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Lab 3: A star search solution

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Lab 3: A star search
The task in this lab is to create a search algorithm to help a robot find its way to a destination
using A star search and the Manhattan distance heuristic. The robot exists in a grid world
named “map” of size MAP_WIDTH by MAP_HEIGHT, and it can move in all 4 directions
(diagonals not allowed) through empty space cells only by steps of exactly 1 cell distance.
The starting location of the robot is marked in map with a number “2” and the goal with a
number “3”. The other two values you can find in the map are “1” for walls and “0” for empty
space.
Sample map of size MAP_WIDTH 5 and MAP_HEIGHT 5
Robot starting location in map[1][1]

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Lab 3: A star search
The task in this lab is to create a search algorithm to help a robot find its way to a destination
using A star search and the Manhattan distance heuristic. The robot exists in a grid world
named “map” of size MAP_WIDTH by MAP_HEIGHT, and it can move in all 4 directions
(diagonals not allowed) through empty space cells only by steps of exactly 1 cell distance.
The starting location of the robot is marked in map with a number “2” and the goal with a
number “3”. The other two values you can find in the map are “1” for walls and “0” for empty
space.
Sample map of size MAP_WIDTH 5 and MAP_HEIGHT 5
Robot starting location in map[1][1]
Goal in map[3][3]
You are to complete the code for the function: astar_search(map)
The A star search should use the Manhattan distance for its heuristic. The function should
return a boolean value “true” if the destination was reached and “false” otherwise. An additional
condition is to mark the map with a number “4” in all explored cells and with a number “5” in the
cells that are part of path found.
To make sure everybody arrives to the same results (very important for the automated grader)
when a tie (two nodes in the frontier have the same f(n) value) is found, first expand the node
with the lowest x value, if there is still a tie, then expand the node with the lowest y value.
Considerations:
● We provided several maps to let you test your solution, but the grading will use a
different set. Good performance on the test cases is not a guarantee you will do well on
the grading cases.
● Remember to use the provided constants for the map boundaries and do not hardcode
any values because during grading the dimensions of the map can be different.
● The running time of your algorithm cannot be longer than 10 seconds for a 15×15 maps
or smaller, otherwise it will fail the grading tests.
● All maps will have a maximum of 1 possible path between the starting location and the
destination (to make it easier).
● Unreachable goals are possible.
● There will be no loops in the maps (to make it easier).
● There will always be exactly 1 starting position and 1 goal.