a_star_pathfinding.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "a*-pathfinding.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5W3j0roePah",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import HTML\n",
        "import matplotlib.animation as animation"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KezxxFQwexP7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def distance(pos1,pos2):\n",
        "    y1,x1 = pos1\n",
        "    y2,x2 = pos2\n",
        "    return abs(y1 - y2) + abs(x1 - x2)\n",
        "\n",
        "def G_cost(start,coord):\n",
        "    return distance(start,coord)\n",
        "\n",
        "def H_cost(end,coord):\n",
        "    return distance(end,coord)\n",
        "\n",
        "def F_cost(start,end,coord):\n",
        "    return G_cost(start,coord) + H_cost(end,coord)\n",
        "\n",
        "def neighbors(coord,board):\n",
        "    y,x = coord\n",
        "    n = [(y-1,x),(y,x-1),(y,x+1),(y+1,x)]\n",
        "    neighbors = []\n",
        "    for c in n:\n",
        "        y,x = c\n",
        "        if 0 <= y < len(board):\n",
        "            if 0 <= x < len(board[y]):\n",
        "                neighbors.append(c)\n",
        "    return neighbors\n",
        "\n",
        "def get_optimal(start,end,open_):\n",
        "    lowest_f_cost = None\n",
        "    c = None \n",
        "    for coord in open_:\n",
        "        f_cost = F_cost(start,end,coord)\n",
        "        if lowest_f_cost == None or f_cost < lowest_f_cost:\n",
        "            c = coord\n",
        "            lowest_f_cost = f_cost\n",
        "    return c\n",
        "\n",
        "def explore(start,end,board):\n",
        "    parents = {}\n",
        "    open_ = []\n",
        "    closed_ = []\n",
        "    open_.append(start)\n",
        "\n",
        "    while True:\n",
        "        c = get_optimal(start,end,open_)\n",
        "        open_.remove(c)\n",
        "        closed_.append(c)\n",
        "        if c == end:\n",
        "            path = trace_back(start,end,parents)\n",
        "            return (path, closed_)\n",
        "        for coord in neighbors(c,board):\n",
        "            if board[coord] == -1 or coord in closed_:\n",
        "                continue\n",
        "            parents[coord] = c\n",
        "            if coord not in open_:\n",
        "                open_.append(coord)\n",
        "\n",
        "def trace_back(start,end,parents):\n",
        "    path = []\n",
        "    path.append(end)\n",
        "    while True:\n",
        "        c = path[-1]\n",
        "        if c == start:\n",
        "            return path\n",
        "        parent = parents[c]\n",
        "        path.append(parent)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8AL26DfieZ31",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "board = np.zeros((15,15))\n",
        "\n",
        "# big obstacle\n",
        "board[5,5] = -1\n",
        "board[4,5] = -1\n",
        "board[3,5] = -1\n",
        "board[2,5] = -1\n",
        "board[1,5] = -1\n",
        "board[0,5] = -1\n",
        "board[2,6] = -1\n",
        "board[2,7] = -1\n",
        "\n",
        "start = (0,0)\n",
        "end = (2,14)\n",
        "\n",
        "path, steps = explore(start,end,board)\n",
        "list_ = steps + path"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUqes3kermaC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fig,ax = plt.subplots()\n",
        "def animate(i):\n",
        "    if i >= len(list_):\n",
        "        return\n",
        "    ax.clear()\n",
        "    coord = list_[i]\n",
        "    board[coord] = i\n",
        "    ax.scatter(x=start[1],y=start[0],c='green')\n",
        "    ax.scatter(x=end[1],y=end[0],c='red')\n",
        "    ax.imshow(board)\n",
        "\n",
        "ani = animation.FuncAnimation(fig,animate,interval=100)\n",
        "plt.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cLwN4e-pQ0Xw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0c77155b-cf69-4153-d156-cb0b1476fcd2"
      },
      "source": [
        "plt.imshow(board)\n",
        "plt.scatter(x=start[1],y=start[0],c='green')\n",
        "plt.scatter(x=end[1],y=end[0],c='red')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f079712f1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSjRqekL7PUw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "37a4f192-afb2-4b36-c514-e2b1a46e29ee"
      },
      "source": [
        "HTML(ani.to_html5_video())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "AAAAHWRhdGEAAAABAAAAAExhdmY1Ny44My4xMDA=\n",
              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    }
  ]
}