Coding Task 1/Naive Robot Navigation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Coding Task 1 | Algorithms 2023 | EC31205**\n",
        "#### Submitted by Irsh Vijay | 21EC30025\n",
        "****\n",
        "### **Question 1: Naive Robot Navigation Problem**\n",
        "Please run the following code cells in succession and provide input parameters in the fourth cell.\n",
        "\n",
        "The function is called in the fifth cell.\n",
        "\n",
        "**For Input:**\n",
        "```\n",
        "maze = [[1, 1, 1, 1, 1],\n",
        "[1, 0, 0, 0, 1],\n",
        "[1, 0, 1, 1, 1],\n",
        "[0, 1, 1, 1, 1]]\n",
        "\n",
        "start = (0, 0)\n",
        "\n",
        "end = (3, 2)\n",
        "\n",
        "all_direction = False\n",
        "```\n",
        "\n",
        "**Expected Output:**<br><br>\n",
        "![expected-maze-output.png](data:image/png;base64,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)\n",
        "\n",
        "(True,\n",
        " [(0, 0),\n",
        "  (0, 1),\n",
        "  (0, 2),\n",
        "  (0, 3),\n",
        "  (0, 4),\n",
        "  (1, 4),\n",
        "  (2, 4),\n",
        "  (3, 4),\n",
        "  (3, 3),\n",
        "  (3, 2)])\n",
        "\n",
        "  **On a bit more complex maze:**<br>\n",
        "  ![maze.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "v3xW1M3u8vtV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def check_valid_move(maze, row, col, all_direction):\n",
        "  return 0 <= row < len(maze) and 0 <= col < len(maze[0]) and maze[row][col] == 1"
      ],
      "metadata": {
        "id": "P14v-0lR1yLj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# A plot maze function to visualise the path taken\n",
        "\n",
        "def plot_maze(maze, start = False, end = False, path = False):\n",
        "  rows = len(maze)\n",
        "  cols = len(maze[0])\n",
        "\n",
        "  grid = [[(255, 255, 255) if maze[row][col] == 1 else (0, 0, 0) for col in range(cols)] for row in range(rows)]\n",
        "\n",
        "  if start:\n",
        "    grid[start[0]][start[1]] = (160, 4, 252)\n",
        "  if end:\n",
        "    grid[end[0]][end[1]] = (160, 4, 4)\n",
        "  if path:\n",
        "    for path_row, path_col in path:\n",
        "      if(grid[path_row][path_col] != (160, 4, 252) and grid[path_row][path_col] != (160, 4, 4)):\n",
        "        grid[path_row][path_col] = (100, 255, 100)\n",
        "\n",
        "  plt.imshow(grid)\n",
        "  plt.xticks(range(cols))\n",
        "  plt.yticks(range(rows))\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "p1JJFgJK3c6b"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def naive_robot_navigation(maze, start, end, all_direction):\n",
        "  allowed_directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n",
        "  if(all_direction):\n",
        "    allowed_directions.extend([(1, 1), (-1, 1), (1, -1), (-1, -1)])\n",
        "\n",
        "  # allowed_directions contain possible move sets from a particular point\n",
        "\n",
        "  visited = set()\n",
        "  queue = [(start, [])]\n",
        "\n",
        "  while queue:\n",
        "    (row, col), path = queue.pop(0)\n",
        "\n",
        "    if (row, col) == end:\n",
        "      plot_maze(maze, start, end, path + [(row, col)])\n",
        "      return True, path + [(row, col)]\n",
        "\n",
        "    # Breadth First Search Implementation\n",
        "\n",
        "    for dr, dc in allowed_directions:\n",
        "      new_row, new_col = row + dr, col + dc\n",
        "\n",
        "      if check_valid_move(maze, new_row, new_col, all_direction) and (new_row, new_col) not in visited:\n",
        "        visited.add((new_row, new_col))\n",
        "        queue.append(((new_row, new_col), path + [(row, col)]))\n",
        "\n",
        "  plot_maze(maze, start, end)\n",
        "  return False, []"
      ],
      "metadata": {
        "id": "6j8MZnI_1Kmo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "maze = [[1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1],\n",
        "[1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0],\n",
        "[1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0],\n",
        "[0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1],\n",
        "[1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0],\n",
        "[1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0],\n",
        "[1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1],\n",
        "[1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1],\n",
        "[0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1],\n",
        "[1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1],\n",
        "[0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1],\n",
        "[1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1],\n",
        "[1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0],\n",
        "[1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0],\n",
        "[0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1],\n",
        "[1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1],\n",
        "[1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1],\n",
        "[0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1],\n",
        "[1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0],\n",
        "[1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1],\n",
        "[1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1],\n",
        "[1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1],\n",
        "[1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0],\n",
        "[1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1],\n",
        "[1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1],\n",
        "[1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1],\n",
        "[1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1],\n",
        "[0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1],\n",
        "[1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1],\n",
        "[1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1]]\n",
        "\n",
        "start = (0, 0)\n",
        "\n",
        "end = (11, 0)\n",
        "\n",
        "all_direction = True"
      ],
      "metadata": {
        "id": "1tqV5rwA21tR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "naive_robot_navigation(maze, start, end, all_direction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 795
        },
        "id": "nUAvJv0p3wqe",
        "outputId": "a85a9484-e560-4ce6-8166-c9c543017d3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(True,\n",
              " [(0, 0),\n",
              "  (1, 0),\n",
              "  (2, 0),\n",
              "  (3, 1),\n",
              "  (4, 1),\n",
              "  (5, 2),\n",
              "  (6, 2),\n",
              "  (7, 2),\n",
              "  (8, 3),\n",
              "  (9, 3),\n",
              "  (10, 3),\n",
              "  (11, 3),\n",
              "  (12, 3),\n",
              "  (13, 3),\n",
              "  (14, 3),\n",
              "  (15, 2),\n",
              "  (14, 1),\n",
              "  (13, 1),\n",
              "  (12, 0),\n",
              "  (11, 0)])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    }
  ]
}