acrobot_obs_vis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating obs...\n",
      "generated.\n",
      "(1, 4, 2)\n",
      "[[  8.56106657  24.65064431]\n",
      " [-14.15088025  21.95995866]\n",
      " [ 25.06862422 -11.56989593]\n",
      " [-23.67215187  -9.17214916]]\n",
      "[[[6.5610665733400815, 22.650644308849348], [6.5610665733400815, 26.650644308849348], [10.561066573340081, 26.650644308849348], [10.561066573340081, 22.650644308849348]], [[-16.150880249663516, 19.959958657095704], [-16.150880249663516, 23.959958657095704], [-12.150880249663516, 23.959958657095704], [-12.150880249663516, 19.959958657095704]], [[23.068624218980926, -13.569895934330166], [23.068624218980926, -9.569895934330166], [27.068624218980926, -9.569895934330166], [27.068624218980926, -13.569895934330166]], [[-25.672151870871392, -11.172149159308665], [-25.672151870871392, -7.172149159308665], [-21.672151870871392, -7.172149159308665], [-21.672151870871392, -11.172149159308665]]]\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.acrobot import Acrobot\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",
    "from plan_utility.line_line_cc import *\n",
    "obs_list = []\n",
    "LENGTH = 20.\n",
    "width = 4.\n",
    "near = LENGTH * 1.2\n",
    "print('generating obs...')\n",
    "\"\"\"\n",
    "for i in range(1):\n",
    "    obs_single = []\n",
    "    for j in range(4):\n",
    "        '''\n",
    "        The obstacle should be inside the maximal circle, but also not blocking (0,0)\n",
    "        '''\n",
    "        while True:\n",
    "            obs = np.random.normal(size=2)\n",
    "            obs = obs / np.linalg.norm(obs) * (LENGTH * 2 - LENGTH / 2)\n",
    "            while True:\n",
    "                # make sure it does not block (0,0)\n",
    "                alpha = np.random.uniform(low=0.5, high=1.)\n",
    "                obs_ = alpha * obs\n",
    "                if np.abs(obs_).max() > width/2:\n",
    "                    obs = obs_\n",
    "                    break\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",
    "                    #print(\"too near\")\n",
    "                    break\n",
    "            if not too_near:\n",
    "                #print('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",
    "\"\"\"\n",
    "# generate one obs in each phase\n",
    "for i in range(1):\n",
    "    obs_single = []\n",
    "    for j in range(4):\n",
    "        '''\n",
    "        The obstacle should be inside the maximal circle, but also not blocking (0,0)\n",
    "        '''\n",
    "        while True:\n",
    "            obs = np.random.normal(size=2)\n",
    "            obs = obs / np.linalg.norm(obs) * (LENGTH * 2 - LENGTH / 2)\n",
    "            if j == 0:\n",
    "                obs = np.abs(obs)  # +,+\n",
    "            elif j == 1:\n",
    "                obs = np.abs(obs)\n",
    "                obs[0] = -obs[0]  # -,+\n",
    "            elif j == 2:\n",
    "                obs = np.abs(obs)\n",
    "                obs[1] = -obs[1]  # +,-\n",
    "            elif j== 3:\n",
    "                obs = -np.abs(obs)\n",
    "            while True:\n",
    "                # make sure it does not block (0,0)\n",
