{
 "metadata": {
  "name": "",
  "signature": "sha256:b5abd502b4be220e1dfc748c7f68f6170d750933a6f98bd2947fa262921b2270"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Time optimal control problem"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this example we will set-up a simple 4-state system,\n",
      "and find a time-optimal trajectory that avoids some obstacles in the world."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Problem setup"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from casadi import *\n",
      "from pylab import *\n",
      "from optoy import *\n",
      "\n",
      "# Time\n",
      "t = time()\n",
      "\n",
      "# An optimization variable for the end time\n",
      "T = var(lb=0,init=4)\n",
      "\n",
      "# States: position and velocity\n",
      "p = state(2,init=vertcat([3*sin(2*pi/T.init*t),3*cos(2*pi/T.init*t)]))\n",
      "v = state(2)\n",
      "\n",
      "# Control\n",
      "u = control(2)\n",
      "\n",
      "# Disturbance\n",
      "w = dist(2,cov=50*DMatrix([[1,0],[0,1]]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Specify the system dynamics as ODE"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p.dot = v\n",
      "v.dot = -10*(p-u)-v*sqrt(sum_square(v)+1)+w"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Set up the path constraints"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Specify some parameters for circular obstacles\n",
      "#            ( position,      radius)\n",
      "#\n",
      "circles = [  (vertcat([2,2]),      1),\n",
      "             (vertcat([0.5,-2]), 1.5),\n",
      "          ]\n",
      "\n",
      "# List of path constraints\n",
      "h = []\n",
      "for center, radius in circles:\n",
      "  h.append(\n",
      "      Prob(\n",
      "         norm_2(p-center) >= radius  # Nominally, don't hit the obstacles\n",
      "      ) <= 0.99                      # Make sure we wont hit it with probability 0.99\n",
      "  )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Probability 0.99 implies that we need to back-off from the path constraint with 2.3 standard deviations:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import norm\n",
      "gamma = norm.ppf(0.99)\n",
      "print gamma"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.32634787404\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Solve the OCP problem"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ocp(T,h+[p.start[0]==0],regularize=[0.1*u/sqrt(2)],N=30,T=T,verbose=True,periodic=True,integration_intervals=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1, 1)\n",
        "Warning. Slicot plugin not found. You may see degraded performance"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "******************************************************************************\n",
        "This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
        " Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
        "         For more information visit http://projects.coin-or.org/Ipopt\n",
        "******************************************************************************\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "This is Ipopt version 3.11.9, running with linear solver mumps.\n",
        "NOTE: Other linear solvers might be more efficient (see Ipopt documentation).\n",
        "\n",
        "Number of nonzeros in equality constraint Jacobian...:      969\n",
        "Number of nonzeros in inequality constraint Jacobian.:    10860\n",
        "Number of nonzeros in Lagrangian Hessian.............:        0\n",
        "\n",
        "Total number of variables............................:      185\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                     variables with only lower bounds:        1\n",
        "                variables with lower and upper bounds:        0\n",
        "                     variables with only upper bounds:        0\n",
        "Total number of equality constraints.................:      125\n",
        "Total number of inequality constraints...............:       60\n",
        "        inequality constraints with only lower bounds:        0\n",
        "   inequality constraints with lower and upper bounds:        0\n",
        "        inequality constraints with only upper bounds:       60\n",
        "\n",
        "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n",
        "   0  4.0000000e+00 3.28e+00 6.68e-01   0.0 0.00e+00    -  0.00e+00 0.00e+00   0\n",
        "   1  2.1657687e+00 1.84e+00 2.47e+01  -0.1 7.36e+00    -  4.63e-02 3.59e-01f  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   2  2.3648752e+00 1.18e+00 6.63e+01  -0.1 9.94e+00    -  8.54e-01 3.47e-01h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   3  3.2887282e+00 1.30e+00 1.01e+01  -0.8 6.50e+00    -  7.88e-01 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   4  3.3248734e+00 3.16e-02 3.17e-01  -2.5 1.37e+00    -  9.65e-01 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   5  3.1257760e+00 3.00e-01 1.87e-01  -3.4 3.35e+00    -  9.81e-01 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   6  3.0194001e+00 2.61e-01 1.92e-01  -2.6 3.36e+00    -  7.15e-01 9.38e-01h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   7  3.0306765e+00 5.00e-01 2.53e-01  -2.4 1.10e+01    -  4.68e-01 4.82e-01h  2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   8  2.9124163e+00 1.36e-01 2.60e-01  -2.6 3.93e+00    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "   9  2.8618526e+00 3.35e-01 2.75e-02  -2.3 3.34e+00    -  9.86e-01 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  10  2.7931785e+00 7.34e-02 5.62e-02  -2.7 2.33e+00    -  1.00e+00 9.95e-01h  1\n",
