08. A*: Real-World Map

A*: Real-World Map

Now it’s time to apply the A* algorithm that you’ve coded earlier to a real-world map.

If you recall, this map is the one you generated with the occupancy grid mapping algorithm using both sonar and odometry data. Our aim is now to find the shortest path for the robot to cross from start o to goal * position.

Given

Map(300x150): The map data stored in the map.txt file in form of log odds values. As a reminder, here's how you should interpret these numbers:

  • A cell is considered unknown if its log odds value is equal to 0.
  • A cell is considered as occupied if its log odds is larger than 0.
  • A cell is considered as free if its log odds value is less than 0.

Grid(300x150): The log odds values converted to 0’s and 1’s where 0 represents the free space and 1 represents the occupied or unknown space.

Robot Start position: 230,145

Robot Goal Position: 60,50

Direction of Movement: Up(-1,0) - left(0,-1) - down(1,0) - right(0,1)

Movement Arrows: Up(^) - left(<) - down(v) - right(>)

Cost of Movement: 1

Heuristic Vector: Manhattan

If you scroll down to the code, you will notice that I added three new functions to the Map class. I coded a GetMap function which reads the map.txt log odds values and assign them the map variable. You will code the MapToGrid function in order to convert the log odds values to 0’s and 1’s. These 0 and 1 values will be assigned to the grid variable. And finally, the GeneratedHeuristic function is another function that you have to code in order to generate a Manhattan-based heuristic vector by computing the Manhattan distance of each cell with respect to the goal position. As a reminder the Manhattan distance of each cell can be calculated as follow:

x_{d} = x_{goal} - x_{cellposition}
y_{d} = y_{goal} - y_{cellposition}
Manhattan \ distance \ d = \left | x_{d} \right | + \left | y_{d} \right |

A*: Real-World Map

Task Description:

Follow these instructions to generate the shortest path using the A* algorithm:

Task List:

Task Feedback:

Great Job!

Start Quiz:

#include <iostream>
#include <math.h>
#include <vector>
#include <algorithm>
#include <fstream>

using namespace std;

// Map class
class Map {
public:
    const static int mapHeight = /* #### TODO: mapHeight #### */
    const static int mapWidth = /* #### TODO: mapWidth #### */
    vector<vector<double> > map = GetMap();
    vector<vector<int> > grid = MaptoGrid();
    vector<vector<int> > heuristic = GenerateHeuristic();

private:
    // Read the file and get the map
    vector<vector<double> > GetMap()
    {
        vector<vector<double> > mymap(mapHeight, vector<double>(mapWidth));
        ifstream myReadFile;
        myReadFile.open("map.txt");

        while (!myReadFile.eof()) {
            for (int i = 0; i < mapHeight; i++) {
                for (int j = 0; j < mapWidth; j++) {
                    myReadFile >> mymap[i][j];
                }
            }
        }
        return mymap;
    }

    /* #### TODO: Code the MaptoGrid function and convert the map to 1's and 0's #### */
    vector<vector<int> > MaptoGrid()
    {
        vector<vector<int> > grid(mapHeight, vector<int>(mapWidth));
        /* Here's how you interpret the data stored inside map
           0:unkown 
          <0:free 
          >0:occupied
        */
        /* You need to convert these data to 0's and 1's and assigned it to grid where:
           0: Free Space
           1: Occupied + Unkown Space
        */
        return grid;
    }

    /* #### TODO: Generate a Manhttan Heuristic Vector #### */
    vector<vector<int> > GenerateHeuristic()
    {
        int goal[2] = { 60, 50 };
        vector<vector<int> > heuristic(mapHeight, vector<int>(mapWidth));
        // Generate a Manhattan heursitic vector
        return heuristic;
    }
};

// Planner class
class Planner : Map {
public:
    int start[2] = /* #### TODO: start #### */
    int goal[2] = /* #### TODO: goal #### */
    int cost = 1;

    string movements_arrows[4] = { "^", "<", "v", ">" };

    vector<vector<int> > movements{
        { -1, 0 },
        { 0, -1 },
        { 1, 0 },
        { 0, 1 }
    };

    vector<vector<int> > path;
};

// Printing vectors of any type
template <typename T>
void print2DVector(T Vec)
{
    for (int i = 0; i < Vec.size(); ++i) {
        for (int j = 0; j < Vec[0].size(); ++j) {
            cout << Vec[i][j] << ' ';
        }
        cout << endl;
    }
}

