test_odometry copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass\n",
    "\n",
    "import geometry as geo\n",
    "import gym_duckietown\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from gym_duckietown.simulator import Simulator\n",
    "from IPython import display as ipythondisplay\n",
    "\n",
    "from utils import get_position, Position, my_odometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [7, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gym_duckietown is very chatty\n",
    "import logging\n",
    "gym_duckietown.logger.setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:duckietown_world: data: /home/giulio/prog/.virtualenvs/RACE/lib/python3.8/site-packages/duckietown_world/data\n"
     ]
    }
   ],
   "source": [
    "env = Simulator(\"small_loop\",\n",
    "    full_transparency=True,\n",
    "    domain_rand=False,\n",
    "    user_tile_start=[1,0],\n",
    "    seed=42,\n",
    "    max_steps=float(\"inf\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.unwrapped.start_pose = [[0.11699990272521976, 0, 0.41029359288296474], 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.0\n",
      "0.09999999999999898\n",
      "0.18333333333333202\n",
      "0.25277777777777843\n",
      "0.31064814814814823\n",
      "0.35887345679012306\n",
      "0.3990612139917682\n",
      "0.4325510116598086\n",
      "0.46045917638317224\n",
      "0.4837159803193125\n",
      "0.5030966502660916\n",
      "0.5192472085550759\n",
      "0.53270600712923\n",
      "0.5439216726076912\n",
      "0.5532680605064111\n",
      "0.5610567170886738\n",
      "0.5675472642405621\n",
      "0.5729560535338007\n",
      "0.5774633779448346\n",
      "0.5812194816206973\n",
      "0.584349568017245\n",
      "0.5869579733477037\n",
      "0.5891316444564243\n",
      "0.5909430370470203\n",
      "0.5924525308725159\n",
      "0.5937104423937622\n",
      "0.5947587019948042\n",
      "0.5956322516623325\n",
      "0.5963602097186138\n",
      "0.596966841432176\n",
      "0.5974723678601479\n",
      "0.5978936398834533\n",
      "0.5982446999028812\n",
      "0.5985372499190666\n",
      "0.5987810415992234\n",
      "0.5989842013326863\n",
      "0.5991535011105698\n",
      "0.5992945842588071\n",
      "0.5994121535490105\n",
      "0.5995101279575032\n",
      "0.599591773297925\n",
      "0.5996598110815987\n",
      "0.5997165092346712\n",
      "0.599763757695555\n",
      "0.5998031314129615\n",
      "0.5998359428441336\n",
      "0.5998632857034503\n",
      "0.5998860714195375\n",
      "0.5999050595162858\n",
      "0.5999208829302383\n",
      "0.5999340691085275\n",
      "0.5999450575904386\n",
      "0.5999542146587022\n",
      "0.5999618455489153\n",
      "0.5999682046240951\n"
     ]
    }
   ],
   "source": [
    "env.reset()\n",
    "for _ in range(100):\n",
    "    obs, reward, done, info = env.step([1,1])\n",
    "    if done:\n",
    "        break\n",
    "    print(info[\"Simulator\"][\"robot_speed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_time = 1.0 / env.frame_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://drive.google.com/file/d/19U1DUo3GtqHxncEKLn2d6RRdLTLgD0Bv/view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "u1 = 5\n",
    "u2 = 0\n",
    "u3 = 0\n",
    "w1 = 4\n",
    "w2 = 0\n",
    "w3 = 0\n",
    "# parameters for forced dynamics\n",
    "uar = 1.5\n",
    "ual = 1.5\n",
    "war = 15  # modify this for trim\n",
    "wal = 15\n",
    "\n",
    "u_alpha_r = uar\n",
    "u_alpha_l = ual\n",
    "w_alpha_r = war\n",
    "w_alpha_l = wal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "R = 0.067 / 2  # 6.7 cm diameter\n",
    "D = 0.102"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_acceleration(action, u=None, w=None):\n",
    "    \"\"\"\n",
    "    Second derivative of x\n",
    "    xdotdot[0] = acc longitudinal\n",
    "    xdotdot[1] = acc angular\n",
    "    \"\"\"\n",
    "    # https://drive.google.com/file/d/19U1DUo3GtqHxncEKLn2d6RRdLTLgD0Bv/view\n",
    "    # Paragraph 5.1.5\n",
    "    \n",
    "    # wr, wl\n",
