d.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TensorFlow with GPU",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/sharathsrini/Extended-Kalman-Filter-for-Sensor-Fusion/blob/master/d.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "2CfbDAqQFYzj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from math import inf\n",
        "\n",
        "def dijkstra(grid, src, dest):\n",
        "    Q = grid.get_nodes()\n",
        "    dist = {node : inf for node in Q}\n",
        "    prev = {node : None for node in Q}\n",
        "\n",
        "    dist[src] = 0\n",
        "\n",
        "    while Q:\n",
        "        u = min(Q, key = dist.__getitem__)\n",
        "        Q.remove(u)\n",
        "\n",
        "        for v in grid.get_adjacent(u):\n",
        "            alt = dist[u] + 1\n",
        "            if alt < dist[v]:\n",
        "                dist[v] = alt\n",
        "                prev[v] = u\n",
        "\n",
        "    path = [dest]\n",
        "    cur = dest\n",
        "\n",
        "    while prev[cur]:\n",
        "        path.append(prev[cur])\n",
        "        cur = prev[cur]\n",
        "\n",
        "    return list(reversed(path))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7r1pNhV6FmeF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from itertools import product\n",
        "\n",
        "class Grid(object):\n",
        "\n",
        "    def __init__(self, i, j, obstacles = []):\n",
        "        self.i = i\n",
        "        self.j = j\n",
        "        self.grid = [[0] * i for _ in range(j)]\n",
        "\n",
        "        self.obstacles = obstacles\n",
        "\n",
        "        for obstacle in obstacles:\n",
        "            self.grid[obstacle[0]][obstacle[1]] = 1\n",
        "\n",
        "    def __str__(self):\n",
        "        return \"\\n\".join(\"\".join(x) for x in self.char_map())\n",
        "\n",
        "    def __repr__(self):\n",
        "        return \"{} by {} grid:\\n\".format(self.i, self.j) + self.__str__()\n",
        "\n",
        "    def char_map(self):\n",
        "        arr = [[\"0\"] * self.j for _ in range(self.i)]\n",
        "\n",
        "        for obstacle in self.obstacles:\n",
        "            arr[obstacle[0]][obstacle[1]] = \"1\"\n",
        "\n",
        "        return arr\n",
        "\n",
        "    def get_adjacent(self, node):\n",
        "        i = node[0]\n",
        "        j = node[1]\n",
        "\n",
        "        if i < 0 or i >= self.i or j < 0 or j >= self.j:\n",
        "            raise Exception(\"Out of bounds\")\n",
        "\n",
        "        if self.grid[i][j] != 0:\n",
        "            return []\n",
        "\n",
        "        adjacent = []\n",
        "\n",
        "        for del_pos in [(1, 0), (-1, 0), (0, 1), (0, -1)]:\n",
        "            if 0 <= i + del_pos[0] < self.j and 0 <= j + del_pos[1] < self.j:\n",
        "                if self.grid[i + del_pos[0]][j + del_pos[1]] == 0:\n",
        "                    adjacent.append((i + del_pos[0], j + del_pos[1]))\n",
        "\n",
        "        return adjacent\n",
        "\n",
        "    def get_nodes(self):\n",
        "        return [(i, j) for i, j in product(range(self.i), range(self.j)) if (i, j) not in self.obstacles]\n",
        "\n",
        "    def plot_path(self, path):\n",
        "        arr = self.char_map()\n",
        "        for node in path:\n",
        "            arr[node[0]][node[1]] = \".\"\n",
        "\n",
        "        return \"\\n\".join(\"\".join(x) for x in arr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "z7UaKd7HFoig",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "35921476-1030-4006-dfa7-2b6abd6aa0cb"
      },
      "cell_type": "code",
      "source": [
        "g = Grid(20, 20, obstacles = [(2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (4, 2), (4, 3), (4, 4), (4, 5), (4, 6)])\n",
        "src = (0, 0)\n",
        "dest = (20, 19)\n",
        "\n",
        "print(\"Grid visualization: \")\n",
        "print(g)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Grid visualization: \n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "11111000000000000000\n",
            "00000000000000000000\n",
            "00111110000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n",
            "00000000000000000000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "h1Z-dOIzGE7n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 21014
        },
        "outputId": "99bf4630-a50a-4011-ba4c-844880b1cf40"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "show_animation = True\n",
        "\n",
        "\n",
        "class Node:\n",
        "\n",
        "    def __init__(self, x, y, cost, pind):\n",
        "        self.x = x\n",
        "        self.y = y\n",
        "        self.cost = cost\n",
        "        self.pind = pind\n",
        "\n",
        "    def __str__(self):\n",