    "                alpha = np.random.uniform(low=0.8, high=1.)\n",
    "                obs_ = alpha * obs\n",
    "                if np.abs(obs_).max() > width/2:\n",
    "                    obs = obs_\n",
    "                    break\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",
    "                    #print(\"too near\")\n",
    "                    break\n",
    "            if not too_near:\n",
    "                #print('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",
    "\n",
    "\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",
    "# obs to rectangle\n",
    "obs_recs = []\n",
    "for i in range(len(obs_list)):\n",
    "    # for each obs setting\n",
    "    obs_rec = []\n",
    "    for j in range(len(obs_list[i])):\n",
    "        obs_rec.append([[obs_list[i][j][0]-width/2,obs_list[i][j][1]-width/2],\n",
    "                        [obs_list[i][j][0]-width/2,obs_list[i][j][1]+width/2],\n",
    "                        [obs_list[i][j][0]+width/2,obs_list[i][j][1]+width/2],\n",
    "                        [obs_list[i][j][0]+width/2,obs_list[i][j][1]-width/2]])\n",
    "    obs_recs.append(obs_rec)\n",
    "\n",
    "# Create custom system\n",
    "#obs_list = [[-10., -3.],\n",
    "#            [0., 3.],\n",
    "#            [10, -3.]]\n",
    "obs_list = obs_list[0]\n",
    "obs_recs = obs_recs[0]\n",
    "print(obs_list)\n",
    "print(obs_recs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generated start point\n",
      "generated endpoint\n",
      "start:\n",
      "[ 1.01588336 -2.36089024  0.          0.        ]\n",
      "goal:\n",
      "[-1.08963115 -1.10433322  0.          0.        ]\n",
      "Solution: (array([[ 1.01588336, -2.36089024,  0.        ,  0.        ],\n",
      "       [ 0.90653999, -2.26876406, -1.18261792,  1.01703231],\n",
      "       [ 0.6917839 , -2.06622705, -1.95463775,  2.01401391],\n",
      "       [-0.25088074, -0.71317009, -3.89143814,  6.        ],\n",
      "       [-0.99218128,  0.46587282, -3.06132008,  5.27348756],\n",
      "       [-1.34489019,  1.1344119 , -0.23720664,  1.2035884 ],\n",
      "       [-1.09365225,  1.17462561,  2.17321239, -1.15994247],\n",
      "       [-0.26899954,  0.57295293,  3.53311799, -3.0653353 ],\n",
      "       [ 0.76764708, -0.20611507,  1.35445494,  0.09575689],\n",
      "       [ 0.87956   , -0.09415502,  0.04901664,  1.72781409],\n",
      "       [ 0.52578055,  0.86078952, -2.45223824,  4.71409011],\n",
      "       [ 0.26883989,  1.32597233, -2.70952395,  4.48423891],\n",
      "       [-0.48784862,  2.39242206, -2.91244689,  3.46347168],\n",
      "       [-0.76435277,  2.70970751, -2.48933306,  2.73483869],\n",
      "       [-1.11302656,  3.0909068 , -1.15173037,  1.33914087],\n",
      "       [-1.20094064, -3.07402949, -0.13723395,  0.48836766],\n",
      "       [-0.72174626,  3.00651041,  2.62233926, -1.51583954],\n",
      "       [-0.44134303,  2.83851382,  3.02095366, -1.90053366],\n",
      "       [ 0.36980728,  2.28899377,  2.31426884, -1.7209259 ],\n",
      "       [ 0.72831938,  2.0172255 ,  0.41256264, -0.23466836],\n",
      "       [ 0.383204  ,  2.38420262, -2.47353095,  2.27148711],\n",
      "       [-0.5014219 ,  3.10194801, -2.95228761,  2.22540091],\n",
