cartpole_obs_vis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating obs...\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n",
      "1\n",
      "generated.\n",
      "(1, 6, 2)\n"
     ]
    }
   ],
   "source": [
    "from ctypes import *\n",
    "#ctypes.cdll.LoadLibrary('')\n",
    "lib1 = CDLL(\"deps/sparse_rrt/deps/trajopt/build/lib/libsco.so\")\n",
    "lib2 = CDLL(\"deps/sparse_rrt/deps/trajopt/build/lib/libutils.so\")\n",
    "\n",
    "import sys\n",
    "sys.path.append('deps/sparse_rrt')\n",
    "sys.path.append('.')\n",
    "\n",
    "from sparse_rrt.planners import SST\n",
    "#from env.cartpole_obs import CartPoleObs\n",
    "from env.cartpole import CartPole\n",
    "from sparse_rrt.systems import standard_cpp_systems\n",
    "from sparse_rrt import _sst_module\n",
    "import numpy as np\n",
    "import time\n",
    "from tools.pcd_generation import rectangle_pcd\n",
    "\n",
    "obs_list = []\n",
    "width = 4.\n",
    "near = width * 1.2\n",
    "H = 0.5\n",
    "L = 2.5\n",
    "print('generating obs...')\n",
    "for i in range(1):\n",
    "    obs_single = []\n",
    "    for j in range(6):\n",
    "        low_h = - width/2 - L\n",
    "        high_h = width/2 + L\n",
    "        '''\n",
    "        make sure the obstacle does not block the pole entirely\n",
    "        by making sure the fixed point of the pole is not in the obs\n",
    "        hence the valid range for y axis is:\n",
    "        H - low_h ~ H - width/2, H + width/2 ~ H + high_h\n",
    "        '''\n",
    "        while True:\n",
    "            # first randomly see if it is left or right\n",
    "            side = np.random.randint(low=0, high=2)\n",
    "            print(side)\n",
    "            # 0: left, 1: right\n",
    "            if side == 0:\n",
    "                obs = np.random.uniform(low=[-20, H+low_h], high=[20, H-width/2])\n",
    "            else:\n",
    "                obs = np.random.uniform(low=[-20, H+width/2], high=[20, H+high_h])\n",
    "            # see if it is cluttered enough by making sure obstacles can't be\n",
    "            # too close\n",
    "            too_near = False\n",
    "            for k in range(len(obs_single)):\n",
    "                if np.linalg.norm(obs-obs_single[k]) < near:\n",
    "                    too_near = True\n",
    "                    break\n",
    "            if not too_near:\n",
    "                break\n",
    "            \n",
    "        obs_single.append(obs)\n",
    "    obs_single = np.array(obs_single)\n",
    "    obs_list.append(obs_single)\n",
    "obs_list = np.array(obs_list)\n",
    "# convert from obs to point cloud\n",
    "obc_list = rectangle_pcd(obs_list, 4., 1400)\n",
    "print('generated.')\n",
    "print(obs_list.shape)\n",
    "\n",
    "# Create custom system\n",
    "#obs_list = [[-10., -3.],\n",
    "#            [0., 3.],\n",
    "#            [10, -3.]]\n",
    "obs_list = obs_list[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution: (array([[  1.15256648,   0.        ,  -2.82844024,   0.        ],\n",
      "       [  1.29516781,   2.29415316,  -2.78011944,   0.77871262],\n",
      "       [  3.62116176,  10.47997388,  -2.16298433,   2.        ],\n",
      "       [  8.30929094,  13.39273785,  -1.38298433,   2.        ],\n",
      "       [  9.62285562,  13.98449221,  -1.19098433,   2.        ],\n",
      "       [ 12.43505188,   9.95884171,  -0.72298433,   2.        ],\n",
      "       [ 14.31034655,   1.16768001,  -0.05098433,   2.        ],\n",
      "       [ 13.07195654,  -7.04634419,   0.72901567,   2.        ],\n",
      "       [  9.74926219, -10.64117135,   1.42283587,   1.51845443],\n",
      "       [  7.50832913, -10.76791623,   1.67698751,   0.89443757],\n",
      "       [  6.80775868,  -9.81243269,   1.73238718,   0.73298028],\n",
      "       [  4.87891643,  -8.05061162,   1.83521788,   0.22452187],\n",
      "       [  3.90295513,  -5.46942461,   1.85326644,   0.02595997],\n",
      "       [  3.3346294 ,  -4.66387962,   1.84232135,  -0.22641361],\n",
      "       [  2.92765628,  -3.28706191,   1.81001151,  -0.41319638],\n",
      "       [  2.63863668,  -1.19071814,   1.74245822,  -0.65358469]]), array([[ 200.83164657],\n",
      "       [ 298.14551342],\n",
      "       [ 150.39408985],\n",
      "       [ 148.40657324],\n",
      "       [-178.57446603],\n",
      "       [-279.54751698],\n",
      "       [-273.34856705],\n",
      "       [-185.41799214],\n",
      "       [ -28.10938342],\n",
      "       [ 206.46857815],\n",
      "       [ 125.74944545],\n",
      "       [ 273.3349906 ],\n",