        "        return str(self.x) + \",\" + str(self.y) + \",\" + str(self.cost) + \",\" + str(self.pind)\n",
        "\n",
        "\n",
        "def dijkstra_planning(sx, sy, gx, gy, ox, oy, reso, rr):\n",
        "    \"\"\"\n",
        "    gx: goal x position [m]\n",
        "    gx: goal x position [m]\n",
        "    ox: x position list of Obstacles [m]\n",
        "    oy: y position list of Obstacles [m]\n",
        "    reso: grid resolution [m]\n",
        "    rr: robot radius[m]\n",
        "    \"\"\"\n",
        "\n",
        "    nstart = Node(round(sx / reso), round(sy / reso), 0.0, -1)\n",
        "    ngoal = Node(round(gx / reso), round(gy / reso), 0.0, -1)\n",
        "    ox = [iox / reso for iox in ox]\n",
        "    oy = [ioy / reso for ioy in oy]\n",
        "\n",
        "    obmap, minx, miny, maxx, maxy, xw, yw = calc_obstacle_map(ox, oy, reso, rr)\n",
        "\n",
        "    motion = get_motion_model()\n",
        "\n",
        "    openset, closedset = dict(), dict()\n",
        "    openset[calc_index(nstart, xw, minx, miny)] = nstart\n",
        "\n",
        "    while 1:\n",
        "        c_id = min(openset, key=lambda o: openset[o].cost)\n",
        "        current = openset[c_id]\n",
        "        #  print(\"current\", current)\n",
        "\n",
        "        # show graph\n",
        "        if show_animation:\n",
        "            plt.plot(current.x * reso, current.y * reso, \"xc\")\n",
        "            if len(closedset.keys()) % 10 == 0:\n",
        "                plt.pause(0.001)\n",
        "\n",
        "        if current.x == ngoal.x and current.y == ngoal.y:\n",
        "            print(\"Find goal\")\n",
        "            ngoal.pind = current.pind\n",
        "            ngoal.cost = current.cost\n",
        "            break\n",
        "\n",
        "        # Remove the item from the open set\n",
        "        del openset[c_id]\n",
        "        # Add it to the closed set\n",
        "        closedset[c_id] = current\n",
        "\n",
        "        # expand search grid based on motion model\n",
        "        for i in range(len(motion)):\n",
        "            node = Node(current.x + motion[i][0], current.y + motion[i][1],\n",
        "                        current.cost + motion[i][2], c_id)\n",
        "            n_id = calc_index(node, xw, minx, miny)\n",
        "\n",
        "            if not verify_node(node, obmap, minx, miny, maxx, maxy):\n",
        "                continue\n",
        "\n",
        "            if n_id in closedset:\n",
        "                continue\n",
        "            # Otherwise if it is already in the open set\n",
        "            if n_id in openset:\n",
        "                if openset[n_id].cost > node.cost:\n",
        "                    openset[n_id].cost = node.cost\n",
        "                    openset[n_id].pind = c_id\n",
        "            else:\n",
        "                openset[n_id] = node\n",
        "\n",
        "    rx, ry = calc_final_path(ngoal, closedset, reso)\n",
        "\n",
        "    return rx, ry\n",
        "\n",
        "\n",
        "def calc_final_path(ngoal, closedset, reso):\n",
        "    # generate final course\n",
        "    rx, ry = [ngoal.x * reso], [ngoal.y * reso]\n",
        "    pind = ngoal.pind\n",
        "    while pind != -1:\n",
        "        n = closedset[pind]\n",
        "        rx.append(n.x * reso)\n",
        "        ry.append(n.y * reso)\n",
        "        pind = n.pind\n",
        "\n",
        "    return rx, ry\n",
        "\n",
        "\n",
        "def verify_node(node, obmap, minx, miny, maxx, maxy):\n",
        "\n",
        "    if obmap[node.x][node.y]:\n",
        "        return False\n",
        "\n",
        "    if node.x < minx:\n",
        "        return False\n",
        "    elif node.y < miny:\n",
        "        return False\n",
        "    elif node.x > maxx:\n",
        "        return False\n",
        "    elif node.y > maxy:\n",
        "        return False\n",
        "\n",
        "    return True\n",
        "\n",
        "\n",
        "def calc_obstacle_map(ox, oy, reso, vr):\n",
        "\n",
        "    minx = 0\n",
        "    miny = 0\n",
        "    maxx = 40\n",
        "    maxy = 40\n",
        "    #  print(\"minx:\", minx)\n",
        "    #  print(\"miny:\", miny)\n",
        "    #  print(\"maxx:\", maxx)\n",
        "    #  print(\"maxy:\", maxy)\n",
        "\n",
        "    xwidth = round(maxx - minx)\n",
        "    ywidth = round(maxy - miny)\n",
        "    #  print(\"xwidth:\", xwidth)\n",
        "    #  print(\"ywidth:\", ywidth)\n",
        "\n",
        "    # obstacle map generation\n",
        "    obmap = [[False for i in range(xwidth)] for i in range(ywidth)]\n",
        "    for ix in range(xwidth):\n",
        "        x = ix + minx\n",
        "        for iy in range(ywidth):\n",
        "            y = iy + miny\n",
        "            #  print(x, y)\n",
