Implementing A* Algorithm
To implement the A* algorithm for grid-based path planning, we need to follow a step-by-step process. In this lesson, we will walk through the implementation of the algorithm using Python.
First, let's import the necessary libraries:
PYTHON
1import heapqxxxxxxxxxx93
print(path)import heapqdef a_star_algorithm(grid, start, target): # Create a priority queue to store the open nodes open_nodes = [] # Create a set to store the visited nodes visited = set() # Create a dictionary to store the parent nodes parents = {} # Create a dictionary to store the g-score for each node g_scores = {} # Create a dictionary to store the f-score for each node f_scores = {} # Initialize the g-score and f-score for the start node g_scores[start] = 0 f_scores[start] = heuristic(start, target) # Add the start node to the priority queue heapq.heappush(open_nodes, (f_scores[start], start)) while open_nodes: # Remove the node with the lowest f-score from the priority queue current = heapq.heappop(open_nodes)[1]OUTPUT
:001 > Cmd/Ctrl-Enter to run, Cmd/Ctrl-/ to comment