      "       [ 115.47564195],\n",
      "       [ 207.04357504],\n",
      "       [ 247.0321448 ]]), array([0.126, 0.354, 0.39 , 0.096, 0.234, 0.336, 0.39 , 0.376, 0.21 ,\n",
      "       0.068, 0.216, 0.144, 0.112, 0.102, 0.128])), Number of nodes: 4007\n",
      "time spent: 7.142935\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#obs_list = np.array(obs_list)\n",
    "system = standard_cpp_systems.RectangleObs(obs_list, 4., 'cartpole')\n",
    "#system = CartPoleObs(obs_list)\n",
    "# Create SST planner\n",
    "min_time_steps = 10\n",
    "max_time_steps = 200\n",
    "integration_step = 0.002\n",
    "max_iter = 100000\n",
    "goal_radius=1.5\n",
    "random_seed=0\n",
    "sst_delta_near=2.0\n",
    "sst_delta_drain=1.2\n",
    "\n",
    "low = []\n",
    "high = []\n",
    "state_bounds = system.get_state_bounds()\n",
    "for i in range(len(state_bounds)):\n",
    "    low.append(state_bounds[i][0])\n",
    "    high.append(state_bounds[i][1])\n",
    "    \n",
    "start = np.random.uniform(low=low, high=high)\n",
    "end = np.random.uniform(low=low, high=high)\n",
    "\n",
    "\n",
    "start[1] = 0.\n",
    "start[3] = 0.\n",
    "end[1] = 0.\n",
    "end[3] = 0.\n",
    "planner = SST(\n",
    "    state_bounds=system.get_state_bounds(),\n",
    "    control_bounds=system.get_control_bounds(),\n",
    "    distance=system.distance_computer(),\n",
    "    start_state=start,\n",
    "    goal_state=end,\n",
    "    goal_radius=goal_radius,\n",
    "    random_seed=0,\n",
    "    sst_delta_near=sst_delta_near,\n",
    "    sst_delta_drain=sst_delta_drain\n",
    ")\n",
    "\n",
    "    \n",
    "# Run planning and print out solution is some statistics every few iterations.\n",
    "time0 = time.time()\n",
    "for iteration in range(max_iter):\n",
    "    #if iteration % 50 == 0:\n",
    "    #    # from time to time use the goal\n",
    "    #    sample = end\n",
    "    #    planner.step_with_sample(system, sample, 20, 200, 0.002)\n",
    "    #else:\n",
    "    planner.step(system, min_time_steps, max_time_steps, integration_step)\n",
    "    #    #sample = np.random.uniform(low=low, high=high)\n",
    "    #print('iteration: %d' % (iteration))\n",
    "    # interation: 0.002\n",
    "    #planner.step_with_sample(system, sample, 2, 20, 0.01)\n",
    "    \n",
    "    #if iteration % 100 == 0:\n",
    "solution = planner.get_solution()\n",
    "print(\"Solution: %s, Number of nodes: %s\" % (planner.get_solution(), planner.get_number_of_nodes()))\n",
    "\n",
    "print('time spent: %f' % (time.time() - time0))\n",
    "assert solution is not None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16, 4)\n",
      "(15, 1)\n"
     ]
    }
   ],
   "source": [
    "states, actions, costs = solution\n",
    "print(states.shape)\n",
    "print(actions.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# visualize the path\n",
    "\"\"\"\n",
    "Given a list of states, render the environment\n",
    "\"\"\"\n",
    "from numpy import sin, cos\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.integrate as integrate\n",
    "import matplotlib.animation as animation\n",
    "import matplotlib as mpl\n",
    "import matplotlib.patches as patches\n",
    "from IPython.display import HTML\n",
    "from visual.visualizer import Visualizer\n",
    "\n",
    "\n",
    "\n",
    "class CartPoleVisualizer(Visualizer):\n",
    "    def __init__(self, system, params):\n",
    "        super(CartPoleVisualizer, self).__init__(system, params)\n",
    "        self.dt = 0.05\n",
    "\n",
    "    def _init(self):\n",
    "        # clear the current ax\n",
    "        ax = plt.gca()\n",
    "        ax.clear()\n",
    "        # add patches\n",
    "        state = self.states[0]\n",
    "        self.pole = patches.Rectangle((state[0]-self.params['pole_w']/2,self.params['cart_h']),\\\n",
    "                                       self.params['pole_w'],self.params['pole_l'],\\\n",
    "                                      linewidth=.5,edgecolor='red',facecolor='red')\n",
    "        self.cart = patches.Rectangle((state[0]-self.params['cart_w']/2,0),\\\n",
    "                                       self.params['cart_w'],self.params['cart_h'],\\\n",
    "                                      linewidth=.5,edgecolor='blue',facecolor='blue')\n",
    "        self.recs = []\n",
    "        self.recs.append(self.pole)\n",
    "        self.recs.append(self.cart)\n",
    "        for i in range(len(self.obs)):\n",
    "            x, y = self.obs[i]\n",
    "            obs = patches.Rectangle((x-self.params['obs_w']/2,y-params['obs_h']/2),\\\n",