      "       [-1.19290369, -2.57527516, -0.7170334 ,  1.08049151],\n",
      "       [-1.23061836, -2.49066516,  0.13114278,  0.49309044],\n",
      "       [-1.18276747, -2.46713397,  0.99487229, -0.13964118],\n",
      "       [-0.17991255, -3.03544139,  4.0040991 , -2.45918486],\n",
      "       [ 0.29582255,  2.96981892,  3.77736064, -2.05981897],\n",
      "       [ 0.8455681 ,  2.66244715,  2.8460323 , -1.63479284],\n",
      "       [ 1.35037854,  2.38078945, -0.28875392,  0.41523157],\n",
      "       [ 1.01732263,  2.71458903, -2.65435903,  2.42919179],\n",
      "       [-0.41890939, -2.29771498, -3.45702772,  3.61537066],\n",
      "       [-0.73736563, -1.93916567, -2.73958182,  3.48436349],\n",
      "       [-1.15354126, -1.25465463, -0.20212741,  1.34837663],\n",
      "       [-1.05552838, -1.07770271,  1.29040007,  0.39982481]]), array([[-3.87305839],\n",
      "       [-2.01726593],\n",
      "       [ 3.90349601],\n",
      "       [ 1.43465759],\n",
      "       [-3.35195585],\n",
      "       [ 1.98375179],\n",
      "       [ 3.0100944 ],\n",
      "       [ 2.94591861],\n",
      "       [ 1.30604411],\n",
      "       [ 2.98513253],\n",
      "       [ 1.43055612],\n",
      "       [ 3.08304528],\n",
      "       [ 1.58418422],\n",
      "       [ 1.28698227],\n",
      "       [ 2.06306685],\n",
      "       [ 3.01118626],\n",
      "       [ 2.39860965],\n",
      "       [ 3.3443728 ],\n",
      "       [ 2.89724236],\n",
      "       [ 3.36051692],\n",
      "       [ 2.37357475],\n",
      "       [ 3.53151537],\n",
      "       [ 2.60846754],\n",
      "       [ 1.99680891],\n",
      "       [-1.14911741],\n",
      "       [ 2.56366937],\n",
      "       [-0.81635241],\n",
      "       [-2.06164096],\n",
      "       [ 0.10763949],\n",
      "       [-0.937261  ],\n",
      "       [-0.51641438],\n",
      "       [-1.97489158],\n",
      "       [ 1.11490123]]), array([0.2 , 0.14, 0.32, 0.2 , 0.2 , 0.28, 0.28, 0.36, 0.14, 0.28, 0.1 ,\n",
      "       0.26, 0.1 , 0.18, 0.12, 0.38, 0.1 , 0.28, 0.24, 0.34, 0.3 , 0.34,\n",
      "       0.1 , 0.1 , 0.38, 0.12, 0.16, 0.36, 0.24, 0.4 , 0.1 , 0.26, 0.36])), Number of nodes: 2198\n",
      "time spent: 1.575907\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#obs_list = np.array(obs_list)\n",
    "system = standard_cpp_systems.RectangleObs(obs_list, 4., 'acrobot')\n",
    "#system = standard_cpp_systems.TwoLinkAcrobot()\n",
    "#system = CartPoleObs(obs_list)\n",
    "# Create SST planner\n",
    "min_time_steps = 5\n",
    "max_time_steps = 20\n",
    "integration_step = 0.02\n",
    "max_iter = 300000\n",
    "goal_radius=2.0\n",
    "random_seed=0\n",
    "sst_delta_near=1.0\n",
    "sst_delta_drain=0.5\n",
    "s_g_dis_threshold = LENGTH * 1.5\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.zeros(4)\n",
    "end = np.zeros(4)\n",
    "while True:\n",
    "    while True:\n",
    "        start[0] = np.random.uniform(low=low[0], high=high[0])\n",
    "        # make sure start midpoint\n",
    "        x0_start = 0.\n",
    "        y0_start = 0.\n",
    "        x1_start = LENGTH * np.cos(start[0] - np.pi/2)\n",
    "        y1_start = LENGTH * np.sin(start[0] - np.pi/2)\n",
