deadrec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import math, random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "class Landmark:\n",
    "    def __init__(self,x,y):\n",
    "        self.position = np.array([x,y])\n",
    "        \n",
    "    def __str__(self):\n",
    "        return \"(%f,%f)\" % (self.position[0],self.position[1]) \n",
    "    \n",
    "    def getX(self):\n",
    "        return self.position[0]\n",
    "    \n",
    "    def getY(self):\n",
    "        return self.position[1]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "actual_landmarks = (Landmark(-0.5,0),Landmark(0.5,0),Landmark(0,0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs = [e.getX() for e in actual_landmarks]\n",
    "ys = [e.getY() for e in actual_landmarks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10ffa0f98>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NK1ZcyKpVa3n66ft59dW7gcVjtHyB+fOv4pxzXmHr1kc5++yzJ92npBNfO48t/f399Pf3\nV94P1HNNYAXQk5krm+vrGEml9S1t/hfwUGb+eXN9F3BxjjEdNFW3iA4NDXHddX/Ipk27OXDg/rds\n7+6+lLVrz+P222+lq6ur9v4lnZhmwrGl3beIDgBLI+KsiJgLrAG2jWqzDfgovBEar4wVAFOpq6uL\n005bzODgO8bcPjT0Dk4//W0GgKTj0unHlsohkJnDjFwNuQ/4PtCXmTsj4tqI+HizzV8D/xgRe4HP\nA39Qtd/J2LhxC4ODq5tre+nq+iSwF4DBwdVs3Djzb+eSNPN08rGllucEMvNvM/O8zPyXmXlL873P\nZ+aGljbXZ+bSzFyWmd+po9/jsWfPHp5//sfAe4E/Y96897B27avMm/ce4EvAe/nRj55n7969012a\npA7W6ceWYp4Y3rx5C8PDl3LSSVdxxhmfYceOB7j77jvYseMBzjjjZk466RqOHPkAmzfP3MSWNPN0\n/LFlsrcVTdWLmm8Rfd255/5GRszJK6/893ngwIE3bTtw4EB+5CMfy4g5ed55756S/iWdmGbCsYUK\nt4gWcSZw+PBhfv7zV+jr6+XeezfQ3d39pu3d3d1s3PgF+vp6ee21lzl8+HCbKpXUSU6EY0uRf0VU\nkk4k7b5FVJLUoQwBSSqYISBJBTMEJKlghoAkFcwQkKSCGQKSVDBDQJIKZghIUsEMAUkqmCEgSQUz\nBCSpYIaAJBXMEJCkghkCklQwQ0CSCmYISFLBDAFJKlilEIiIfx4R90XEDyPiGxFxylHa3RkRL0TE\nU1X6kyTVq+qZwDrggcw8D/gm8F+P0u6LwG9W7Kvj9ff3t7uEKeX4OpvjK1PVEFgF3NNcvge4bKxG\nmbkdeLliXx3vRP8hdHydzfGVqWoI/GJmvgCQmc8Dv1i9JEnSdJkzXoOIuB9Y3PoWkMCnx2ieNdUl\nSZoGkTn543ZE7AQamflCRLwNeCgzf+Uobc8CvpaZ7xpnnwaJJB2nzIzJfG7cM4FxbAOuAtYD/w7Y\neoy20Xwd02QHIkk6flWvCawHPhARPwQuAW4BiIjTIuLrrzeKiF7gW8C5EfF/IuLqiv1KkmpQaTpI\nktTZ2vrE8HE8bHZKRGyOiJ0R8f2IuHC6a52MiY6v2XZWRHwnIrZNZ41VTGR8EfH2iPhm83v7h4j4\nZDtqPR4RsTIidkXE7oi44ShtbouIPRHxZEScP901TtZ4Y4uItRHx3eZre0S8sx11TtZEvrtmu3dH\nxFBEXD6d9VU1wZ/NRkQ8ERHfi4iHxt1pZrbtxch00n9pLt8A3HKUdncDVzeX5wAL21l33eNrbv8j\n4M+Abe2uu87xAW8Dzm8uLwB+CPxyu2s/xphmAXuBs4Au4MnR9QIfBP6quXwh8Ei7665xbCuAU5rL\nKztlbBMdX0u7B4GvA5e3u+6av79TgO8DS5rrp46333b/7aBxHzaLiIXAv87MLwJk5uHM/L/TV2Il\nE3qYLiLeDvwWcMc01VWXcceXmc9n5pPN5QPATmDJtFV4/JYDezJzX2YOAX2MjLPVKuBegMx8FDgl\nIhYz8407tsx8JDN/1lx9hJn9XY02ke8O4BPAV4AfT2dxNZjI+NYCWzLzWYDMfHG8nbY7BCbysNnZ\nwIsR8cXmdMmGiJg3rVVO3kQfpvsfwH+m856zOK6HBSPiXwDnA49OeWWTtwR4pmV9P289EI5u8+wY\nbWaiiYyt1ceAv5nSiuo17vgi4nTgssz8UyZwt+IMM5Hv71xgUUQ8FBEDEXHleDuteovouGp42GwO\ncAFwXWY+HhG3MvI3i26qu9bJqDq+iPht4IXMfDIiGsywH8y6HhaMiAWM/Pb1qeYZgWawiHgfcDVw\nUbtrqdmtjExdvm5G/f9Wg9ePl+8HuoEdEbEjM/ce6wNTKjM/cLRtzb8sujj/6WGzsU7P9gPPZObj\nzfWv8OYvsa1qGN+/Aj4UEb8FzANOjoh7M/OjU1TycalhfETEHEa+t42ZeaxnSWaCZ4EzW9bf3nxv\ndJszxmkzE01kbETEu4ANwMrM7KS/+TWR8f0G0BcRAZwKfDAihjKzE27ImMj49gMvZuZrwGsR8ffA\nMkauJYyp3dNBrz9sBkd52Kw53fBMRJzbfOsS4AfTUl11Exnff8vMMzPzl4A1wDdnSgBMwLjja7oL\n+EFmfnY6iqpoAFgaEWdFxFxGvpPRB4htwEcBImIF8Mrr02Iz3Lhji4gzgS3AlZn5dBtqrGLc8WXm\nLzVfZzPyi8kfdEgAwMR+NrcCF0XE7IiYz8iNCzuPudc2X+1eBDzAyB0j9wH/rPn+acDXW9ota/4H\neBL4C5p3L8z010TH19L+Yjrr7qBxx8fImc5w87t7AvgOI79htr3+Y4xrZXNMe4B1zfeuBT7e0uZz\njPx29V3ggnbXXNfYgC8AP21+T08Aj7W75rq/u5a2d9FBdwdNdHzAf2LkDqGngE+Mt08fFpOkgrV7\nOkiS1EaGgCQVzBCQpIIZApJUMENAkgpmCEhSwQwBSSqYISBJBfv/kpDFbxs7CnQAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fdc3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xs,ys,s=300,marker=\"*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Robot:\n",