Planner search(Map map, Planner planner)
{
    // Create a closed 2 array filled with 0s and first element 1
    vector<vector<int> > closed(map.mapHeight, vector<int>(map.mapWidth));
    closed[planner.start[0]][planner.start[1]] = 1;

    // Create expand array filled with -1
    vector<vector<int> > expand(map.mapHeight, vector<int>(map.mapWidth, -1));

    // Create action array filled with -1
    vector<vector<int> > action(map.mapHeight, vector<int>(map.mapWidth, -1));

    // Defined the quadruplet values
    int x = planner.start[0];
    int y = planner.start[1];
    int g = 0;
    int f = g + map.heuristic[x][y];

    // Store the expansions
    vector<vector<int> > open;
    open.push_back({ f, g, x, y });

    // Flags and Counts
    bool found = false;
    bool resign = false;
    int count = 0;

    int x2;
    int y2;

    // While I am still searching for the goal and the problem is solvable
    while (!found && !resign) {
        // Resign if no values in the open list and you can't expand anymore
        if (open.size() == 0) {
            resign = true;
            cout << "Failed to reach a goal" << endl;
        }
        // Keep expanding
        else {
            // Remove quadruplets from the open list
            sort(open.begin(), open.end());
            reverse(open.begin(), open.end());
            vector<int> next;
            // Stored the poped value into next
            next = open.back();
            open.pop_back();

            x = next[2];
            y = next[3];
            g = next[1];

            // Fill the expand vectors with count
            expand[x][y] = count;
            count += 1;
            

            // Check if we reached the goal:
            if (x == planner.goal[0] && y == planner.goal[1]) {
                found = true;
                //cout << "[" << g << ", " << x << ", " << y << "]" << endl;
            }

            //else expand new elements
            else {
                for (int i = 0; i < planner.movements.size(); i++) {
                    x2 = x + planner.movements[i][0];
                    y2 = y + planner.movements[i][1];
                    if (x2 >= 0 && x2 < map.grid.size() && y2 >= 0 && y2 < map.grid[0].size()) {
                        if (closed[x2][y2] == 0 and map.grid[x2][y2] == 0) {
                            int g2 = g + planner.cost;
                            f = g2 + map.heuristic[x2][y2];
                            open.push_back({ f, g2, x2, y2 });
                            closed[x2][y2] = 1;
                            action[x2][y2] = i;
                        }
                    }
                }
            }
        }
    }

    // Print the expansion List
    //print2DVector(expand);

    // Find the path with robot orientation
    vector<vector<string> > policy(map.mapHeight, vector<string>(map.mapWidth, "-"));

    // Going backward
    x = planner.goal[0];
    y = planner.goal[1];
    policy[x][y] = '*';

    while (x != planner.start[0] or y != planner.start[1]) {
        x2 = x - planner.movements[action[x][y]][0];
        y2 = y - planner.movements[action[x][y]][1];
        // Store the  Path in a vector
        planner.path.push_back({ x2, y2 });
        policy[x2][y2] = planner.movements_arrows[action[x][y]];
        x = x2;
        y = y2;
    }

    // Print the robot path
    //cout << endl;
    //print2DVector(policy);

    return planner;
}

int main()
{
    // Instantiate a planner and map objects
    Map map;
    Planner planner;
    // Generate the shortest Path using the Astar algorithm
    planner = search(map, planner);

