def __init__(self, stateTraj, controlTraj, knots, dt, d, cf, cm):
+ self.stateTraj = stateTraj
+ self.controlTraj = controlTraj
+ self.knots = knots
+ self.dt = dt
+ self.d = d
+ self.cf = cf
+ self.cm = cm
+ self.PlotDataType = PlotDataUAM(self)
+
+ def plotMotorForces(self):
+ fig, axs = plt.subplots(2,2, figsize=(15,10))
+ fig.suptitle('Motor forces')
+ t = self.PlotDataType.t
+ axs[0, 0].plot(t,self.PlotDataType.control[:,0])
+ axs[0, 0].set_title('Motor 1')
+ axs[0, 1].plot(t,self.PlotDataType.control[:,1])
+ axs[0, 1].set_title('Motor 2')
+ axs[1, 0].plot(t,self.PlotDataType.control[:,2])
+ axs[1, 0].set_title('Motor 3')
+ axs[1, 1].plot(t,self.PlotDataType.control[:,3])
+ axs[1, 1].set_title('Motor 4')
+
+ return fig,axs
+
+ def plotActuation(self):
+ fig, axs = plt.subplots(1,2, figsize=(15,10))
+ fig.suptitle('Motor forces')
+ t = self.PlotDataType.t
+ axs[0].plot(t,self.PlotDataType.M[:,0], t,self.PlotDataType.M[:,1], t,self.PlotDataType.M[:,2])
+ axs[0].set_title('Moments')
+ axs[0].legend(['Mx','My','Mz'])
+ axs[1].plot(t,self.PlotDataType.thrust)
+ axs[1].set_title('Motor 2')
+
+
+ return fig,axs
+
+class PlotDataUAM():
+ def __init__(self, model):
+ self.t = np.arange(0, model.knots*model.dt, model.dt)
+ self.t_state = np.append(self.t, self.t[-1]+model.dt)
+ self.control = np.vstack(model.controlTraj)
+ self.thrust = self.control[:,0]+self.control[:,1]+self.control[:,2]+self.control[:,3]
+ Mx = model.d*(-self.control[:,0]+self.control[:,1]+self.control[:,2]-self.control[:,3])
+ My = model.d*(-self.control[:,0]+self.control[:,1]-self.control[:,2]+self.control[:,3])
+ Mz = model.cm/model.cf*(-self.control[:,0]-self.control[:,1]+self.control[:,2]+self.control[:,3])
+
+ self.M = np.zeros([np.size(self.control,1), 3])
+ self.M[:, 0] = Mx
+ self.M[:, 1] = My
+ self.M[:, 2] = Mz
diff --git a/crocoddyl/robots.py b/crocoddyl/robots.py
index 00d0e0c23aa07e04446fdb522b51d5be62881fea..802d504bb03d5263deba23015da79d310330df9f 100644
--- a/crocoddyl/robots.py
+++ b/crocoddyl/robots.py
@@ -132,6 +132,8 @@ def loadKinton(modelPath='/opt/openrobots/share/example-robot-data'):
robot = RobotWrapper.BuildFromURDF(modelPath + URDF_SUBPATH, [modelPath], pinocchio.JointModelFreeFlyer())
robot.q0.flat[7:] = [0, 0, 0, 0, 0, 0]
robot.model.referenceConfigurations["initial_pose"] = robot.q0
+ robot.q0.flat[7:] = [0, 0.2, 1.7, 2, 0, 0]
+ robot.model.referenceConfigurations["centered"] = robot.q0
return robot
def loadKintonArm(modelPath='/opt/openrobots/share/example-robot-data'):
diff --git a/examples/notebooks/kinton_fixed_base.ipynb b/examples/notebooks/kinton_fixed_base.ipynb
index dad285041ad10db2eaef228e0e630ec32cc823bb..df79af6a9602d6fb0a9628cd187e9db746229e4c 100644
--- a/examples/notebooks/kinton_fixed_base.ipynb
+++ b/examples/notebooks/kinton_fixed_base.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -11,8 +11,8 @@
"import numpy as np\n",
"\n",
"robot = loadKintonArm()\n",
- "#robot.initDisplay(loadModel=True)\n",
- "#robot.display(robot.q0)\n",
+ "robot.initViewer(loadModel=True)\n",
+ "robot.display(robot.q0)\n",
"\n",
"robot.framesForwardKinematics(robot.q0)\n",
"\n",
@@ -23,22 +23,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "ename": "AttributeError",
- "evalue": "'NoneType' object has no attribute 'viewer'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-6-cd53aa81cd48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Plot goal frame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mrobot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviewer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddXYZaxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'world/framegoal'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.015\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mrobot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviewer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapplyConfiguration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'world/framegoal'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_pos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtarget_quat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_quat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_quat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_quat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mrobot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviewer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrefresh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/opt/openrobots/lib/python2.7/site-packages/pinocchio/robot_wrapper.pyc\u001b[0m