    "        #print('x1 = %f, y1 = %f' % (x1_start, y1_start))\n",
    "        # make sure (x0, y0) to (x1, y1) is collision free\n",
    "        cf = True\n",
    "        for i in range(len(obs_recs)):\n",
    "            for j in range(4):\n",
    "                if line_line_cc(x0_start, y0_start, x1_start, y1_start, obs_recs[i][j][0], obs_recs[i][j][1], \n",
    "                                obs_recs[i][(j+1)%4][0], obs_recs[i][(j+1)%4][1]):\n",
    "                    cf = False\n",
    "                    break\n",
    "        if cf:\n",
    "            break\n",
    "    print('generated start point')\n",
    "\n",
    "    while True:\n",
    "        start[1] = np.random.uniform(low=low[1], high=high[1])\n",
    "        # make sure start and end not in collision\n",
    "        x2_start = LENGTH * np.cos(start[0] + start[1] - np.pi/2) + x1_start\n",
    "        y2_start = LENGTH * np.sin(start[0] + start[1] - np.pi/2) + y1_start\n",
    "        # make sure (x0, y0) to (x1, y1) is collision free\n",
    "        cf = True\n",
    "        for i in range(len(obs_recs)):\n",
    "            for j in range(4):\n",
    "                if line_line_cc(x1_start, y1_start, x2_start, y2_start, obs_recs[i][j][0], obs_recs[i][j][1], \n",
    "                                obs_recs[i][(j+1)%4][0], obs_recs[i][(j+1)%4][1]):\n",
    "                    cf = False\n",
    "                    break\n",
    "        if cf:\n",
    "            break\n",
    "    # start endpoint\n",
    "    while True:\n",
    "        print('generated endpoint')\n",
    "        while True:\n",
    "            end[0] = np.random.uniform(low=low[0], high=high[0])\n",
    "            # make sure start midpoint\n",
    "            x0_goal = 0.\n",
    "            y0_goal = 0.\n",
    "            x1_goal = LENGTH * np.cos(end[0] - np.pi/2)\n",
    "            y1_goal = LENGTH * np.sin(end[0] - np.pi/2)\n",
    "            # make sure (x0, y0) to (x1, y1) is collision free\n",
    "            cf = True\n",
    "            for i in range(len(obs_recs)):\n",
    "                for j in range(4):\n",
    "                    if line_line_cc(x0_goal, y0_goal, x1_goal, y1_goal, obs_recs[i][j][0], obs_recs[i][j][1], \n",
    "                                    obs_recs[i][(j+1)%4][0], obs_recs[i][(j+1)%4][1]):\n",
    "                        cf = False\n",
    "                        break\n",
    "            if cf:\n",
    "                break\n",
    "        # start endpoint\n",
    "        while True:\n",
    "            end[1] = np.random.uniform(low=low[1], high=high[1])\n",
    "            # make sure start and end not in collision\n",
    "            x2_goal = LENGTH * np.cos(end[0] + end[1] - np.pi/2) + x1_goal\n",
    "            y2_goal = LENGTH * np.sin(end[0] + end[1] - np.pi/2) + y1_goal\n",
    "            # make sure (x0, y0) to (x1, y1) is collision free\n",
    "            cf = True\n",
    "            for i in range(len(obs_recs)):\n",
    "                for j in range(4):\n",
    "                    if line_line_cc(x1_goal, y1_goal, x2_goal, y2_goal, obs_recs[i][j][0], obs_recs[i][j][1], \n",
    "                                    obs_recs[i][(j+1)%4][0], obs_recs[i][(j+1)%4][1]):\n",