    return 0;
}
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0	0	0	0.8	1.2	0.4	-1.6	-1.6	-2.8	-3.6	-4.4	-6	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-5.2	-2.8	-4.8	-9.6	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-13.2	-14.4	-14	-13.6	-14	-13.2	-12.8	-13.6	-14	-14.4	-15.2	-15.6	-17.2	-18	-18	-18	-18.8	-19.6	-20.8	-22	-23.2	-24.4	-25.2	-25.2	-25.2	-25.6	-26.4	-26.8	-26.4	-26	-26.4	-26	-27.2	-28	-28	-26	-22.8	-22.4	-24.4	-22	-21.2	-23.2	-23.2	-20.8	-21.2	-19.6	-21.2	-22	-21.2	-21.2	-19.6	-16.4	-11.2	-10.8	-10.8	-9.6	-9.2	-8.8	-8	-7.6	-7.6	-8.4	-7.2	-4.4	-2.4	-2	-2.8	-2.4	-0.8	-1.11022e-16	0.8	-1.11022e-16	-0.4	-0.4	-1.2	-1.2	-2.8	-2.8	-4	-4	-4.4	-4.4	-5.6	-4.8	-3.6	-3.2	-2.8	-3.6	-2.8	-2	-2	-0.4	-0.4	-1.2	-0.4	-0.4	-1.6	-1.2	-2	-0.8	-0.4	-1.2	-1.11022e-16	0.8	0.8	1.6	3.6	4.4	5.6	4.8	4	3.2	
0	0	0	0.8	0.8	0.4	-1.6	-1.6	-2.8	-3.6	-4.4	-6	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-6	-3.2	-3.2	-9.6	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-14	-14.4	-14	-13.6	-13.6	-12.8	-13.6	-13.6	-14	-14.8	-15.2	-16.4	-17.2	-18	-18	-18	-18	-19.2	-20	-21.2	-22.4	-23.6	-24.4	-24.8	-25.6	-26.4	-26	-26.4	-26	-26.8	-26.4	-27.2	-28.4	-28	-28.4	-26.4	-24.8	-26	-24.8	-23.2	-23.2	-24.4	-25.2	-25.6	-24	-23.6	-21.6	-22	-22.8	-21.2	-19.2	-14	-12.4	-12.8	-10.8	-9.6	-9.6	-10	-9.2	-8.4	-8	-8	-8.4	-5.6	-2.8	-2	-1.2	-1.2	-1.6	-1.2	-0.8	-1.2	-1.2	-2.4	-2	-3.2	-3.6	-4.4	-4	-4.8	-5.2	-6.4	-6.4	-7.6	-7.6	-7.6	-6.4	-6	-5.2	-4	-3.6	-3.6	-3.2	-3.2	-4.8	-6.4	-5.6	-5.2	-3.2	-3.2	-2.8	-3.6	-5.2	-5.6	-4.8	-4.8	-3.2	-2.8	-0.8	1.11022e-16	1.6	4.4	
0	0	0	0.8	1.6	0.4	-1.6	-1.6	-2.8	-3.6	-4.4	-6	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-4.4	-2	-7.2	-10.4	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-13.6	-14.4	-14.4	-14	-13.2	-13.6	-13.2	-13.2	-13.6	-14.8	-14.8	-14.8	-16.4	-18	-18	-18	-18	-18	-18.8	-19.6	-20.8	-22	-22.8	-23.6	-24.4	-26.4	-26.4	-26.4	-26.4	-26	-26.4	-26.8	-28.4	-28.4	-28.8	-30.4	-28.4	-25.2	-28.4	-26	-24.8	-24.8	-25.6	-26.4	-26.4	-28	-26	-24	-22.4	-22.8	-19.6	-16.8	-15.6	-14	-12.4	-11.2	-9.2	-8	-7.2	-7.6	-5.2	-4	-3.6	-4	-3.6	-0.4	2	2	0.8	1.2	-1.11022e-16	-1.11022e-16	-1.11022e-16	-0.8	-2.4	-2.4	-3.6	-4	-4.8	-5.6	-6	-6	-7.2	-7.6	-8.4	-9.2	-9.6	-9.2	-7.6	-6	-5.6	-6.4	-6	-5.2	-6.4	-7.2	-9.2	-6.8	-5.6	-5.2	-6.4	-7.2	-6.8	-6	-6	-5.6	-5.6	-5.2	-4	-3.6	-2.4	-1.6	0.4	
0	0	0	0.8	1.6	-1.11022e-16	-0.8	-1.6	-2.8	-3.6	-4.4	-6	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-4.8	-2.8	-5.6	-10.4	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-13.2	-14	-14.4	-14.4	-14	-13.2	-13.2	-13.2	-13.2	-13.2	-14.4	-14.8	-15.6	-16.4	-18	-18	-18	-18	-18	-18	-18.8	-20	-21.2	-22	-22.8	-24.4	-26	-26	-26.4	-26	-26.4	-27.2	-27.6	-27.6	-28.8	-30	-29.2	-28.8	-27.2	-28.8	-26	-26	-26	-26	-27.6	-28	-28.4	-28	-27.2	-22.8	-19.2	-18.4	-16	-15.6	-15.2	-12.8	-11.2	-8.4	-6	-6.8	-5.6	-5.2	-3.6	-3.2	-2	-1.11022e-16	2.4	3.6	4	4.8	3.2	3.6	2.4	2	0.8	-0.8	-2	-2.8	-3.6	-5.2	-5.2	-6.4	-7.2	-7.6	-8.4	-9.2	-10.4	-10.8	-10	-8.8	-8.8	-7.6	-7.2	-8	-8	-8.4	-9.6	-7.2	-7.6	-7.6	-9.6	-9.2	-8	-8	-7.2	-6.8	-6	-5.6	-6.4	-5.6	-4	-3.6	-2.4	-1.6	