in \u001b[0;36mviewer\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mviewer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviewer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 217\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msetVisualizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy_models\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'viewer'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"target_pos = np.array([-0.1,-0.05,-0.15])\n",
"target_quat = pin.Quaternion(.4, .02, -.5, .7)\n",
@@ -64,7 +51,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -92,1269 +79,25 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
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- " array([ 0.86953931, -0.58633231, -0.82798614, -1.64831962, 0.55190896,\n",
- " 1.80801137]),\n",
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- " 2.07472138]),\n",
- " array([ 0.4764655 , -0.84946206, 0.22134384, -2.23923687, 1.44338066,\n",
- " 2.37952862])],\n",
- " True)"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-4-f74b548defae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m# Solving it with the DDP algorithm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mddp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/ddp.pyc\u001b[0m in \u001b[0;36msolve\u001b[0;34m(self, maxiter, init_xs, init_us, isFeasible, regInit)\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputeDirection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecalc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecalc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 105\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mArithmeticError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0mrecalc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/ddp.pyc\u001b[0m in \u001b[0;36mcomputeDirection\u001b[0;34m(self, recalc)\u001b[0m\n\u001b[1;32m 59\u001b[0m \"\"\"\n\u001b[1;32m 60\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 62\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackwardPass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/ddp.pyc\u001b[0m in \u001b[0;36mcalc\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 41\u001b[0m \"\"\" Compute the tangent (LQR) model.\n\u001b[1;32m 42\u001b[0m \"\"\"\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misFeasible\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;31m# Gap store the state defect from the guess to feasible (rollout) trajectory, i.e.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/shooting.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, xs, us)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mus\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunningModels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunningDatas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminalModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminalData\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunningDatas\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mterminalData\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/integrated_action.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, data, x, u, recalc)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mdxnext_dx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdxnext_ddx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mState\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mJintegrate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mda_dx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mda_du\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/differential_action.