    "                        cf = False\n",
    "                        break\n",
    "            # add endpoint length constraint\n",
    "            if cf and np.linalg.norm(np.array([x2_goal,y2_goal])-np.array([x2_start,y2_start])) >= s_g_dis_threshold:\n",
    "                break\n",
    "        # need to be in different phase, i.e. at least one sign need to be different\n",
    "        start_sign = np.array([x2_start, y2_start]) >= 0\n",
    "        end_sign = np.array([x2_goal, y2_goal]) >= 0\n",
    "        if start_sign[0] != end_sign[0] or start_sign[1] != end_sign[1]:\n",
    "            break\n",
    "    \n",
    "\n",
    "    start[2] = 0.\n",
    "    start[3] = 0.\n",
    "    end[2] = 0.\n",
    "    end[3] = 0.\n",
    "    print('start:')\n",
    "    print(start)\n",
    "    print('goal:')\n",
    "    print(end)\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",
    "        if planner.get_solution() is not None:\n",
    "            break\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",
    "    if solution is not None:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34, 4)\n",
      "(33, 1)\n",
      "states:\n",
      "[[ 1.01588336 -2.36089024  0.          0.        ]\n",
      " [ 0.90653999 -2.26876406 -1.18261792  1.01703231]\n",
      " [ 0.6917839  -2.06622705 -1.95463775  2.01401391]\n",
      " [-0.25088074 -0.71317009 -3.89143814  6.        ]\n",
      " [-0.99218128  0.46587282 -3.06132008  5.27348756]\n",
      " [-1.34489019  1.1344119  -0.23720664  1.2035884 ]\n",
      " [-1.09365225  1.17462561  2.17321239 -1.15994247]\n",
      " [-0.26899954  0.57295293  3.53311799 -3.0653353 ]\n",
      " [ 0.76764708 -0.20611507  1.35445494  0.09575689]\n",
      " [ 0.87956    -0.09415502  0.04901664  1.72781409]\n",
      " [ 0.52578055  0.86078952 -2.45223824  4.71409011]\n",
      " [ 0.26883989  1.32597233 -2.70952395  4.48423891]\n",
      " [-0.48784862  2.39242206 -2.91244689  3.46347168]\n",
      " [-0.76435277  2.70970751 -2.48933306  2.73483869]\n",
      " [-1.11302656  3.0909068  -1.15173037  1.33914087]\n",
      " [-1.20094064 -3.07402949 -0.13723395  0.48836766]\n",
      " [-0.72174626  3.00651041  2.62233926 -1.51583954]\n",
      " [-0.44134303  2.83851382  3.02095366 -1.90053366]\n",
      " [ 0.36980728  2.28899377  2.31426884 -1.7209259 ]\n",
      " [ 0.72831938  2.0172255   0.41256264 -0.23466836]\n",
      " [ 0.383204    2.38420262 -2.47353095  2.27148711]\n",
      " [-0.5014219   3.10194801 -2.95228761  2.22540091]\n",
      " [-1.19290369 -2.57527516 -0.7170334   1.08049151]\n",
      " [-1.23061836 -2.49066516  0.13114278  0.49309044]\n",
      " [-1.18276747 -2.46713397  0.99487229 -0.13964118]\n",
      " [-0.17991255 -3.03544139  4.0040991  -2.45918486]\n",
      " [ 0.29582255  2.96981892  3.77736064 -2.05981897]\n",
      " [ 0.8455681   2.66244715  2.8460323  -1.63479284]\n",
      " [ 1.35037854  2.38078945 -0.28875392  0.41523157]\n",
      " [ 1.01732263  2.71458903 -2.65435903  2.42919179]\n",
      " [-0.41890939 -2.29771498 -3.45702772  3.61537066]\n",
      " [-0.73736563 -1.93916567 -2.73958182  3.48436349]\n",
      " [-1.15354126 -1.25465463 -0.20212741  1.34837663]\n",