        "  11  2.7850355e+00 1.56e-02 3.92e-02  -3.3 1.03e+00    -  1.00e+00 9.98e-01h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  12  2.7750510e+00 1.78e-02 6.74e-02  -3.3 2.62e+00    -  1.00e+00 9.45e-01H  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  13  2.7793391e+00 7.78e-02 6.32e-02  -2.7 1.19e+01    -  8.08e-01 2.50e-01h  3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  14  2.7712775e+00 2.07e-02 3.78e-02  -3.0 1.33e+00    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  15  2.7607517e+00 5.45e-03 2.65e-02  -3.4 5.39e-01    -  1.00e+00 9.77e-01h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  16  2.7601729e+00 1.10e-02 3.60e-02  -3.7 2.69e+00    -  1.00e+00 2.28e-01h  3"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  17  2.7558985e+00 5.93e-03 2.09e-02  -4.1 7.08e-01    -  1.00e+00 9.97e-01h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  18  2.7539639e+00 1.94e-03 2.90e-03  -4.7 4.65e-01    -  1.00e+00 9.97e-01h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  19  2.7538477e+00 1.28e-03 7.15e-03  -5.4 2.48e-01    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  20  2.7538463e+00 1.55e-04 5.83e-03  -6.0 1.05e-01    -  1.00e+00 1.00e+00h  1\n",
        "  21  2.7537947e+00 3.44e-05 1.29e-02  -6.5 2.87e-01    -  9.97e-01 1.00e+00H  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  22  2.7545118e+00 3.74e-05 8.18e-03  -5.1 4.53e-01    -  1.00e+00 1.00e+00H  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  23  2.7537486e+00 1.50e-03 3.18e-03  -6.3 4.24e-01    -  1.00e+00 8.89e-01h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  24  2.7537472e+00 2.63e-05 6.98e-04  -7.0 4.85e-02    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  25  2.7537481e+00 2.91e-06 3.44e-04  -8.4 1.58e-02    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  26  2.7537479e+00 3.46e-07 5.96e-04  -9.1 3.90e-02    -  1.00e+00 1.00e+00H  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  27  2.7537473e+00 6.65e-06 1.02e-04 -10.6 2.00e-02    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  28  2.7537472e+00 7.53e-07 1.01e-04 -11.0 1.08e-02    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  29  2.7537472e+00 1.23e-07 1.92e-05 -11.0 3.27e-03    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "iter    objective    inf_pr   inf_du lg(mu)  ||d||  lg(rg) alpha_du alpha_pr  ls\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  30  2.7537472e+00 1.61e-08 3.08e-07 -11.0 1.04e-03    -  1.00e+00 1.00e+00h  1\n",
        "  31  2.7537472e+00 2.38e-09 1.39e-07 -11.0 4.14e-04    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  32  2.7537472e+00 3.18e-10 3.83e-08 -11.0 1.54e-04    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "  33  2.7537472e+00 7.64e-12 8.19e-09 -11.0 3.27e-05    -  1.00e+00 1.00e+00h  1"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Number of Iterations....: 33\n",
        "\n",
        "                                   (scaled)                 (unscaled)\n",
        "Objective...............:   2.7537471526254755e+00    2.7537471526254755e+00\n",
        "Dual infeasibility......:   8.1866526092932990e-09    8.1866526092932990e-09\n",
        "Constraint violation....:   7.6401107662604772e-12    7.6401107662604772e-12\n",
        "Complementarity.........:   9.9999999999999994e-12    9.9999999999999994e-12\n",
        "Overall NLP error.......:   8.1866526092932990e-09    8.1866526092932990e-09\n",
        "\n",
        "\n",
        "Number of objective function evaluations             = 53\n",
        "Number of objective gradient evaluations             = 34\n",
        "Number of equality constraint evaluations            = 53\n",
        "Number of inequality constraint evaluations          = 53\n",
        "Number of equality constraint Jacobian evaluations   = 34\n",
        "Number of inequality constraint Jacobian evaluations = 34\n",
        "Number of Lagrangian Hessian evaluations             = 0\n",
        "Total CPU secs in IPOPT (w/o function evaluations)   =      0.536\n",
        "Total CPU secs in NLP function evaluations           =     12.804\n",
        "\n",
        "EXIT: Optimal Solution Found.\n",
        "                   proc           wall      num           mean             mean\n",
        "                   time           time     evals       proc time        wall time\n",
        "       eval_f     0.395 [s]      0.395 [s]    53       7.45 [ms]        7.44 [ms]\n",
        "  eval_grad_f     0.267 [s]      0.267 [s]    35       7.64 [ms]        7.63 [ms]\n",
        "       eval_g     0.393 [s]      0.393 [s]    53       7.42 [ms]        7.42 [ms]\n",
        "   eval_jac_g    12.081 [s]     12.074 [s]    36     335.58 [ms]      335.39 [ms]\n",
        "    main loop    13.349 [s]     13.339 [s]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "2.7537471526254755"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting of results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(12,12))\n",
      "# Plot the nominal trajectory\n",
      "plot(value(p[0]),value(p[1]),'o-')\n",
      "xlabel('x position')\n",
      "ylabel('y position')\n",
      "title('time-optimal trajectory')\n",
      "axis('equal')\n",
      "\n",
      "# Plot the obstacles\n",
      "theta = linspace(0,2*pi,1000)\n",
      "for center, radius in circles:\n",
      "  fill(radius*cos(theta) + center[0],radius*sin(theta) + center[1],'r')\n",
      "    \n",
      "# Plot covariances\n",
      "circle = array([[sin(x),cos(x)] for x in linspace(0,2*pi,100)]).T\n",
      "\n",
      "for pcov, pn in zip(cov(p),value(p)):\n",
      "  w,v = numpy.linalg.eig(pcov)\n",
      "  W   = mul(v,diag(sqrt(w)))\n",
      "  e = gamma*mul(W,circle)\n",
      "  plot(e[0,:].T + pn[0],e[1,:].T + pn[1],'r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAssAAALYCAYAAACKSr/uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10d89e5850>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}