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, data, x, u, recalc)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMinv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m \u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputeRNEADerivatives\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 172\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mnv\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMinv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtau_dq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnv\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMinv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtau_dv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
}
],
"source": [
@@ -1388,16 +131,7 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/diagnostic.py:75: DeprecatedWarning: Call to deprecated function initDisplay. Use initViewer\n",
- " robot.initDisplay(loadModel=True)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from crocoddyl.diagnostic import displayTrajectory\n",
"\n",
@@ -1435,7 +169,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.16"
+ "version": "2.7.12"
}
},
"nbformat": 4,
diff --git a/examples/notebooks/kinton_flying_base.ipynb b/examples/notebooks/kinton_flying_base.ipynb
index c37bf320d9121f118a33155357499842492c37e0..ce686bdafb8a3e44c36a45a8a2ac07b3b93b4187 100644
--- a/examples/notebooks/kinton_flying_base.ipynb
+++ b/examples/notebooks/kinton_flying_base.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -14,7 +14,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -30,7 +30,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -47,7 +47,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -112,11 +112,27 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {
"scrolled": true
},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-10-b175ebf9a54b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquasiStatic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdifferential\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefaultState\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIntegratedActionModelEuler\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m for m, d in zip(fddp.problem.runningModels, fddp.problem.runningDatas)]\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0mxs0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mproblem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialState\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfddp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/flying.pyc\u001b[0m in \u001b[0;36mquasiStatic\u001b[0;34m(self, data, x)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnq\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 74\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactuation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mnu\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/flying.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, data, x, u, recalc)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputeJointJacobians\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdateFramePlacements\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcosts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcosts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/cost.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, data, x, u, recalc)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0mnr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcosts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcosts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcost\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalcDiff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecalc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/cost.pyc\u001b[0m in \u001b[0;36mcalcDiff\u001b[0;34m(self, data, x, u, recalc)\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcalc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[0mnq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 385\u001b[0;31m \u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdateFramePlacements\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpinocchio\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 386\u001b[0m J = np.dot(\n\u001b[1;32m 387\u001b[0m \u001b[0mpinocchio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mJlog6\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrMf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