    "                                       self.params['obs_w'],self.params['obs_h'],\\\n",
    "                                      linewidth=.5,edgecolor='black',facecolor='black')\n",
    "            self.recs.append(obs)\n",
    "            ax.add_patch(obs)\n",
    "        # transform pole according to state\n",
    "        t = mpl.transforms.Affine2D().rotate_deg_around(state[0], self.params['cart_h'], \\\n",
    "                                                        -state[2]/np.pi * 180) + ax.transData\n",
    "        self.pole.set_transform(t)\n",
    "        ax.add_patch(self.pole)\n",
    "        ax.add_patch(self.cart)\n",
    "        return self.recs\n",
    "    def _animate(self, i):\n",
    "        ax = plt.gca()\n",
    "        ax.set_xlim(-40, 40)\n",
    "        ax.set_ylim(-20, 20)\n",
    "        state = self.states[i]\n",
    "        self.recs[0].set_xy((state[0]-self.params['pole_w']/2,self.params['cart_h']))\n",
    "        t = mpl.transforms.Affine2D().rotate_deg_around(state[0], self.params['cart_h'], \\\n",
    "                                                        -state[2]/np.pi * 180) + ax.transData\n",
    "        self.recs[0].set_transform(t)\n",
    "        self.recs[1].set_xy((state[0]-self.params['cart_w']/2,params['cart_h']))\n",
    "        # print location of cart\n",
    "        return self.recs\n",
    "\n",
    "\n",
    " \n",
    "    def animate(self, states, actions, obstacles):\n",
    "        '''\n",
    "        given a list of states, actions and obstacles, animate the robot\n",
    "        '''\n",
    "        # transform the waypoint states and actions into trajectory\n",
    "        traj = []\n",
    "        for i in range(len(states)-1):\n",
    "            print('state: %d, remaining: %d' % (i, len(states)-i))\n",
    "            s = states[i]\n",
    "            action = actions[i]\n",
    "            sT = states[i+1]\n",
    "            # propogate until reaching next state\n",
    "            while True:\n",
    "                traj.append(np.array(s))\n",
    "                #print(\"porpagating...\")\n",
    "                #print(s)\n",
    "                #print('st:')\n",
    "                #print(sT)\n",
    "                s = self.system.propagate(s, action, 1, self.params['integration_step'])\n",
    "                if np.linalg.norm(s-sT) == 0.:\n",
    "                    break\n",
    "        traj = np.array(traj)\n",
    "        print(\"animating...\")\n",
    "        # animate\n",
    "        self.states = traj\n",
    "        self.obs = obstacles\n",
    "        print(len(self.states))\n",
    "        ani = animation.FuncAnimation(plt.gcf(), self._animate, range(0, len(self.states)),\n",
    "                                      interval=self.dt*10, blit=True, init_func=self._init,\n",
    "                                      repeat=True)\n",
    "        return ani\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state: 0, remaining: 16\n",
      "state: 1, remaining: 15\n",
      "state: 2, remaining: 14\n",
      "state: 3, remaining: 13\n",
      "state: 4, remaining: 12\n",
      "state: 5, remaining: 11\n",
      "state: 6, remaining: 10\n",
      "state: 7, remaining: 9\n",
      "state: 8, remaining: 8\n",
      "state: 9, remaining: 7\n",
      "state: 10, remaining: 6\n",
      "state: 11, remaining: 5\n",
      "state: 12, remaining: 4\n",
      "state: 13, remaining: 3\n",
      "state: 14, remaining: 2\n",
      "animating...\n",
      "1641\n"
     ]
    },
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {}\n",
    "params['pole_l'] = 2.5\n",
    "params['pole_w'] = 0.1\n",
    "params['cart_w'] = 1.\n",
    "params['cart_h'] = 0.5\n",
    "params['obs_w'] = 4\n",
    "params['obs_h'] = 4\n",
    "params['integration_step'] = 0.002\n",
    "system = CartPole(obs_list)\n",
    "vis = CartPoleVisualizer(system, params)\n",
    "states, actions, costs = solution\n",
    "anim = vis.animate(np.array(states), np.array(actions), obs_list)\n",
    "HTML(anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-10.20745995   3.0188329 ]\n",
      " [ -5.27464147  -2.12034307]\n",
      " [-10.10767577  -2.57065996]\n",
      " [  7.97226132   4.28624   ]\n",
      " [  4.91782384  -2.32228069]\n",
      " [-16.8481709    2.52533289]]\n"
     ]
    }
   ],
   "source": [
    "print(obs_list)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:ylmiao_kino]",
   "language": "python",
   "name": "conda-env-ylmiao_kino-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}