    "    U = np.array([action[1], action[0]])\n",
    "    V = U.reshape(U.size, 1)\n",
    "    V = np.clip(V, -1, +1)\n",
    "\n",
    "    # Previous step linear and angular speed\n",
    "    # u, w = longit_prev, angular_prev\n",
    "\n",
    "    ## Calculate Dynamics\n",
    "    # nonlinear Dynamics - autonomous response\n",
    "    f_dynamic = np.array([[-u1 * u - u2 * w + u3 * w ** 2], [-w1 * w - w2 * u - w3 * u * w]])  #\n",
    "    # input Matrix\n",
    "    B = np.array([[u_alpha_r, u_alpha_l], [w_alpha_r, -w_alpha_l]])\n",
    "    # forced response\n",
    "    f_forced = np.matmul(B, V)\n",
    "    # acceleration\n",
    "    x_dot_dot = f_dynamic + f_forced\n",
    "    return x_dot_dot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myodom(action, x0, y0, theta0, v0=0, w0=0, dt=0.033):\n",
    "    x_dot_dot, w_dot_dot = get_acceleration(action, u=v0, w=w0)\n",
    "\n",
    "    v1 = v0 + x_dot_dot[0]*dt\n",
    "    w1 = w0 + w_dot_dot[0]*dt\n",
    "\n",
    "    # Runge Kutta\n",
    "    x1 = x0 + v0*dt*np.cos(theta0 + w0*dt/2)\n",
    "    y1 = y0 + v0*dt*np.sin(theta0 + w0*dt/2)\n",
    "    theta1 = theta0 + w0*dt\n",
    "\n",
    "    return Position(x1, y1, theta1), v1, w1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03333333333333333"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collision\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "env.reset()\n",
    "pose = get_position(env)\n",
    "delay = round(0.15/delta_time)\n",
    "actions = []\n",
    "odoms = [pose]\n",
    "odoms_l = [pose]\n",
    "poses = []\n",
    "v_1 = 0\n",
    "w_1 = 0\n",
    "v0 = 0\n",
    "w0 = 0\n",
    "v = [0,0]\n",
    "real_v = []\n",
    "w = [0,0]\n",
    "# plt.imshow(np.flip(env.render(mode=\"top_down\"), [0]))\n",
    "for i in range(50):\n",
    "    action = [1,0.2]\n",
    "    actions.append(action)\n",
    "    obs, reward, done, info = env.step(action)\n",
    "    if reward == -1000:\n",
    "        print(\"Collision\")\n",
    "        break\n",
    "    # Test odometry\n",
    "    real_v.append(info[\"Simulator\"][\"robot_speed\"])\n",
    "    if len(actions) < delay:\n",
    "        # p_l  = my_odom_lin(v0, w0, odoms_l[-1].x, odoms_l[-1].y, odoms_l[-1].theta, dt=delta_time, v_eq=v_1, w_eq=w_1)\n",
    "        # v0, w0 = v_1, w_1\n",
    "        # p, v0, w0 = auto_odom_full_direct([0,0], odoms[-1].x, odoms[-1].y, odoms[-1].theta, dt=delta_time, v0=v0, w0=w0)\n",
    "        # u[t], x0, y0, theta0, v0, w0, dt=dt\n",
    "        p, v0, w0 = myodom([0,0], odoms[-1].x, odoms[-1].y, odoms[-1].theta, v0, w0, dt=delta_time)\n",
    "        #res = linearized_odom([0,0], odoms[-1].x, odoms[-1].y, odoms[-1].theta, v0=v0, w0=w0, dt=delta_time)\n",
    "        # p, v0, w0 = Position(*res[:3]), res[3], res[4]\n",
    "    else:\n",
    "        p, v0, w0 = myodom(action, odoms[-1].x, odoms[-1].y, odoms[-1].theta, v0, w0, dt=delta_time)\n",
    "        # p, v0, w0 = Position(*res[:3]), res[3], res[4]\n",
    "    # p_l = Position(p_l[0], p_l[1], p_l[2])\n",
    "    v.append(v0)\n",
    "    w.append(w0)\n",
    "    odoms.append(p)\n",
    "    # odoms_l.append(p_l)\n",
    "    try:\n",
    "        pose = get_position(env)\n",
    "    except ValueError:\n",
    "        print(env.cartesian_from_weird(env.cur_pos, env.cur_angle))\n",
    "        break\n",
    "    poses.append(pose)\n",
    "    # print(loss(np.array([0,1]*int(horizon_len/2)).reshape(-1), pose, traj, index))\n",
    "    plt.scatter(pose.x, pose.y, marker=\"o\", c=\"b\")\n",
    "    # Test odometry\n",
    "    o = get_position(env)\n",
    "    # plt.scatter(o.x, o.y, marker=\"*\", c=\"r\")\n",
    "    plt.scatter(p.x, p.y, marker=\"*\", c=\"r\")\n",
    "    # Linearized odometry\n",
    "    # plt.scatter(p_l.x, p_l.y, marker=\"^\", c=\"g\")\n",
    "    # plt.imshow(obs)\n",
    "    ipythondisplay.clear_output(wait=True)\n",
    "    ipythondisplay.display(plt.gcf())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>theta</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.702000</td>\n",