      " [-1.05552838 -1.07770271  1.29040007  0.39982481]]\n"
     ]
    }
   ],
   "source": [
    "states, actions, costs = solution\n",
    "print(states.shape)\n",
    "print(actions.shape)\n",
    "print('states:')\n",
    "print(states)"
   ]
  },
  {
   "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 AcrobotVisualizer(Visualizer):\n",
    "    def __init__(self, system, params):\n",
    "        super(AcrobotVisualizer, self).__init__(system, params)\n",
    "        self.dt = 0.05\n",
    "\n",
    "    def _state_to_xy(self, state):\n",
    "        angle0 = state[0]\n",
    "        angle1 = state[1]\n",
    "        x0 = 0.\n",
    "        y0 = 0.\n",
    "        x1 = LENGTH * np.cos(angle0 - np.pi/2)\n",
    "        y1 = LENGTH * np.sin(angle0 - np.pi/2)\n",
    "        x2 = x1 + LENGTH * np.cos(angle0 + angle1 - np.pi/2)\n",
    "        y2 = y1 + LENGTH * np.sin(angle0 + angle1 - np.pi/2)\n",
    "        return x0, y0, x1, y1, x2, y2\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",
    "        print('state:')\n",
    "        print(state)\n",
    "        x0, y0, x1, y1, x2, y2 = self._state_to_xy(state)\n",
    "        print('x1: %f, y1: %f, x2: %f, y2: %f' % (x1, y1, x2, y2))\n",
    "        self.l1 = ax.plot([x0,x1,x2], [y0,y1,y2])[0]\n",
    "        self.recs = []\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",
    "        self.recs.append(self.l1)\n",
    "        return self.recs\n",
    "    def _animate(self, i):\n",
    "        ax = plt.gca()\n",
    "        ax.set_xlim(-40, 40)\n",
    "        ax.set_ylim(-40, 40)\n",
    "        state = self.states[i]\n",
    "        x0, y0, x1, y1, x2, y2 = self._state_to_xy(state)\n",
    "        self.l1.set_xdata([x0,x1,x2])\n",
    "        self.l1.set_ydata([y0,y1,y2])\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",
    "            print(s)\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",
    "                #print('distance:')\n",
    "                #print(np.linalg.norm(s-sT))\n",
    "                s = self.system.propagate(s, action, 1, self.params['integration_step'])\n",
    "                if np.linalg.norm(s-sT) <= 0.1:\n",
    "                    break\n",
    "        traj = np.array(traj)\n",
    "        print(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*500, 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": [
      "states:\n",
      "[[ 1.01588336 -2.36089024  0.          0.        ]\n",
      " [ 0.90653999 -2.26876406 -1.18261792  1.01703231]\n",
      " [ 0.6917839  -2.06622705 -1.95463775  2.01401391]\n",
      " [-0.25088074 -0.71317009 -3.89143814  6.        ]\n",
      " [-0.99218128  0.46587282 -3.06132008  5.27348756]\n",
      " [-1.34489019  1.1344119  -0.23720664  1.2035884 ]\n",
      " [-1.09365225  1.17462561  2.17321239 -1.15994247]\n",
      " [-0.26899954  0.57295293  3.53311799 -3.0653353 ]\n",
      " [ 0.76764708 -0.20611507  1.35445494  0.09575689]\n",
      " [ 0.87956    -0.09415502  0.04901664  1.72781409]\n",
      " [ 0.52578055  0.86078952 -2.45223824  4.71409011]\n",
      " [ 0.26883989  1.32597233 -2.70952395  4.48423891]\n",
      " [-0.48784862  2.39242206 -2.91244689  3.46347168]\n",
      " [-0.76435277  2.70970751 -2.48933306  2.73483869]\n",