"source": [
"# DEFINING THE SHOOTING PROBLEM & SOLVING\n",
"\n",
@@ -152,7 +168,7 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -161,7 +177,7 @@
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -197,56 +213,9 @@
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.text.Text at 0x7fd690bfa590>"
- ]
- },
- "execution_count": 69,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fd6c852a150>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fd698f71090>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x7fd690bb2e50>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"t = np.arange(0., 1, dt)\n",
@@ -302,7 +271,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.16"
+ "version": "2.7.12"
}
},
"nbformat": 4,
diff --git a/examples/notebooks/kinton_flying_ee.ipynb b/examples/notebooks/kinton_flying_ee.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5bc2d31dc01a91b333e4c15a004f0c338824f149
--- /dev/null
+++ b/examples/notebooks/kinton_flying_ee.ipynb
@@ -0,0 +1,523 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from crocoddyl import *\n",
+ "import pinocchio as pin\n",
+ "import numpy as np\n",
+ "from crocoddyl.diagnostic import displayTrajectory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# LOAD ROBOT\n",
+ "robot = loadKinton()\n",
+ "robot.initViewer(loadModel=True)\n",
+ "rmodel = robot.model\n",
+ "\n",
+ "q0 = rmodel.referenceConfigurations[\"centered\"]\n",
+ "robot.display(q0)\n",
+ "robot.framesForwardKinematics(q0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# DEFINE TARGET POSITION\n",
+ "target_pos = np.array([0,0,1])\n",
+ "[ -0.7071068, 0, 0.7071068, 0 ]\n",
+ "target_quat = pin.Quaternion(-1, 0, 1, 0)\n",
+ "target_quat.normalize()\n",
+ "\n",
+ "# Plot goal frame\n",
+ "robot.viewer.gui.addXYZaxis('world/framegoal', [1., 0., 0., 1.], .015, 4)\n",
+ "robot.viewer.gui.applyConfiguration('world/framegoal', target_pos.tolist() + [target_quat[0], target_quat[1], target_quat[2], target_quat[3]])\n",
+ "robot.viewer.gui.refresh()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ACTUATION MODEL\n",
+ "distanceRotorCOG = 0.1525\n",
+ "cf = 6.6e-5\n",
+ "cm = 1e-6\n",
+ "actModel = ActuationModelUAM(robot.model, distanceRotorCOG, cf, cm)\n",
+ "\n",
+ "# COST MODEL\n",
+ "# Create a cost model per the running and terminal action model.\n",
+ "runningCostModel = CostModelSum(robot.model, actModel.nu)\n",
+ "terminalCostModel = CostModelSum(robot.model, actModel.nu)\n",
+ "\n",
+ "frameName = 'link6'\n",
+ "state = StatePinocchio(robot.model)\n",
+ "SE3ref = pin.SE3()\n",
+ "SE3ref.translation = target_pos.reshape(3,1)\n",
+ "SE3ref.rotation = target_quat.matrix()\n",
+ "\n",
+ "\n",
+ "wBasePos = [1]\n",
+ "wBaseOri = [5]\n",
+ "wArmPos = [1]\n",
+ "wBaseVel = [10]\n",
+ "wBaseRate = [10]\n",
+ "wArmVel = [10] \n",
+ "stateWeights = np.array(wBasePos * 3 + wBaseOri * 3 + wArmPos * (robot.model.nv - 6) + wBaseVel * robot.model.nv)\n",
+ "controlWeights = np.array([0.1]*4 + [100]*6)\n",
+ "\n",
+ "goalTrackingCost = CostModelFramePlacement(rmodel,\n",
+ " frame=rmodel.getFrameId(frameName),\n",
+ " ref=SE3ref,\n",
+ " nu =actModel.nu)\n",
+ "\n",
+ "xRegCost = CostModelState(rmodel, \n",
+ " state, \n",
+ " ref=state.zero(), \n",
+ " nu=actModel.nu,\n",
+ " activation=ActivationModelWeightedQuad(stateWeights))\n",
+ "uRegCost = CostModelControl(rmodel, \n",
+ " nu=robot.\n",
+ " model.nv-2,\n",
+ " activation = ActivationModelWeightedQuad(controlWeights))\n",
+ "uLimCost = CostModelControl(rmodel, \n",
+ " nu=robot.\n",
+ " model.nv-2,\n",
+ " activation = ActivationModelInequality(np.array([0.1, 0.1, 0.1, 0.1, -1, -1, -1, -1, -1, -1]), \n",
+ " np.array([5, 5, 5, 5, 1, 1, 1, 1, 1, 1])))\n",
+ "\n",
+ "# Then let's add the running and terminal cost functions\n",
+ "runningCostModel.addCost(name=\"pos\", weight=0.1, cost=goalTrackingCost)\n",
+ "runningCostModel.addCost(name=\"regx\", weight=1e-4, cost=xRegCost)\n",
