Configuration-Space.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confguration Space\n",
    "\n",
    "In this notebook you'll create a configuration space given a map of the world and setting a particular altitude for your drone. You'll read in a `.csv` file containing obstacle data which consists of six columns $x$, $y$, $z$ and $\\delta x$, $\\delta y$, $\\delta z$.\n",
    "\n",
    "You can look at the `.csv` file [here](/edit/colliders.csv). The first line gives the map center coordinates and the file is arranged such that:\n",
    "\n",
    "* $x$ -> NORTH\n",
    "* $y$ -> EAST\n",
    "* $z$ -> ALTITUDE (positive up, note the difference with NED coords)\n",
    "\n",
    "Each $(x, y, z)$ coordinate is the center of an obstacle. $\\delta x$, $\\delta y$, $\\delta z$ are the half widths of the obstacles, meaning for example that an obstacle with $(x = 37, y = 12, z = 8)$ and $(\\delta x = 5, \\delta y = 5, \\delta z = 8)$ is a 10 x 10 m obstacle that is 16 m high and is centered at the point $(x, y) = (37, 12)$ at a height of 8 m.\n",
    "\n",
    "Given a map like this, the free space in the $(x, y)$ plane is a function of altitude, and you can plan a path around an obstacle, or simply fly over it! You'll extend each obstacle by a safety margin to create the equivalent of a 3 dimensional configuration space. \n",
    "\n",
    "Your task is to extract a 2D grid map at 1 metre resolution of your configuration space for a particular altitude, where each value is assigned either a 0 or 1 representing feasible or infeasible (obstacle) spaces respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The end result should look something like this ... (colours aren't important)\n",
    "\n",
    "![title](grid_map.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [12, 12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the csv file which contains the coordinates of the obstacles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "filename = 'colliders.csv'\n",
    "# Read in the data skipping the first two lines.  \n",
    "# Note: the first line contains the latitude and longitude of map center\n",
    "# Where is this??\n",
    "data = np.loadtxt(filename,delimiter=',',dtype='Float64',skiprows=2)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Static drone altitude (metres)\n",
    "drone_altitude = 5\n",
    "\n",
    "# Minimum distance required to stay away from an obstacle (metres)\n",
    "# Think of this as padding around the obstacles.\n",
    "safe_distance = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The given function will take the data from the file describing the obstacles city and will return a 2D grid representation showing open and closed spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.floor(-1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_grid(data, drone_altitude, safety_distance):\n",
    "    \"\"\"\n",
    "    Returns a grid representation of a 2D configuration space\n",
    "    based on given obstacle data, drone altitude and safety distance\n",
    "    arguments.\n",
    "    \"\"\"\n",
    "\n",
    "    # minimum and maximum north coordinates\n",
    "    north_min = np.floor(np.amin(data[:, 0] - data[:, 3]))\n",
    "    north_max = np.ceil(np.amax(data[:, 0] + data[:, 3]))\n",
    "\n",
    "    # minimum and maximum east coordinates\n",
    "    east_min = np.floor(np.amin(data[:, 1] - data[:, 4]))\n",
    "    east_max = np.ceil(np.amax(data[:, 1] + data[:, 4]))\n",
    "\n",
    "    # given the minimum and maximum coordinates we can\n",
    "    # calculate the size of the grid.\n",
    "    north_size = int(np.ceil(north_max - north_min))\n",
    "    east_size = int(np.ceil(east_max - east_min))\n",
    "    # Initialize an empty grid\n",
    "    grid = np.zeros((north_size, east_size))\n",
    "    # Center offset for grid\n",
    "    north_min_center = np.min(data[:, 0])\n",
    "    east_min_center = np.min(data[:, 1])\n",
    "    # Populate the grid with obstacles\n",
    "    for i in range(data.shape[0]):\n",
    "        north, east, alt, d_north, d_east, d_alt = data[i, :]\n",
    "        if alt + d_alt + safety_distance > drone_altitude:\n",
    "            obstacles = [\n",
    "                         int(np.clip(north - d_north - safety_distance - north_min,0,north_size-1)),\n",
    "                         int(np.clip(north + d_north + safety_distance - north_min,0,north_size-1)),\n",
    "                         int(np.clip(east - d_east - safety_distance - east_min,0,east_size-1)),\n",
    "                         int(np.clip(east + d_east + safety_distance - east_min,0,east_size-1 ))\n",
    "                        ]\n",
    "            \n",
    "            grid[obstacles[0]:obstacles[1]+1, obstacles[2]:obstacles[3]+1] = 1\n",
    "        # TODO: Determine which cells contain obstacles\n",
    "        # and set them to 1.\n",
    "        #\n",
    "        # Example:\n",
    "        #\n",
    "        #    grid[north_coordinate, east_coordinate] = 1\n",
    "\n",
    "    return grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = create_grid(data, drone_altitude, safe_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa96ec58208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# equivalent to\n",
    "# plt.imshow(np.flip(grid, 0))\n",
    "# NOTE: we're placing the origin in the lower lefthand corner here\n",
    "# so that north is up, if you didn't do this north would be positive down\n",
    "plt.imshow(grid, origin='lower') \n",
    "\n",
    "plt.xlabel('EAST')\n",
    "plt.ylabel('NORTH')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Play around with the `drone_altitude` and `safe_distance` values to get a feel for how it changes the map."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[solution](/notebooks/Configuration-Space-Solution.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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
}