      " [-1.11302656  3.0909068  -1.15173037  1.33914087]\n",
      " [-1.20094064 -3.07402949 -0.13723395  0.48836766]\n",
      " [-0.72174626  3.00651041  2.62233926 -1.51583954]\n",
      " [-0.44134303  2.83851382  3.02095366 -1.90053366]\n",
      " [ 0.36980728  2.28899377  2.31426884 -1.7209259 ]\n",
      " [ 0.72831938  2.0172255   0.41256264 -0.23466836]\n",
      " [ 0.383204    2.38420262 -2.47353095  2.27148711]\n",
      " [-0.5014219   3.10194801 -2.95228761  2.22540091]\n",
      " [-1.19290369 -2.57527516 -0.7170334   1.08049151]\n",
      " [-1.23061836 -2.49066516  0.13114278  0.49309044]\n",
      " [-1.18276747 -2.46713397  0.99487229 -0.13964118]\n",
      " [-0.17991255 -3.03544139  4.0040991  -2.45918486]\n",
      " [ 0.29582255  2.96981892  3.77736064 -2.05981897]\n",
      " [ 0.8455681   2.66244715  2.8460323  -1.63479284]\n",
      " [ 1.35037854  2.38078945 -0.28875392  0.41523157]\n",
      " [ 1.01732263  2.71458903 -2.65435903  2.42919179]\n",
      " [-0.41890939 -2.29771498 -3.45702772  3.61537066]\n",
      " [-0.73736563 -1.93916567 -2.73958182  3.48436349]\n",
      " [-1.15354126 -1.25465463 -0.20212741  1.34837663]\n",
      " [-1.05552838 -1.07770271  1.29040007  0.39982481]]\n",
      "state: 0, remaining: 34\n",
      "[ 1.01588336 -2.36089024  0.          0.        ]\n",
      "state: 1, remaining: 33\n",
      "[ 0.90653999 -2.26876406 -1.18261792  1.01703231]\n",
      "state: 2, remaining: 32\n",
      "[ 0.6917839  -2.06622705 -1.95463775  2.01401391]\n",
      "state: 3, remaining: 31\n",
      "[-0.25088074 -0.71317009 -3.89143814  6.        ]\n",
      "state: 4, remaining: 30\n",
      "[-0.99218128  0.46587282 -3.06132008  5.27348756]\n",
      "state: 5, remaining: 29\n",
      "[-1.34489019  1.1344119  -0.23720664  1.2035884 ]\n",
      "state: 6, remaining: 28\n",
      "[-1.09365225  1.17462561  2.17321239 -1.15994247]\n",
      "state: 7, remaining: 27\n",
      "[-0.26899954  0.57295293  3.53311799 -3.0653353 ]\n",
      "state: 8, remaining: 26\n",
      "[ 0.76764708 -0.20611507  1.35445494  0.09575689]\n",
      "state: 9, remaining: 25\n",
      "[ 0.87956    -0.09415502  0.04901664  1.72781409]\n",
      "state: 10, remaining: 24\n",
      "[ 0.52578055  0.86078952 -2.45223824  4.71409011]\n",
      "state: 11, remaining: 23\n",
      "[ 0.26883989  1.32597233 -2.70952395  4.48423891]\n",
      "state: 12, remaining: 22\n",
      "[-0.48784862  2.39242206 -2.91244689  3.46347168]\n",
      "state: 13, remaining: 21\n",
      "[-0.76435277  2.70970751 -2.48933306  2.73483869]\n",
      "state: 14, remaining: 20\n",
      "[-1.11302656  3.0909068  -1.15173037  1.33914087]\n",
      "state: 15, remaining: 19\n",
      "[-1.20094064 -3.07402949 -0.13723395  0.48836766]\n",
      "state: 16, remaining: 18\n",
      "[-0.72174626  3.00651041  2.62233926 -1.51583954]\n",
      "state: 17, remaining: 17\n",
      "[-0.44134303  2.83851382  3.02095366 -1.90053366]\n",
      "state: 18, remaining: 16\n",
      "[ 0.36980728  2.28899377  2.31426884 -1.7209259 ]\n",
      "state: 19, remaining: 15\n",
      "[ 0.72831938  2.0172255   0.41256264 -0.23466836]\n",
      "state: 20, remaining: 14\n",