+ "runningCostModel.addCost(name=\"regu\", weight=1e-6, cost=uRegCost)\n",
+ "# runningCostModel.addCost(name=\"limu\", weight=1e-3, cost=uLimCost)\n",
+ "terminalCostModel.addCost(name=\"pos\", weight=0, cost=goalTrackingCost)\n",
+ "\n",
+ "# DIFFERENTIAL ACTION MODEL\n",
+ "runningModel = IntegratedActionModelEuler(DifferentialActionModelUAM(robot.model, actModel, runningCostModel))\n",
+ "terminalModel = IntegratedActionModelEuler(DifferentialActionModelUAM(robot.model, actModel, terminalCostModel))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 0 9.08198e+00 4.56473e-01 1.97554e+01 1.00000e-09 1.00000e-09 0.5000 0\n",
+ " 1 4.04502e+00 2.73160e-01 1.52749e+01 1.00000e-09 1.00000e-09 0.5000 0\n",
+ " 2 2.95386e+00 9.48071e-02 4.97010e+00 1.00000e-09 1.00000e-09 0.2500 0\n",
+ " 3 2.46366e+00 5.13997e-02 2.99065e+00 1.00000e-09 1.00000e-09 0.5000 0\n",
+ " 4 1.82967e+00 4.92640e-02 1.93473e+00 1.00000e-09 1.00000e-09 0.5000 0\n",
+ " 5 1.76305e+00 1.16694e-02 6.93346e-01 1.00000e-09 1.00000e-09 0.5000 0\n",
+ " 6 1.56127e+00 1.93517e-02 5.77282e-01 1.00000e-09 1.00000e-09 0.5000 0\n",
+ " 7 1.51972e+00 4.57215e-03 2.26322e-01 1.00000e-09 1.00000e-09 0.5000 0\n",
+ " 8 1.47284e+00 6.55408e-03 1.88064e-01 1.00000e-09 1.00000e-09 0.5000 0\n",
+ " 9 1.42210e+00 3.88011e-03 1.41680e-01 1.00000e-09 1.00000e-09 0.5000 0\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 10 1.39035e+00 7.66291e-04 7.05487e-02 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 11 1.36621e+00 5.54061e-04 4.43489e-02 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 12 1.35191e+00 6.60168e-04 2.63324e-02 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 13 1.33763e+00 6.67983e-04 3.33965e-02 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 14 1.31112e+00 1.07201e-03 5.27193e-02 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 15 1.28462e+00 7.90939e-04 4.01468e-02 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 16 1.27488e+00 1.77533e-04 1.22286e-02 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 17 1.27045e+00 9.30922e-05 5.35113e-03 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 18 1.26834e+00 4.14692e-05 2.48093e-03 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 19 1.26725e+00 1.98810e-05 1.26676e-03 1.00000e-09 1.00000e-09 1.0000 1\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 20 1.26666e+00 1.03064e-05 6.83409e-04 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 21 1.26632e+00 5.53840e-06 3.87870e-04 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 22 1.26611e+00 3.14271e-06 2.30334e-04 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 23 1.26598e+00 1.84439e-06 1.42197e-04 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 24 1.26590e+00 1.12726e-06 9.06540e-05 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 25 1.26585e+00 7.07977e-07 5.93357e-05 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 26 1.26581e+00 4.57519e-07 3.96762e-05 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 27 1.26579e+00 3.01730e-07 2.69959e-05 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 28 1.26577e+00 2.02930e-07 1.86307e-05 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 29 1.26576e+00 1.38494e-07 1.30080e-05 1.00000e-09 1.00000e-09 1.0000 1\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 30 1.26575e+00 9.57989e-08 9.16982e-06 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 31 1.26574e+00 6.69611e-08 6.51573e-06 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 32 1.26574e+00 4.72426e-08 4.66059e-06 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 33 1.26574e+00 3.35797e-08 3.35217e-06 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 34 1.26573e+00 2.40247e-08 2.42232e-06 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 35 1.26573e+00 1.72804e-08 1.75728e-06 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 36 1.26573e+00 1.24872e-08 1.27906e-06 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 37 1.26573e+00 9.05825e-09 9.33607e-07 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 38 1.26573e+00 6.59276e-09 6.83084e-07 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 39 1.26573e+00 4.81171e-09 5.00804e-07 1.00000e-09 1.00000e-09 1.0000 1\n",