0	0	0	0.8	1.6	-1.11022e-16	-1.2	-2	-2.8	-3.6	-4.4	-6	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-6	-3.2	-3.6	-8.8	-10.4	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-13.6	-14.4	-14.4	-14.4	-14	-13.2	-12.4	-12.8	-13.2	-14	-14.4	-15.2	-15.6	-17.2	-18	-18	-18	-18	-18	-18	-18.8	-19.2	-20.4	-21.2	-22	-23.6	-25.6	-26.8	-26.4	-26	-26.4	-28.4	-28	-28.4	-29.6	-30.4	-28.4	-26.8	-28.8	-28.4	-26	-26.4	-26.4	-27.6	-28.8	-29.6	-30	-29.6	-27.6	-23.2	-16.8	-14.8	-17.6	-16.4	-14.8	-12	-11.6	-10.8	-10.4	-10.4	-12	-12.4	-12.4	-10.4	-7.6	-5.2	-2	-0.4	-0.4	0.8	-0.4	-1.11022e-16	-1.6	-0.8	-2.4	-2.8	-2.8	-3.6	-4	-4.8	-5.6	-6.4	-7.2	-8.8	-9.2	-10.8	-10.8	-10.4	-10.4	-10.4	-10	-9.2	-8.8	-9.2	-10.8	-10.8	-9.2	-7.2	-8.8	-11.2	-10.8	-9.6	-9.6	-8.4	-7.6	-7.2	-7.2	-6	-6	-5.6	-5.6	-4	-3.6	-2.8	
0	0	0	0.4	1.6	-1.11022e-16	-1.2	-2	-2	-3.6	-4.4	-6	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-4.4	-2.8	-7.2	-10.4	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-13.2	-14.4	-14.4	-14.4	-14.4	-14	-13.2	-12	-12.8	-13.2	-14	-14.4	-15.2	-15.6	-17.2	-18	-18	-18	-18	-18	-18	-18	-18.8	-20	-20.4	-21.6	-24	-24.8	-26	-26	-26.8	-27.2	-27.6	-28	-28	-30	-29.2	-28.8	-27.6	-28.8	-26.8	-26.4	-26.8	-27.2	-28.8	-30	-30	-30.4	-29.6	-26	-18.4	-15.6	-15.6	-17.2	-16	-14.8	-14	-13.2	-12.4	-12	-12	-13.6	-14.4	-13.6	-13.2	-11.6	-8.4	-6.8	-4.8	-6	-4	-5.2	-5.2	-6	-6	-6.4	-6.4	-4.4	-5.2	-4.4	-6	-6	-7.2	-7.2	-8.8	-10	-11.2	-10.4	-10.8	-10.8	-11.6	-10.8	-10	-10.8	-11.2	-12.8	-10.8	-8.8	-9.2	-11.6	-11.6	-11.6	-10.4	-10	-8.8	-8.8	-7.6	-7.6	-6.4	-5.6	-6	-6	-5.6	-4	-3.2	
0	0	0	0.4	1.2	0.8	-1.2	-2	-2	-3.6	-4.4	-6	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-5.2	-3.2	-3.2	-8.8	-10.4	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-12.8	-14	-14.4	-14.4	-14.4	-14.4	-14	-13.2	-12	-12.8	-13.2	-14	-14.4	-15.2	-15.6	-17.2	-18	-18	-18	-18	-18	-18	-18	-18.8	-19.2	-19.6	-20.8	-23.2	-24	-25.6	-26	-26.4	-27.2	-28.4	-27.6	-29.6	-30	-28.4	-26.8	-29.2	-27.6	-26.8	-27.2	-26.8	-28	-30	-30.8	-31.2	-30.8	-27.6	-23.2	-16.4	-15.2	-16.8	-18.8	-16.4	-15.6	-15.2	-14.4	-13.6	-13.2	-14.4	-14.8	-15.2	-14.8	-13.6	-12	-9.2	-9.2	-8.8	-8.4	-6.8	-8.8	-7.2	-6.4	-8.4	-8	-7.2	-5.6	-6.8	-5.6	-6.8	-6.8	-7.6	-7.6	-9.2	-10	-10.8	-10	-11.2	-11.2	-12	-12.8	-11.2	-12	-12.4	-12.8	-10.4	-10	-11.6	-12	-11.2	-12	-10.8	-10.4	-9.2	-9.2	-8	-7.2	-7.2	-6	-5.6	-6.4	-6.4	-4.8	-4.4	
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0	0	0	0	0	0	0	0	0	0	0	1.6	2.8	-0.4	-1.6	-2.8	-5.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-6	-6	-6.4	-7.2	-7.2	-8	-8.8	-8.8	-8.8	-8.8	-8.8	-8.8	-7.6	-7.2	-7.2	-7.2	-7.2	-7.2	-6.8	-6.4	-6.4	-6.4	-6.4	-6.4	-6.4	-6	-5.6	-4.8	-4	-4	-4.8	-5.6	-8	-8.8	-11.6	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-10.8	-10	-8.4	-8	-8	-6.8	-6.8	-2.4	-1.2	-2	-3.2	-3.6	-4	-4.4	-4.8	-3.2	-2.4	-2.8	-1.6	-2	-2	-2	-2	-1.6	-1.2	-1.2	0	0	0.8	2	2.4	2.4	1.6	0.4	0.4	-0.4	-1.6	-2.4	-3.2	-3.6	-4.4	-4.8	-4.8	-3.2	-3.6	-4.8	-3.6	-4.4	-2.8	-2.4	-3.6	-3.2	-4.8	-4.8	-6.4	-6.8	-6.4	-5.2	-3.6	-3.6	-4	-3.6	-5.2	-5.6	-6	-5.2	-4.8	-4	-3.2	-2	1.11022e-16	4.8	
0	0	0	0	0	0	0	0	0	0	0	0.4	2	2.4	-1.2	-2	-4	-6.4	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-6.8	-6.4	-6	-6	-6.4	-6.4	-7.2	-8	-8	-8.8	-8.8	-8.8	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-7.2	-6.8	-6.8	-6.8	-6.8	-6.4	-5.6	-5.2	-4	-4.4	-5.6	-7.6	-9.2	-10.8	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-12.4	-10.8	-10	-9.2	-7.6	-7.6	-7.2	-6	-6	-5.6	-5.6	-6.4	-6.8	-6.8	-5.2	-5.2	-5.2	-4.4	-3.2	-3.2	-3.2	-2.4	-3.6	-3.2	-2.4	-2	-1.6	-0.8	-0.4	-0.4	-0.8	0	0.8	1.6	2	2.8	3.6	4.4	4.8	6	3.6	2.8	2.8	2.8	1.6	2.4	1.6	1.6	1.6	0.4	-0.8	-0.4	-0.8	-0.8	-1.2	-0.8	-1.2	-2	-2.4	-3.6	-4.4	-4	-1.6	-1.6	-1.2	-1.6	-1.6	-1.2	-0.8	1.6	1.6	3.6	5.6	4.8	4	
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#include <iostream>
#include <math.h>
#include <vector>
#include <algorithm>
#include <fstream>

