week2_motionplanning.ipynb

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  "metadata": {
    "colab": {
      "name": "assignment1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/patilunmesh/motion_planning_coursera/blob/master/week2_motionplanning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEEtOMIfNbqC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as anim\n",
        "from IPython.display import HTML"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZYuoJJGNuYT",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "# Calculates the inverse measurement model for a laser scanner.\n",
        "# It identifies three regions. The first where no information is available occurs\n",
        "# outside of the scanning arc. The second where objects are likely to exist, at the\n",
        "# end of the range measurement within the arc. The third are where objects are unlikely\n",
        "# to exist, within the arc but with less distance than the range measurement.\n",
        "def inverse_scanner(num_rows, num_cols, x, y, theta, meas_phi, meas_r, rmax, alpha, beta):\n",
        "    m = np.zeros((M, N))\n",
        "    for i in range(num_rows):\n",
        "        for j in range(num_cols):\n",
        "            # Find range and bearing relative to the input state (x, y, theta).\n",
        "            r = math.sqrt((i - x)**2 + (j - y)**2)\n",
        "            phi = (math.atan2(j - y, i - x) - theta + math.pi) % (2 * math.pi) - math.pi\n",
        "            \n",
        "            # Find the range measurement associated with the relative bearing.\n",
        "            k = np.argmin(np.abs(np.subtract(phi, meas_phi)))\n",
        "            \n",
        "            # If the range is greater than the maximum sensor range, or behind our range\n",
        "            # measurement, or is outside of the field of view of the sensor, then no\n",
        "            # new information is available.\n",
        "            if (r > min(rmax, meas_r[k] + alpha / 2.0)) or (abs(phi - meas_phi[k]) > beta / 2.0):\n",
        "                m[i, j] = 0.5\n",
        "            \n",
        "            # If the range measurement lied within this cell, it is likely to be an object.\n",
        "            elif (meas_r[k] < rmax) and (abs(r - meas_r[k]) < alpha / 2.0):\n",
        "                m[i, j] = 0.7\n",
        "            \n",
        "            # If the cell is in front of the range measurement, it is likely to be empty.\n",
        "            elif r < meas_r[k]:\n",
        "                m[i, j] = 0.3\n",
        "    return m\n",
        "               "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2mXD1drMOKL-",
        "colab_type": "code",
        "colab": {}
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      "source": [
        "# Generates range measurements for a laser scanner based on a map, vehicle position,\n",
        "# and sensor parameters.\n",
        "# Uses the ray tracing algorithm.\n",
        "def get_ranges(true_map, X, meas_phi, rmax):\n",
        "    (M, N) = np.shape(true_map)\n",
        "    x = X[0]\n",
        "    y = X[1]\n",
        "    theta = X[2]\n",
        "    meas_r = rmax * np.ones(meas_phi.shape)\n",
        "    # np ones returns same shape of input with all values 1\n",
        "    \n",
        "    # Iterate for each measurement bearing.\n",