+ "iter \t cost \t stop \t grad \t xreg \t ureg \t step \t feas\n",
+ " 40 1.26573e+00 3.52026e-09 3.67802e-07 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 41 1.26573e+00 2.58066e-09 2.70523e-07 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 42 1.26573e+00 1.89516e-09 1.99225e-07 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 43 1.26573e+00 1.39382e-09 1.46878e-07 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 44 1.26573e+00 1.02641e-09 1.08385e-07 1.00000e-09 1.00000e-09 1.0000 1\n",
+ " 45 1.26573e+00 7.56671e-10 8.00443e-08 1.00000e-09 1.00000e-09 1.0000 1\n"
+ ]
+ },
+ {
+ "data": {
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+ " 2.21115902e-04])],\n",
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+ " 0.46018017, 0.98905117]),\n",
+ " array([-5.01555855, -7.11863201, -5.13583246, 0.47347274, -5.83432046,\n",
+ " 0.48719645, 0.16921048, 0.17903738, 0.14619236, 0.34068169]),\n",
+ " array([-1.81014129, -2.47911897, -2.29206602, 1.8203355 , -2.98200078,\n",
+ " 0.40003903, 0.06784759, 0.07606747, 0.06794457, 0.09823517]),\n",
+ " array([ 0.11711936, -0.35938356, -0.47384594, 2.09523737, -1.4594199 ,\n",
+ " 0.22774331, 0.06770523, 0.06199525, 0.0504948 , 0.03012877]),\n",
+ " array([ 1.30710893, 0.86576737, 0.75227682, 2.31136296, -0.70185548,\n",
+ " 0.11365101, 0.06389094, 0.05220085, 0.03891677, 0.01056189]),\n",
+ " array([ 2.03895858, 1.68096641, 1.60426154, 2.53503256, -0.33489837,\n",
+ " 0.05336425, 0.04969197, 0.03850675, 0.02702695, 0.00418002]),\n",
+ " array([ 2.51484327e+00, 2.27159472e+00, 2.22803779e+00, 2.75271176e+00,\n",
+ " -1.59529705e-01, 2.36580357e-02, 3.25558285e-02, 2.49294003e-02,\n",
+ " 1.67789483e-02, 1.81628741e-03]),\n",
+ " array([ 2.85205435e+00, 2.72460004e+00, 2.70833754e+00, 2.95657219e+00,\n",
+ " -7.65895065e-02, 9.26047902e-03, 1.70949941e-02, 1.38942310e-02,\n",
+ " 9.18632238e-03, 8.81109096e-04]),\n",
+ " array([ 3.10628901e+00, 3.08044883e+00, 3.08649201e+00, 3.13933946e+00,\n",
+ " -3.78231053e-02, 2.30284847e-03, 4.73602411e-03, 5.74320084e-03,\n",
+ " 4.01681500e-03, 5.04632008e-04]),\n",
+ " array([ 3.30030921e+00, 3.35560879e+00, 3.37899729e+00, 3.29372262e+00,\n",
+ " -1.99673011e-02, -9.78743151e-04, -4.41252105e-03, 6.52372659e-05,\n",
+ " 6.73281570e-04, 3.45843215e-04]),\n",
+ " array([ 3.44286515e+00, 3.55602919e+00, 3.59137528e+00, 3.41443206e+00,\n",
+ " -1.18464317e-02, -2.36620225e-03, -1.05369585e-02, -3.58955146e-03,\n",
+ " -1.35469871e-03, 2.63248796e-04]),\n",
+ " array([ 3.53923346e+00, 3.68593872e+00, 3.72753548e+00, 3.50010801e+00,\n",
+ " -8.10061953e-03, -2.73350220e-03, -1.38496532e-02, -5.55479744e-03,\n",
+ " -2.41455568e-03, 2.01267280e-04]),\n",
+ " array([ 3.59638169e+00, 3.75281537e+00, 3.79502924e+00, 3.55432316e+00,\n",
+ " -6.16948792e-03, -2.54176492e-03, -1.45963111e-02, -6.08896092e-03,\n",
+ " -2.73982835e-03, 1.42173280e-04]),\n",
+ " array([ 3.62463136e+00, 3.76885637e+00, 3.80653123e+00, 3.58537452e+00,\n",
+ " -4.84931732e-03, -2.05220340e-03, -1.30584497e-02, -5.40276388e-03,\n",
+ " -2.48406254e-03, 8.31367175e-05]),\n",
+ " array([ 3.63705735e+00, 3.74976681e+00, 3.77846186e+00, 3.60497454e+00,\n",
+ " -3.61759997e-03, -1.41116157e-03, -9.48261424e-03, -3.62488965e-03,\n",
+ " -1.70907553e-03, 2.46370804e-05]),\n",
+ " array([ 3.64592489e+00, 3.71082244e+00, 3.72695008e+00, 3.62443823e+00,\n",
+ " -2.36944256e-03, -6.90309154e-04, -4.09873764e-03, -8.39841467e-04,\n",
+ " -4.23017391e-04, -3.35345754e-05]),\n",
+ " array([ 3.58659503e+00, 3.59345573e+00, 3.59509465e+00, 3.57778944e+00,\n",
+ " -1.35828234e-03, 2.09598015e-05, 1.98145545e-03, 2.13912798e-03,\n",
+ " 8.59804174e-04, -8.04109323e-05]),\n",
+ " array([ 8.76869348e-08, -1.06208173e-07, -4.56167150e-08, 5.66103164e-08,\n",
+ " 1.22935923e-10, 6.21020052e-10, 1.49192224e-10, -7.99966095e-10,\n",
+ " -4.29309670e-11, 1.17057616e-09])],\n",
+ " True)"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# DEFINING THE SHOOTING PROBLEM & SOLVING\n",
+ "\n",
+ "# Defining the time duration for running action models and the terminal one\n",