        "            for iox, ioy in zip(ox, oy):\n",
        "                d = math.sqrt((iox - x)**2 + (ioy - y)**2)\n",
        "                if d <= vr / reso:\n",
        "                    obmap[ix][iy] = True\n",
        "                    break\n",
        "\n",
        "    return obmap, minx, miny, maxx, maxy, xwidth, ywidth\n",
        "\n",
        "\n",
        "def calc_index(node, xwidth, xmin, ymin):\n",
        "    return (node.y - ymin) * xwidth + (node.x - xmin)\n",
        "\n",
        "\n",
        "def get_motion_model():\n",
        "    # dx, dy, cost\n",
        "    motion = [[1, 0, 1],\n",
        "              [0, 1, 1],\n",
        "              [-1, 0, 1],\n",
        "              [0, -1, 1],\n",
        "              [-1, -1, math.sqrt(2)],\n",
        "              [-1, 1, math.sqrt(2)],\n",
        "              [1, -1, math.sqrt(2)],\n",
        "              [1, 1, math.sqrt(2)]]\n",
        "\n",
        "    return motion\n",
        "\n",
        "\n",
        "def main():\n",
        "    \n",
        "\n",
        "    # start and goal position\n",
        "    sx = 10.0  # [m]\n",
        "    sy = 10.0  # [m]\n",
        "    gx = 50.0  # [m]\n",
        "    gy = 50.0  # [m]\n",
        "    grid_size = 1.0  # [m]\n",
        "    robot_size = 1.0  # [m]\n",
        "\n",
        "    ox = []\n",
        "    oy = []\n",
        "\n",
        "    for i in range(60):\n",
        "        ox.append(i)\n",
        "        oy.append(0.0)\n",
        "    for i in range(60):\n",
        "        ox.append(60.0)\n",
        "        oy.append(i)\n",
        "    for i in range(61):\n",
        "        ox.append(i)\n",
        "        oy.append(60.0)\n",
        "    for i in range(61):\n",
        "        ox.append(0.0)\n",
        "        oy.append(i)\n",
        "    for i in range(40):\n",
        "        ox.append(20.0)\n",
        "        oy.append(i)\n",
        "    for i in range(40):\n",
        "        ox.append(40.0)\n",
        "        oy.append(60.0 - i)\n",
        "\n",
        "    if show_animation:\n",
        "        plt.plot(ox, oy, \".k\")\n",
        "        plt.plot(sx, sy, \"xr\")\n",
        "        plt.plot(gx, gy, \"xb\")\n",
        "        plt.grid(True)\n",
        "        plt.axis(\"equal\")\n",
        "\n",
        "    rx, ry = dijkstra_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_size)\n",
        "\n",
        "    if show_animation:\n",
        "        plt.plot(rx, ry, \"-r\")\n",
        "        plt.show()\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    main()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6li1bpvPnz6u2tlbr169XIpHwPcxo5HI5ffOb39S9996rWbNmkbUDu3fv1pYt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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c250b70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c2fba58>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t2xb4ef369ers7AxZzgAA4Jqw7jmfOXNG+/fvlyS99957Wr58uWJiri01PDysXbt26fLl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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c2eae48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bf81e80>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522e467b38>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bfcc898>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522e4d77f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c312e48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c1f10b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bf13080>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522be49ef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522e594710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f5240fcaeb8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pf84f//jHVo90zfD59OMmJyfz2GOP5fbbb//erVb+bffu3dmxY0erx2h7U1NTl18e+/nP\nf56XX3553s4lvldp+fLlue2229JoNHLTTTdlyZIl+fzzz1s9Vtv58MMPs2bNmiTJrbfemjNnzmRy\ncrLFU7Wvrq6uXLx4MUly+vTp791C5H/t2LEjfX192bZtW6tHaUunT5/Oxx9/nEcffTSbNm3KmTNn\nMjg42Oqx2tKNN96YX/ziF0mSNWvW5F//+te8nUt8r9KaNWvy3nvv5bvvvsv4+HgmJia8njmNvr6+\nfPTRR0n+fWtnyZIl6ejoaPFU7euOO+7IkSNHkiRvvPFG1q5d2+KJ2tPhw4ezaNGiPPzww60epW2t\nWLEib775Zg4ePJiDBw+mt7f3ez8Fzb/98pe/vPybGP/85z+zatWqeTuX/9VoDvzlL3+5/NOov/vd\n7+b1Rfpr1YULF7Jz586cPXs23377bbZv3+4W4X87fvx49uzZk9HR0TQajaxYsSLPPPNMnnjiiVy6\ndCkrV67M7t27s2jRolaP2lLT7ens2bO5/vrrs3Tp0iTJzTffnF27drV20Babbk8vvPBCbrjhhiTJ\nXXfdlbfffrvFU7beD33dPf300xkbG0tXV1f27NmTG2+8cV7OL74AUMxtZwAoJr4AUEx8AaCY+AJA\nMfEFgGLiCwDFxBcAiokvABT7L0zFjlZkfOxVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c33fa58>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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fzO2335677747X3/9dYsXNpb4nqZnn302F110UatnNM3IyEieeeaZbN68ORs2bMjrr7/e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\nAAr7P3UafKGTRmFfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c318128>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bebbc50