        "    for i in range(len(meas_phi)):\n",
        "        # Iterate over each unit step up to and including rmax.\n",
        "        for r in range(1, rmax+1):\n",
        "            # Determine the coordinates of the cell.\n",
        "            xi = int(round(x + r * math.cos(theta + meas_phi[i])))\n",
        "            yi = int(round(y + r * math.sin(theta + meas_phi[i])))\n",
        "            \n",
        "            # If not in the map, set measurement there and stop going further.\n",
        "            if (xi <= 0 or xi >= M-1 or yi <= 0 or yi >= N-1):\n",
        "                meas_r[i] = r\n",
        "                break\n",
        "            # If in the map, but hitting an obstacle, set the measurement range\n",
        "            # and stop ray tracing.\n",
        "            elif true_map[int(round(xi)), int(round(yi))] == 1:\n",
        "                meas_r[i] = r\n",
        "                break\n",
        "                \n",
        "    return meas_r"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29slp8-jORmy",
        "colab_type": "code",
        "outputId": "9d23c21e-030a-467d-c876-4e247258324b",
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          "base_uri": "https://localhost:8080/",
          "height": 808
        }
      },
      "source": [
        "# Simulation time initialization.\n",
        "T_MAX = 150\n",
        "time_steps = np.arange(T_MAX)\n",
        "\n",
        "# Initializing the robot's location.\n",
        "x_0 = [30, 30, 0]\n",
        "\n",
        "# The sequence of robot motions.\n",
        "u = np.array([[3, 0, -3, 0], [0, 3, 0, -3]])\n",
        "u_i = 1\n",
        "\n",
        "# Robot sensor rotation command\n",
        "w = np.multiply(0.3, np.ones(len(time_steps)))\n",
        "\n",
        "# True map (note, columns of map correspond to y axis and rows to x axis, so \n",
        "# robot position x = x(1) and y = x(2) are reversed when plotted to match\n",
        "M = 50\n",
        "N = 60\n",
        "true_map = np.zeros((M, N))\n",
        "true_map[0:10, 0:10] = 1\n",
        "true_map[30:35, 40:45] = 1\n",
        "true_map[3:6,40:60] = 1;\n",
        "true_map[20:30,25:29] = 1;\n",
        "true_map[40:50,5:25] = 1;\n",
        "\n",
        "# Initialize the belief map.\n",
        "# We are assuming a uniform prior.\n",
        "m = np.multiply(0.5, np.ones((M, N)))\n",
        "\n",
        "# Initialize the log odds ratio.\n",
        "L0 = np.log(np.divide(m, np.subtract(1, m)))\n",
        "L = L0\n",
        "\n",
        "# Parameters for the sensor model.\n",
        "meas_phi = np.arange(-0.4, 0.4, 0.05)\n",
        "rmax = 30 # Max beam range.\n",
        "alpha = 1 # Width of an obstacle (distance about measurement to fill in).\n",
        "beta = 0.05 # Angular width of a beam.\n",
        "\n",
        "# Initialize the vector of states for our simulation.\n",
        "x = np.zeros((3, len(time_steps)))\n",
        "x[:, 0] = x_0\n",
        "print(\"init successful\")\n",
        "\n",
        "\n",
        "\n",
        "# Intitialize figures.\n",
        "map_fig = plt.figure()\n",
        "map_ax = map_fig.add_subplot(111)\n",
        "map_ax.set_xlim(0, N)\n",
        "map_ax.set_ylim(0, M)\n",
        "\n",
        "invmod_fig = plt.figure()\n",
        "invmod_ax = invmod_fig.add_subplot(111)\n",
        "invmod_ax.set_xlim(0, N)\n",