using namespace std;

// Map class
class Map {
public:
    const static int mapHeight = 300;
    const static int mapWidth = 150;
    vector<vector<double> > map = GetMap();
    vector<vector<int> > grid = MaptoGrid();
    vector<vector<int> > heuristic = GenerateHeuristic();

private:
    // Read the file and get the map
    vector<vector<double> > GetMap()
    {
        vector<vector<double> > mymap(mapHeight, vector<double>(mapWidth));
        ifstream myReadFile;
        myReadFile.open("map.txt");

        while (!myReadFile.eof()) {
            for (int i = 0; i < mapHeight; i++) {
                for (int j = 0; j < mapWidth; j++) {
                    myReadFile >> mymap[i][j];
                }
            }
        }
        return mymap;
    }

    //Convert the map to 1's and 0's
    vector<vector<int> > MaptoGrid()
    {
        vector<vector<int> > grid(mapHeight, vector<int>(mapWidth));
        for (int x = 0; x < mapHeight; x++) {
            for (int y = 0; y < mapWidth; y++) {
                if (map[x][y] == 0) //unkown state
                    grid[x][y] = 1;

                else if (map[x][y] > 0) //Occupied state
                    grid[x][y] = 1;

                else //Free state
                    grid[x][y] = 0;
            }
        }

        return grid;
    }

    // Generate a Manhattan Heuristic Vector
    vector<vector<int> > GenerateHeuristic()
    {
        vector<vector<int> > heuristic(mapHeight, vector<int>(mapWidth));
        int goal[2] = { 60, 50 };
        for (int i = 0; i < heuristic.size(); i++) {
            for (int j = 0; j < heuristic[0].size(); j++) {
                int xd = goal[0] - i;
                int yd = goal[1] - j;
                // Manhattan Distance
                   int d = abs(xd) + abs(yd);
                // Euclidian Distance
                // double d = sqrt(xd * xd + yd * yd);
                // Chebyshev distance
                // int d = max(abs(xd), abs(yd));
                heuristic[i][j] = d;
            }
        }
        return heuristic;
    }
};