       "      <td>1.344706</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.702000</td>\n",
       "      <td>1.344706</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.702000</td>\n",
       "      <td>1.344706</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.702000</td>\n",
       "      <td>1.344706</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.702000</td>\n",
       "      <td>1.344706</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.704000</td>\n",
       "      <td>1.344693</td>\n",
       "      <td>-0.013333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.707665</td>\n",
       "      <td>1.344599</td>\n",
       "      <td>-0.038222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.712713</td>\n",
       "      <td>1.344317</td>\n",
       "      <td>-0.073126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.718898</td>\n",
       "      <td>1.343728</td>\n",
       "      <td>-0.116709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.726003</td>\n",
       "      <td>1.342711</td>\n",
       "      <td>-0.167815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.733830</td>\n",
       "      <td>1.341152</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.742197</td>\n",
       "      <td>1.338952</td>\n",
       "      <td>-0.288714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.750931</td>\n",
       "      <td>1.336031</td>\n",
       "      <td>-0.356886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.759867</td>\n",
       "      <td>1.332325</td>\n",
       "      <td>-0.429301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.768850</td>\n",
       "      <td>1.327792</td>\n",
       "      <td>-0.505394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.777730</td>\n",
       "      <td>1.322408</td>\n",
       "      <td>-0.584675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.786366</td>\n",
       "      <td>1.316168</td>\n",
       "      <td>-0.666718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.794623</td>\n",
       "      <td>1.309086</td>\n",
       "      <td>-0.751156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.802377</td>\n",
       "      <td>1.301191</td>\n",
       "      <td>-0.837668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.809511</td>\n",
       "      <td>1.292530</td>\n",
       "      <td>-0.925979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.815919</td>\n",
       "      <td>1.283161</td>\n",
       "      <td>-1.015849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.821506</td>\n",
       "      <td>1.273156</td>\n",
       "      <td>-1.107069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.826190</td>\n",
       "      <td>1.262599</td>\n",
       "      <td>-1.199460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.829897</td>\n",
       "      <td>1.251582</td>\n",
       "      <td>-1.292865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.832571</td>\n",
       "      <td>1.240205</td>\n",
       "      <td>-1.387150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "           x         y     theta\n",
       "0   0.702000  1.344706  0.000000\n",
       "1   0.702000  1.344706  0.000000\n",
       "2   0.702000  1.344706  0.000000\n",
       "3   0.702000  1.344706  0.000000\n",
       "4   0.702000  1.344706  0.000000\n",
       "5   0.704000  1.344693 -0.013333\n",
       "6   0.707665  1.344599 -0.038222\n",
       "7   0.712713  1.344317 -0.073126\n",
       "8   0.718898  1.343728 -0.116709\n",
       "9   0.726003  1.342711 -0.167815\n",
       "10  0.733830  1.341152 -0.225439\n",
       "11  0.742197  1.338952 -0.288714\n",
       "12  0.750931  1.336031 -0.356886\n",
       "13  0.759867  1.332325 -0.429301\n",
       "14  0.768850  1.327792 -0.505394\n",
       "15  0.777730  1.322408 -0.584675\n",
       "16  0.786366  1.316168 -0.666718\n",
       "17  0.794623  1.309086 -0.751156\n",
       "18  0.802377  1.301191 -0.837668\n",
       "19  0.809511  1.292530 -0.925979\n",
       "20  0.815919  1.283161 -1.015849\n",
