Receding-Horizon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Receding Horizon\n",
    "\n",
    "This notebook is your playground to pull together techniques from the previous lessons! A solution here can be built from previous solutions (more or less) so we will offer no solution notebook this time.\n",
    "\n",
    "Here's a suggested approach:\n",
    "\n",
    "1. Load the colliders data\n",
    "2. Discretize your search space into a grid or graph\n",
    "3. Define a start and goal location\n",
    "4. Find a coarse 2D plan from start to goal\n",
    "5. Choose a location along that plan and discretize\n",
    "   a local volume around that location (for example, you\n",
    "   might try a 40x40 m area that is 10 m high discretized\n",
    "   into 1m^3 voxels)\n",
    "6. Define your goal in the local volume to a a node or voxel\n",
    "   at the edge of the volume in the direction of the next\n",
    "   waypoint in your coarse global plan.\n",
    "7. Plan a path through your 3D grid or graph to that node\n",
    "   or voxel at the edge of the local volume.  \n",
    "\n",
    "We'll import some of the routines from previous exercises that you might find useful here.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sampling'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-a1a7c905e829>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mplanning\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0ma_star\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# Random sampling routine\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msampling\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSampler\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sampling'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Grid creation routine\n",
    "from grid import create_grid\n",
    "# Voxel map creation routine\n",
    "from voxmap import create_voxmap\n",
    "# 2D A* planning routine (can you convert to 3D??)\n",
    "from planning import a_star\n",
    "# Random sampling routine\n",
    "from sampling import Sampler\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = 14, 14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-310.2389   -439.2315     85.5         5.          5.         85.5     ]\n",
      " [-300.2389   -439.2315     85.5         5.          5.         85.5     ]\n",
      " [-290.2389   -439.2315     85.5         5.          5.         85.5     ]\n",
      " ...\n",
      " [ 257.8061    425.1645      1.75852     1.292725    1.292725    1.944791]\n",
      " [ 293.9967    368.3391      3.557666    1.129456    1.129456    3.667319]\n",
      " [ 281.5162    354.4156      4.999351    1.053772    1.053772    4.950246]]\n"
     ]
    }
   ],
   "source": [
    "# This is the same obstacle data from the previous lesson.\n",
    "filename = 'colliders.csv'\n",
    "data = np.loadtxt(filename, delimiter=',', dtype='Float64', skiprows=2)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'create_grid' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-b57cf7cfed05>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mflight_altitude\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0msafety_distance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mgrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_grid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflight_altitude\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msafety_distance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'create_grid' is not defined"
     ]
    }
   ],
   "source": [
    "flight_altitude = 3\n",
    "safety_distance = 3\n",
    "grid = create_grid(data, flight_altitude, safety_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa8f8213cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "plt.imshow(grid, cmap='Greys', origin='lower')\n",
    "\n",
    "plt.xlabel('NORTH')\n",
    "plt.ylabel('EAST')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}