// Planner class
class Planner : Map {
public:
    int start[2] = { 230, 145 };
    int goal[2] = { 60, 50 };
    int cost = 1;

    string movements_arrows[4] = { "^", "<", "v", ">" };

    vector<vector<int> > movements{
        { -1, 0 },
        { 0, -1 },
        { 1, 0 },
        { 0, 1 }
    };

    vector<vector<int> > path;
};

// Printing vectors of any type
template <typename T>
void print2DVector(T Vec)
{
    for (int i = 0; i < Vec.size(); ++i) {
        for (int j = 0; j < Vec[0].size(); ++j) {
            cout << Vec[i][j] << ' ';
        }
        cout << endl;
    }
}

Planner search(Map map, Planner planner)
{
    // Create a closed 2 array filled with 0s and first element 1
    vector<vector<int> > closed(map.mapHeight, vector<int>(map.mapWidth));
    closed[planner.start[0]][planner.start[1]] = 1;

    // Create expand array filled with -1
    vector<vector<int> > expand(map.mapHeight, vector<int>(map.mapWidth, -1));

    // Create action array filled with -1
    vector<vector<int> > action(map.mapHeight, vector<int>(map.mapWidth, -1));

    // Defined the quadruplet values
    int x = planner.start[0];
    int y = planner.start[1];
    int g = 0;
    int f = g + map.heuristic[x][y];

    // Store the expansions
    vector<vector<int> > open;
    open.push_back({ f, g, x, y });

    // Flags and Counts
    bool found = false;
    bool resign = false;
    int count = 0;

    int x2;
    int y2;

    // While I am still searching for the goal and the problem is solvable
    while (!found && !resign) {
        // Resign if no values in the open list and you can't expand anymore
        if (open.size() == 0) {
            resign = true;
            cout << "Failed to reach a goal" << endl;
        }
        // Keep expanding
        else {
            // Remove quadruplets from the open list
            sort(open.begin(), open.end());
            reverse(open.begin(), open.end());
            vector<int> next;
            // Stored the poped value into next
            next = open.back();
            open.pop_back();

            x = next[2];
            y = next[3];
            g = next[1];

            // Fill the expand vectors with count
            expand[x][y] = count;
            count += 1;
            

            // Check if we reached the goal:
            if (x == planner.goal[0] && y == planner.goal[1]) {
                found = true;
                //cout << "[" << g << ", " << x << ", " << y << "]" << endl;
            }

            //else expand new elements
            else {
                for (int i = 0; i < planner.movements.size(); i++) {
                    x2 = x + planner.movements[i][0];
                    y2 = y + planner.movements[i][1];
                    if (x2 >= 0 && x2 < map.grid.size() && y2 >= 0 && y2 < map.grid[0].size()) {
                        if (closed[x2][y2] == 0 and map.grid[x2][y2] == 0) {
                            int g2 = g + planner.cost;
                            f = g2 + map.heuristic[x2][y2];
                            open.push_back({ f, g2, x2, y2 });
                            closed[x2][y2] = 1;
                            action[x2][y2] = i;
                        }
                    }
                }
            }
        }
    }

    // Print the expansion List
    //print2DVector(expand);

    // Find the path with robot orientation
    vector<vector<string> > policy(map.mapHeight, vector<string>(map.mapWidth, "-"));

    // Going backward
    x = planner.goal[0];
    y = planner.goal[1];
    policy[x][y] = '*';

    while (x != planner.start[0] or y != planner.start[1]) {
        x2 = x - planner.movements[action[x][y]][0];
        y2 = y - planner.movements[action[x][y]][1];
        // Store the  Path in a vector
        planner.path.push_back({ x2, y2 });
        policy[x2][y2] = planner.movements_arrows[action[x][y]];
        x = x2;
        y = y2;
    }

    // Print the robot path
    //cout << endl;
    print2DVector(policy);

    return planner;
}

int main()
{
    // Instantiate a planner and map objects
    Map map;
    Planner planner;
    // Generate the shortest Path using the Astar algorithm
    planner = search(map, planner);

    return 0;
}