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c318be0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c3a02e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c318c88>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bf77ef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c0c1cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c13b6d8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522e4d3be0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c04d198>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522e4e1470>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c01a780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522e4dba90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c01c7b8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bf2c1d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522e486550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bfa18d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c03cb70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c1f6be0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c0edbe0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c0e07f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c02d828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c2d0438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c2e9940>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c2c3cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bf22a90>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bfde2e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c1b1780>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bedb5f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522be1a7f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c293828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522e5b0b38>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bfded68>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bffcef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Rvi9JbneyXK7EWMeJiceT0qP7xRNZ7HFKDoksFjklh0SW7oipnCsqKlRaWqqtW7cqJSVF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\niwZgxgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c044dd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c075cf8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LQ7pntJDFHqfkkMhikVNySGSJtF9/Ih5rHzlyRGVlZZI+P67u6OjQrFmzVFlZKUmqqqrS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\n/ClTnAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c2fbc50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522be9aef0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c2dcb70>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c09ff98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c037320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522be0a048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c1f0748>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522be437f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f524aa535f8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c2ea860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2OGUPiV0scsoeErv0RdA4V1ZWKjExUSNHjlRqaqp8Pp/Cw8M1bdo0SVJ6erp27drV5Zzk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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522bf6aa20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-c5075ee239be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    213\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__name__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'__main__'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 215\u001b[0;31m     \u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-23-c5075ee239be>\u001b[0m in \u001b[0;36mmain\u001b[0;34m()\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"equal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m     \u001b[0mrx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdijkstra_planning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrobot_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mshow_animation\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-23-c5075ee239be>\u001b[0m in \u001b[0;36mdijkstra_planning\u001b[0;34m(sx, sy, gx, gy, ox, oy, reso, rr)\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mn_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalc_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mverify_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-23-c5075ee239be>\u001b[0m in \u001b[0;36mverify_node\u001b[0;34m(node, obmap, minx, miny, maxx, maxy)\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mverify_node\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mminy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mobmap\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pn77jcvZtKSkp8ng8GjdunPr6+uTz+QI+a+7o6Pp6GxjEbh8ij+dauMcIOfa0Fva0FvYc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\nOAMAYJj/AMEK00TQ9A6OAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f522c08e2b0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}