      "[ 0.383204    2.38420262 -2.47353095  2.27148711]\n",
      "state: 21, remaining: 13\n",
      "[-0.5014219   3.10194801 -2.95228761  2.22540091]\n",
      "state: 22, remaining: 12\n",
      "[-1.19290369 -2.57527516 -0.7170334   1.08049151]\n",
      "state: 23, remaining: 11\n",
      "[-1.23061836 -2.49066516  0.13114278  0.49309044]\n",
      "state: 24, remaining: 10\n",
      "[-1.18276747 -2.46713397  0.99487229 -0.13964118]\n",
      "state: 25, remaining: 9\n",
      "[-0.17991255 -3.03544139  4.0040991  -2.45918486]\n",
      "state: 26, remaining: 8\n",
      "[ 0.29582255  2.96981892  3.77736064 -2.05981897]\n",
      "state: 27, remaining: 7\n",
      "[ 0.8455681   2.66244715  2.8460323  -1.63479284]\n",
      "state: 28, remaining: 6\n",
      "[ 1.35037854  2.38078945 -0.28875392  0.41523157]\n",
      "state: 29, remaining: 5\n",
      "[ 1.01732263  2.71458903 -2.65435903  2.42919179]\n",
      "state: 30, remaining: 4\n",
      "[-0.41890939 -2.29771498 -3.45702772  3.61537066]\n",
      "state: 31, remaining: 3\n",
      "[-0.73736563 -1.93916567 -2.73958182  3.48436349]\n",
      "state: 32, remaining: 2\n",
      "[-1.15354126 -1.25465463 -0.20212741  1.34837663]\n",
      "[[ 1.01588336 -2.36089024  0.          0.        ]\n",
      " [ 1.01588336 -2.36089024 -0.1241758   0.10340811]\n",
      " [ 1.01339985 -2.35882207 -0.24803717  0.20651249]\n",
      " ...\n",
      " [-1.11627424 -1.11071067  0.87270281  0.6302863 ]\n",
      " [-1.09882018 -1.09810494  1.01636321  0.54558119]\n",
      " [-1.07849292 -1.08719332  1.15633066  0.46803953]]\n",
      "animating...\n",
      "378\n",
      "state:\n",
      "[ 1.01588336 -2.36089024  0.          0.        ]\n",
      "x1: 16.998926, y1: -10.537387, x2: -2.493427, y2: -15.014903\n",
      "state:\n",
      "[ 1.01588336 -2.36089024  0.          0.        ]\n",
      "x1: 16.998926, y1: -10.537387, x2: -2.493427, y2: -15.014903\n"
     ]
    },
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       "AGAAAAA/AAAAPQAAAOEAAABZAAAAOwAAADkAAAB8AAAAbAAAADQAAAAqAAAAHgAAAG4AAAAyAAAA\n",
       "JQAAADIAAACcAAAASgAAADgAAAA2AAAAyAAAAFcAAAA7AAAAQwAAAHIAAAEsAAAAeAAAAEQAAABE\n",
       "AAAAiAAAAI8AAACVAAABPwAAALcAAAB7AAAAcAAAAUMAAACzAAAAXgAAAGYAAACPAAAAmQAAAIoA\n",
       "AACgAAAAowAAAK4AAACQAAAAqAAAAcAAAAB8AAAASwAAAE8AAACTAAABagAAAHYAAABVAAAAQwAA\n",
       "AU8AAABZAAAAPwAAADoAAADCAAAAUwAAAD8AAAA3AAAAqQAAAFUAAAA7AAAAQgAAAI8AAABTAAAA\n",
       "RAAAABRzdGNvAAAAAAAAAAEAAAAsAAAAYnVkdGEAAABabWV0YQAAAAAAAAAhaGRscgAAAAAAAAAA\n",
       "bWRpcmFwcGwAAAAAAAAAAAAAAAAtaWxzdAAAACWpdG9vAAAAHWRhdGEAAAABAAAAAExhdmY1Ni40\n",
       "MC4xMDE=\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "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['obs_w'] = 4\n",
    "params['obs_h'] = 4\n",
    "params['integration_step'] = 0.02\n",
    "system = Acrobot()\n",
    "vis = AcrobotVisualizer(system, params)\n",
    "states, actions, costs = solution\n",
    "print('states:')\n",
    "print(states)\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": [
      "[[  8.56106657  24.65064431]\n",
      " [-14.15088025  21.95995866]\n",
      " [ 25.06862422 -11.56989593]\n",
      " [-23.67215187  -9.17214916]]\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
}