       "21  0.821506  1.273156 -1.107069\n",
       "22  0.826190  1.262599 -1.199460\n",
       "23  0.829897  1.251582 -1.292865\n",
       "24  0.832571  1.240205 -1.387150"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(odoms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1.344706</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.704000</td>\n",
       "      <td>1.344693</td>\n",
       "      <td>-0.8</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.707665</td>\n",
       "      <td>1.344599</td>\n",
       "      <td>-2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.712713</td>\n",
       "      <td>1.344317</td>\n",
       "      <td>-4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.718897</td>\n",
       "      <td>1.343728</td>\n",
       "      <td>-6.7</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.726001</td>\n",
       "      <td>1.342711</td>\n",
       "      <td>-9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.733828</td>\n",
       "      <td>1.341152</td>\n",
       "      <td>-12.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.742193</td>\n",
       "      <td>1.338953</td>\n",
       "      <td>-16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.750925</td>\n",
       "      <td>1.336032</td>\n",
       "      <td>-20.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.759859</td>\n",
       "      <td>1.332327</td>\n",
       "      <td>-24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.768840</td>\n",
       "      <td>1.327795</td>\n",
       "      <td>-29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.777718</td>\n",
       "      <td>1.322412</td>\n",
       "      <td>-33.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.786351</td>\n",
       "      <td>1.316174</td>\n",
       "      <td>-38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.794606</td>\n",
       "      <td>1.309094</td>\n",
       "      <td>-43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.802357</td>\n",
       "      <td>1.301202</td>\n",
       "      <td>-48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.809489</td>\n",
       "      <td>1.292543</td>\n",
       "      <td>-53.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.815895</td>\n",
       "      <td>1.283178</td>\n",
       "      <td>-58.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.821480</td>\n",
       "      <td>1.273176</td>\n",
       "      <td>-63.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.826162</td>\n",
       "      <td>1.262623</td>\n",
       "      <td>-68.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.829868</td>\n",
       "      <td>1.251610</td>\n",
       "      <td>-74.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.832541</td>\n",
       "      <td>1.240237</td>\n",
       "      <td>-79.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "           x         y  theta\n",
       "0   0.702000  1.344706    0.0\n",
       "1   0.702000  1.344706    0.0\n",
       "2   0.702000  1.344706    0.0\n",
       "3   0.702000  1.344706    0.0\n",
       "4   0.704000  1.344693   -0.8\n",
       "5   0.707665  1.344599   -2.2\n",
       "6   0.712713  1.344317   -4.2\n",
       "7   0.718897  1.343728   -6.7\n",
       "8   0.726001  1.342711   -9.6\n",
       "9   0.733828  1.341152  -12.9\n",
       "10  0.742193  1.338953  -16.5\n",
       "11  0.750925  1.336032  -20.4\n",
       "12  0.759859  1.332327  -24.6\n",
       "13  0.768840  1.327795  -29.0\n",
       "14  0.777718  1.322412  -33.5\n",
       "15  0.786351  1.316174  -38.2\n",
       "16  0.794606  1.309094  -43.0\n",
       "17  0.802357  1.301202  -48.0\n",
       "18  0.809489  1.292543  -53.1\n",
       "19  0.815895  1.283178  -58.2\n",
       "20  0.821480  1.273176  -63.4\n",
       "21  0.826162  1.262623  -68.7\n",
       "22  0.829868  1.251610  -74.1\n",
       "23  0.832541  1.240237  -79.5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   "source": [
    "pd.DataFrame(poses)"
   ]
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