+ "dt = 5e-2\n",
+ "runningModel.timeStep = dt\n",
+ "\n",
+ "# For this optimal control problem, we define 250 knots (or running action\n",
+ "# models) plus a terminal knot\n",
+ "T = 20\n",
+ "q0 = rmodel.referenceConfigurations[\"centered\"].copy()\n",
+ "v0 = pin.utils.zero(rmodel.nv)\n",
+ "x0 = m2a(np.concatenate([q0, v0]))\n",
+ "rmodel.defaultState = x0.copy()\n",
+ "\n",
+ "problem = ShootingProblem(x0, [runningModel] * T, terminalModel)\n",
+ "\n",
+ "# Creating the DDP solver for this OC problem, defining a logger\n",
+ "fddp = SolverFDDP(problem)\n",
+ "fddp.callback = [CallbackDDPVerbose()]\n",
+ "fddp.callback.append(CallbackDDPLogger())\n",
+ "\n",
+ "us0 = [\n",
+ " m.differential.quasiStatic(d.differential, rmodel.defaultState)\n",
+ " if isinstance(m, IntegratedActionModelEuler) else np.zeros(0)\n",
+ " for m, d in zip(fddp.problem.runningModels, fddp.problem.runningDatas)]\n",
+ "xs0 = [problem.initialState]*len(fddp.models())\n",
+ "\n",
+ "# Solving it with the DDP algorithm\n",
+ "#fddp.solve(init_xs=xs0, init_us=us0)\n",
+ "fddp.solve()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "displayTrajectory(robot, fddp.xs, runningModel.timeStep)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "could not broadcast input array from shape (20) into shape (10)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-27-83867ff4d1ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpltUAM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPlotUAM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfddp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfddp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdistanceRotorCOG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpltUAM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotMotorForces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/plots.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, stateTraj, controlTraj, knots, dt, d, cf, cm)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlotDataType\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPlotDataUAM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mplotMotorForces\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/home/jmarti/robotics/toolboxes/crocoddyl/crocoddyl/plots.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 55\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mValueError\u001b[0m: could not broadcast input array from shape (20) into shape (10)"
+ ]
+ }
+ ],
+ "source": [
+ "pltUAM = PlotUAM(fddp.xs, fddp.us, T, dt, distanceRotorCOG, cf, cm)\n",
+ "\n",
+ "fig, axs = pltUAM.plotMotorForces()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "fig,axs = pltUAM.plotActuation()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "t = np.arange(0., 1, dt)\n",
+ "\n",
+ "fig, axs = plt.subplots(2,2, figsize=(15,10))\n",
+ "fig.suptitle('Motor forces')\n",
+ "axs[0, 0].plot(t,f1)\n",
+ "axs[0, 0].set_title('Motor 1')\n",
+ "axs[0, 1].plot(t,f2)\n",
+ "axs[0, 1].set_title('Motor 2')\n",
+ "axs[1, 0].plot(t,f3)\n",
+ "axs[1, 0].set_title('Motor 3')\n",
+ "axs[1, 1].plot(t,f4)\n",
+ "axs[1, 1].set_title('Motor 4')\n",
+ "\n",
+ "plt.figure()\n",
+ "t = np.append(t, 1)\n",
+ "plt.plot(t,Xx,t,Xy,t,Xz)\n",
+ "plt.legend(['x','y','z'])\n",
+ "plt.title('State - Position')\n",
+ "plt.ylabel('Position, [m]')\n",
+ "plt.xlabel('[s]')\n",
+ "\n",
+ "plt.figure()\n",
+ "plt.plot(t,Vx,t,Vy,t,Vz)\n",
+ "plt.legend(['x','y','z'])\n",
+ "plt.title('State - Velocity')\n",
+ "plt.ylabel('Velocity, [m/s]')\n",
+ "plt.xlabel('[s]')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t = np.arange(0,T*dt,dt)\n",
+ "t_state = np.append(t, t[-1]+dt)\n",
+ "t_state"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/notebooks/kinton_flying_mission.ipynb b/examples/notebooks/kinton_flying_mission.ipynb
index e86064df1cdeeb1a6ee4384f70c5fd94648c6261..733826adb45f3fab52447136e25e7186c8dda164 100644
--- a/examples/notebooks/kinton_flying_mission.ipynb
+++ b/examples/notebooks/kinton_flying_mission.ipynb
@@ -1347,7 +1347,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.16"
+ "version": "2.7.12"
}
},
"nbformat": 4,