        "invmod_ax.set_ylim(0, M)\n",
        "\n",
        "belief_fig = plt.figure()\n",
        "belief_ax = belief_fig.add_subplot(111)\n",
        "belief_ax.set_xlim(0, N)\n",
        "belief_ax.set_ylim(0, M)\n",
        "\n",
        "meas_rs = []\n",
        "meas_r = get_ranges(true_map, x[:, 0], meas_phi, rmax)\n",
        "meas_rs.append(meas_r)\n",
        "invmods = []\n",
        "invmod = inverse_scanner(M, N, x[0, 0], x[1, 0], x[2, 0], meas_phi, meas_r, rmax, alpha, beta)\n",
        "invmods.append(invmod)\n",
        "ms = []\n",
        "ms.append(m)\n",
        "\n",
        "# Main simulation loop.\n",
        "for t in range(1, len(time_steps)):\n",
        "    # Perform robot motion.\n",
        "    move = np.add(x[0:2, t-1], u[:, u_i]) \n",
        "    # If we hit the map boundaries, or a collision would occur, remain still.\n",
        "    if (move[0] >= M - 1) or (move[1] >= N - 1) or (move[0] <= 0) or (move[1] <= 0) \\\n",
        "        or true_map[int(round(move[0])), int(round(move[1]))] == 1:\n",
        "        x[:, t] = x[:, t-1]\n",
        "        u_i = (u_i + 1) % 4\n",
        "    else:\n",
        "        x[0:2, t] = move\n",
        "    x[2, t] = (x[2, t-1] + w[t]) % (2 * math.pi)\n",
        "    \n",
        "    # TODO Gather the measurement range data, which we will convert to occupancy probabilities\n",
        "    # using our inverse measurement model.\n",
        "    # meas_r = ...\n",
        "    #added by me\n",
        "    meas_r = get_ranges(true_map, x[:,t], meas_phi, rmax)\n",
        "    meas_rs.append(meas_r)\n",
        "    \n",
        "    # TODO Given our range measurements and our robot location, apply our inverse scanner model\n",
        "    # to get our measure probabilities of occupancy.\n",
        "    # invmod = ...\n",
        "    #added by me\n",
        "    invmod = inverse_scanner(M, N, x[0, t], x[1, t], x[2, t], meas_phi, meas_r, rmax, alpha, beta)\n",
        "    invmods.append(invmod)\n",
        "    \n",
        "    # TODO Calculate and update the log odds of our occupancy grid, given our measured\n",
        "    # occupancy probabilities from the inverse model.\n",
        "    # print(invmod)\n",
        "     \n",
        "    Lt = np.log(invmod/(1-invmod))\n",
        "    L =  Lt - L0 + L\n",
        "    # TODO Calculate a grid of probabilities from the log odds.\n",
        "    Al = np.exp(L)\n",
        "    m = (Al / (1 + Al))\n",
        "    ms.append(m)\n",
        "    \n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "init successful\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:101: RuntimeWarning: divide by zero encountered in log\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUkPGXZIaMu6S1JBxl6SG/gfVkq6NLS9zhgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi40LCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcv7US4rQAADN5JREFUeJzt3EGMnPV5x/Hvr5iU1ElrSLaWhZGg\nAgVxKCZdURCoaqBElEaBA0KJosoHS75QiaiRUmilSpF6CJeQHKqqVqDxIU2gJBTEIanrEFWVKpN1\ngARwKA4BBQTs0mIl7YHW5OlhXldby3Rndma83ofvR1rNvO+8wzx/MXx3eHdmUlVIknr5pY0eQJI0\ne8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqaEt4xyU5EXg58DbwPGqWkxyHnA/cCHwInBbVb05nzElSZOY\n5JX7R6pqV1UtDtt3Ager6hLg4LAtSToDTHNa5mZg/3B9P3DL9ONIkmYh43xCNclPgDeBAv66qvYl\nOVZV24bbA7x5Yvuk++4F9gJs3br1ty699NJZzi9J7R0+fPiNqlqY5D5jnXMHrq2qV5L8OnAgyY9W\n31hVleSUvyWqah+wD2BxcbGWlpYmmU+S3vWSvDTpfcY6LVNVrwyXy8BDwJXA60l2DA+8A1ie9MEl\nSfOxZtyTbE3y/hPXgY8CTwOPALuHw3YDD89rSEnSZMY5LbMdeGh0Wp0twN9W1beSfA94IMke4CXg\ntvmNKUmaxJpxr6oXgMtPsf/fgOvnMZQkaTp+QlWSGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lq\nyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1\nZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIa\nMu6S1JBxl6SGxo57krOSPJHk0WH7oiSHkhxNcn+S98xvTEnSJCZ55X4HcGTV9t3APVV1MfAmsGeW\ng0mS1m+suCfZCfwB8OVhO8B1wIPDIfuBW+YxoCRpcuO+cv8i8FngF8P2B4BjVXV82H4ZOP9Ud0yy\nN8lSkqWVlZWphpUkjWfNuCf5GLBcVYfX8wBVta+qFqtqcWFhYT3/CEnShLaMccw1wMeT3AScA/wq\n8CVgW5Itw6v3ncAr8xtTkjSJNV+5V9VdVbWzqi4EPgF8p6o+BTwG3Doctht4eG5TSpImMs373P8E\n+OMkRxmdg793NiNJkqY1zmmZ/1VV3wW+O1x/Abhy9iNJkqblJ1QlqSHjLkkNGXdJasi4S1JDxl2S\nGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5J\nDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZek\nhoy7JDVk3CWpIeMuSQ2tGfck5yR5PMlTSZ5J8rlh/0VJDiU5muT+JO+Z/7iSpHGM88r9LeC6qroc\n2AXcmOQq4G7gnqq6GHgT2DO/MSVJk1gz7jXyH8Pm2cNPAdcBDw779wO3zGVCSdLExjrnnuSsJE8C\ny8AB4MfAsao6PhzyMnD+O9x3b5KlJEsrKyuzmFmStIax4l5Vb1fVLmAncCVw6bgPUFX7qmqxqhYX\nFhbWOaYkaRITvVumqo4BjwFXA9uSbBlu2gm8MuPZJEnrNM67ZRaSbBuuvxe4ATjCKPK3DoftBh6e\n15CSpMlsWfsQdgD7k5zF6JfBA1X1aJJnga8n+QvgCeDeOc4pSZrAmnGvqh8AV5xi/wuMzr9Lks4w\nfkJVkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWp\nIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLU\nkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhtaMe5ILkjyW5NkkzyS5Y9h/\nXpIDSZ4fLs+d/7iSpHGM88r9OPCZqroMuAq4PcllwJ3Awaq6BDg4bEuSzgBrxr2qXq2q7w/Xfw4c\nAc4Hbgb2D4ftB26Z15CSpMlMdM49yYXAFcAhYHtVvTrc9Bqw/R3uszfJUpKllZWVKUaVJI1r7Lgn\neR/wDeDTVfWz1bdVVQF1qvtV1b6qWqyqxYWFhamGlSSNZ6y4JzmbUdi/WlXfHHa/nmTHcPsOYHk+\nI0qSJjXOu2UC3AscqaovrLrpEWD3cH038PDsx5MkrceWMY65BvhD4IdJnhz2/SnweeCBJHuAl4Db\n5jOiJGlSa8a9qv4ZyDvcfP1sx5EkzYKfUJWkhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S\n1JBxl6SGjLskNWTcJakh4y5JDRl3SWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJ\nasi4S1JDxl2SGjLuktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLsk\nNbRm3JPcl2Q5ydOr9p2X5ECS54fLc+c7piRpEuO8cv8KcONJ++4EDlbVJcDBYVuSdIZYM+5V9U/A\nv5+0+2Zg/3B9P3DLjOeSJE1hvefct1fVq8P114Dt73Rgkr1JlpIsraysrPPhJEmTmPoPqlVVQP0/\nt++rqsWqWlxYWJj24SRJY1hv3F9PsgNguFye3UiSpGmtN+6PALuH67uBh2czjiRpFsZ5K+TXgH8B\nPpTk5SR7gM8DNyR5Hvi9YVuSdIbYstYBVfXJd7jp+hnPIkmaET+hKkkNGXdJasi4S1JDxl2SGjLu\nktSQcZekhoy7JDVk3CWpIeMuSQ0Zd0lqyLhLUkPGXZIaMu6S1JBxl6SGjLskNWTcJakh4y5JDRl3\nSWrIuEtSQ8Zdkhoy7pLUkHGXpIaMuyQ1ZNwlqSHjLkkNGXdJasi4S1JDxl2SGjLuktSQcZekhoy7\nJDVk3CWpIeMuSQ0Zd0lqyLhLUkNTxT3JjUmeS3I0yZ2zGkqSNJ11xz3JWcBfAr8PXAZ8MsllsxpM\nkrR+07xyvxI4WlUvVNV/AV8Hbp7NWJKkaWyZ4r7nAz9dtf0y8NsnH5RkL7B32HwrydNTPOaZ7oPA\nGxs9xJx0Xhu4vs2u+/o+NOkdpon7WKpqH7APIMlSVS3O+zE3Suf1dV4buL7N7t2wvknvM81pmVeA\nC1Zt7xz2SZI22DRx/x5wSZKLkrwH+ATwyGzGkiRNY92nZarqeJI/Ar4NnAXcV1XPrHG3fet9vE2i\n8/o6rw1c32bn+k6SqprHIJKkDeQnVCWpIeMuSQ2dlrh3+5qCJPclWV79nv0k5yU5kOT54fLcjZxx\nGkkuSPJYkmeTPJPkjmF/izUmOSfJ40meGtb3uWH/RUkODc/T+4c3CmxKSc5K8kSSR4ftTmt7MckP\nkzx54i2CXZ6bAEm2JXkwyY+SHEly9XrWN/e4N/2agq8AN560707gYFVdAhwctjer48Bnquoy4Crg\n9uHfWZc1vgVcV1WXA7uAG5NcBdwN3FNVFwNvAns2cMZp3QEcWbXdaW0AH6mqXave297luQnwJeBb\nVXUpcDmjf4+Tr6+q5voDXA18e9X2XcBd837c07CuC4GnV20/B+wYru8AntvoGWe41oeBGzquEfgV\n4PuMPl39BrBl2P9/nreb6YfRZ04OAtcBjwLpsrZh/heBD560r8VzE/g14CcMb3aZZn2n47TMqb6m\n4PzT8Lin2/aqenW4/hqwfSOHmZUkFwJXAIdotMbhtMWTwDJwAPgxcKyqjg+HbObn6ReBzwK/GLY/\nQJ+1ARTwD0kOD19vAn2emxcBK8DfDKfVvpxkK+tYn39QnYMa/Xrd9O8xTfI+4BvAp6vqZ6tv2+xr\nrKq3q2oXo1e5VwKXbvBIM5HkY8ByVR3e6Fnm6Nqq+jCjU723J/md1Tdu8ufmFuDDwF9V1RXAf3LS\nKZhx13c64v5u+ZqC15PsABgulzd4nqkkOZtR2L9aVd8cdrdaI0BVHQMeY3SqYluSEx/s26zP02uA\njyd5kdE3tV7H6Bxuh7UBUFWvDJfLwEOMfjl3eW6+DLxcVYeG7QcZxX7i9Z2OuL9bvqbgEWD3cH03\no/PUm1KSAPcCR6rqC6tuarHGJAtJtg3X38vo7wlHGEX+1uGwTbm+qrqrqnZW1YWM/lv7TlV9igZr\nA0iyNcn7T1wHPgo8TZPnZlW9Bvw0yYlvgbweeJb1rO80/ZHgJuBfGZ3X/LON/qPFDNbzNeBV4L8Z\n/abdw+i85kHgeeAfgfM2es4p1ncto//t+wHw5PBzU5c1Ar8JPDGs72ngz4f9vwE8DhwF/g745Y2e\ndcp1/i7waKe1Det4avh55kRPujw3h7XsApaG5+ffA+euZ31+/YAkNeQfVCWpIeMuSQ0Zd0lqyLhL\nUkPGXZIaMu6S1JBxl6SG/gfVkq6NLS9zhgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmadWTS25MRx",
        "colab_type": "code",
        "outputId": "6c62ae8f-c2e4-408d-b600-f22632888864",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "m_f = ms[-1]\n",
        "\n",
        "print(\"{}\".format(m_f[40, 10]))\n",
        "print(\"{}\".format(m_f[30, 40]))\n",
        "print(\"{}\".format(m_f[35, 40]))\n",
        "print(\"{}\".format(m_f[0, 50]))\n",
        "print(\"{}\".format(m_f[10, 5]))\n",
        "print(\"{}\".format(m_f[20, 15]))\n",
        "print(\"{}\".format(m_f[25, 50]))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.985747800587\n",
            "0.998863179956\n",
            "0.844827586207\n",
            "0.5\n",
            "0.072972972973\n",
            "0.000208997634687\n",
            "1.2953015128e-06\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIEmCRyrOTxn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "cc4a85d7-2320-40ee-db3a-647731baba91"
      },
      "source": [
        "def map_update(i):\n",
        "    map_ax.clear()\n",
        "    map_ax.set_xlim(0, N)\n",
        "    map_ax.set_ylim(0, M)\n",
        "    map_ax.imshow(np.subtract(1, true_map), cmap='gray', origin='lower', vmin=0.0, vmax=1.0)\n",
        "    x_plot = x[1, :i+1]\n",
        "    y_plot = x[0, :i+1]\n",
        "    map_ax.plot(x_plot, y_plot, \"bx-\")\n",
        "\n",
        "def invmod_update(i):\n",
        "    invmod_ax.clear()\n",
        "    invmod_ax.set_xlim(0, N)\n",
        "    invmod_ax.set_ylim(0, M)\n",
        "    invmod_ax.imshow(invmods[i], cmap='gray', origin='lower', vmin=0.0, vmax=1.0)\n",
        "    for j in range(len(meas_rs[i])):\n",
        "        invmod_ax.plot(x[1, i] + meas_rs[i][j] * math.sin(meas_phi[j] + x[2, i]), \\\n",
        "                       x[0, i] + meas_rs[i][j] * math.cos(meas_phi[j] + x[2, i]), \"ko\")\n",
        "    invmod_ax.plot(x[1, i], x[0, i], 'bx')\n",
        "    \n",
        "def belief_update(i):\n",
        "    belief_ax.clear()\n",
        "    belief_ax.set_xlim(0, N)\n",
        "    belief_ax.set_ylim(0, M)\n",
        "    belief_ax.imshow(ms[i], cmap='gray', origin='lower', vmin=0.0, vmax=1.0)\n",
        "    belief_ax.plot(x[1, max(0, i-10):i], x[0, max(0, i-10):i], 'bx-')\n",
        "    \n",
        "map_anim = anim.FuncAnimation(map_fig, map_update, frames=len(x[0, :]), repeat=False)\n",
        "invmod_anim = anim.FuncAnimation(invmod_fig, invmod_update, frames=len(x[0, :]), repeat=False)\n",
        "belief_anim = anim.FuncAnimation(belief_fig, belief_update, frames=len(x[0, :]), repeat=False)\n",
        "\n",
        "\n",
        "HTML(map_anim.to_html5_video())\n",
        "\n",
        "HTML(invmod_anim.to_html5_video())\n",
        "\n",
        "HTML(belief_anim.to_html5_video())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    }
  ]
}