    "    def __init__(self,x,y,rad):\n",
    "        self.actual_poses = [np.array([x,y,rad])]\n",
    "        self.guess_poses = [np.array([x,y,rad])]\n",
    "        random.seed()\n",
    "        \n",
    "    def getX(self):             return self.actual_poses[-1][0]\n",
    "    def getY(self):             return self.actual_poses[-1][1]\n",
    "    def getTheta(self):     return self.actual_poses[-1][2]\n",
    "    \n",
    "    def getActualXs(self):           return [e[0] for e in self.actual_poses]\n",
    "    def getActualYs(self):           return [e[1] for e in self.actual_poses]\n",
    "    def getActualThetas(self):   return [e[2] for e in self.actual_poses]\n",
    "    def getActualDXs(self):           return [math.cos(e[2]) for e in self.actual_poses]\n",
    "    def getActualDYs(self):           return [math.sin(e[2]) for e in self.actual_poses]\n",
    "    \n",
    "    def getGuessXs(self):           return [e[0] for e in self.guess_poses]\n",
    "    def getGuessYs(self):           return [e[1] for e in self.guess_poses]\n",
    "    def getGuessThetas(self):   return [e[2] for e in self.guess_poses]\n",
    "    def getGuessDXs(self):   return [math.cos(e[2]) for e in self.guess_poses]\n",
    "    def getGuessDYs(self):   return [math.sin(e[2]) for e in self.guess_poses]\n",
    "    \n",
    "    def move(self,fw,rot):\n",
    "        actual_fw = random.gauss(fw,fw/10)    #10% noise\n",
    "        actual_rot = random.gauss(rot,rot/10) #10% noise\n",
    "        \n",
    "        p = self.actual_poses[-1]\n",
    "        px, py, pt = p[0],p[1],p[2]\n",
    "        x = px + actual_fw * math.cos(pt)\n",
    "        y = py + actual_fw * math.sin(pt)\n",
    "        t = pt + actual_rot\n",
    "        self.actual_poses.append(np.array([x,y,t]))\n",
    "        \n",
    "        g = self.guess_poses[-1]\n",
    "        gx, gy, gt = g[0],g[1],g[2]\n",
    "        x = gx + fw * math.cos(gt)\n",
    "        y = gy + fw * math.sin(gt)\n",
    "        t = gt + rot\n",
    "        self.guess_poses.append(np.array([x,y,t]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "robot = Robot(0,0,0)\n",
    "\n",
    "for i in range(10):\n",
    "    robot.move(0.3,3.14/6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x110e538d0>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pl6+++kpERMaPHy+RkZHy0UcfSX5+vkyePFlatGgh+fn5kpeXJ61atZIXXnhB8vLyZMmS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\nSikVwjQJKKVUCNMkoJRSIez/AW1gIjo5gx+3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110eaeb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xs,ys,s=300,marker=\"*\",label=\"landmarks\",color=\"orange\")\n",
    "plt.quiver(robot.getActualXs(),robot.getActualYs(),robot.getActualDXs(),robot.getActualDYs(),\n",
    "           color=\"red\",label=\"actual robot motion\")\n",
    "plt.quiver(robot.getGuessXs(),robot.getGuessYs(),robot.getGuessDXs(),robot.getGuessDYs(),\n",
    "           color=\"blue\",label=\"robot guess\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}