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pathfinding.py
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pathfinding.py
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from __future__ import print_function
import board
import main
from constants import *
import heapq
import board_viz
class AStarGraph(object):
# Define a class board like grid with two barriers
def __init__(self, game_board):
self.barriers = []
self._game_board = game_board
self.set_barriers()
def set_barriers(self):
self.barriers = [wall.get_pos() for wall in self._game_board.get_walls()]
def heuristic(self, start, goal):
return 0
# Use Chebyshev distance heuristic if we can move one square either
# adjacent or diagonal
D = 1
D2 = 1
dx = abs(start[0] - goal[0])
dy = abs(start[1] - goal[1])
return D * (dx + dy) + (D2 - 2 * D) * min(dx, dy)
def get_vertex_neighbours(self, pos):
n = []
# Moves allow link a chess king
for dx, dy in DIRECTIONS:
x2 = pos[0] + dx
y2 = pos[1] + dy
if (
x2 < -MARGIN or x2 > BOARD_SIZE + MARGIN
or y2 < -MARGIN or y2 > BOARD_SIZE + MARGIN
):
continue
n.append((x2, y2))
return n
def move_cost(self, a, b):
if b in self.barriers:
return 20 # high cost to move through walls
return 1 # Normal movement cost
"""
def AStarSearch(start, targets, graph):
# remove every barrier that is in the targets
graph.set_barriers()
graph.barriers = list(set(graph.barriers) - set(targets))
G = {} # Actual movement cost to each position from the start position
F = {} # Estimated movement cost of start to end going via this position
# Initialize starting values
G[start] = 0
F[start] = graph.heuristic(start, targets)
closedVertices = set()
openVertices = set([start])
cameFrom = {}
while len(openVertices) > 0:
# Get the vertex in the open list with the lowest F score
current = None
currentFscore = None
for pos in openVertices:
if current is None or F[pos] < currentFscore:
currentFscore = F[pos]
current = pos
# Check if we have reached the goal
if current in targets:
# Retrace our route backward
path = [current]
while current in cameFrom:
current = cameFrom[current]
path.append(current)
path.reverse()
# cut path at barrier if passes through
final_path = []
for loc in path:
final_path.append(loc)
if loc in graph.barriers:
break
return final_path, F[current] # Done!
# Mark the current vertex as closed
openVertices.remove(current)
closedVertices.add(current)
# Update scores for vertices near the current position
for neighbour in graph.get_vertex_neighbours(current):
if neighbour in closedVertices:
continue # We have already processed this node exhaustively
candidateG = G[current] + graph.move_cost(current, neighbour)
if neighbour not in openVertices:
openVertices.add(neighbour) # Discovered a new vertex
elif candidateG >= G[neighbour]:
continue # This G score is worse than previously found
# Adopt this G score
cameFrom[neighbour] = current
G[neighbour] = candidateG
H = graph.heuristic(neighbour, targets)
F[neighbour] = G[neighbour] + H
print("A* failed to find a solution")
return [], 0
"""
def Dijkstra(start, targets, graph):
graph.set_barriers()
graph.barriers = list(set(graph.barriers) - set(targets))
G = {} # Actual movement cost to each position from the start position
# Initialize starting values
G[start] = 0
closedVertices = list()
# create priority queue of open veritces
openVertices = []
heapq.heappush(openVertices, (0, start))
cameFrom = {}
while len(openVertices) > 0:
# Get the vertex in the open list with the lowest F score
cost, current = heapq.heappop(openVertices)
# Check if we have reached the goal
if current in targets:
# Retrace our route backward
path = [current]
while current in cameFrom:
current = cameFrom[current]
path.append(current)
path.reverse()
# cut path at barrier if passes through
final_path = []
for loc in path:
final_path.append(loc)
if loc in graph.barriers:
break
return final_path, G[loc] # Done!
# Mark the current vertex as closed
closedVertices.append(current)
# Update scores for vertices near the current position
for neighbour in graph.get_vertex_neighbours(current):
if neighbour in closedVertices:
continue # We have already processed this node exhaustively
candidateG = G[current] + graph.move_cost(current, neighbour)
if (
neighbour not in G.keys()
or (
neighbour in G.keys()
and candidateG < G[neighbour]
)
):
heapq.heappush(openVertices, (candidateG, neighbour))
cameFrom[neighbour] = current
G[neighbour] = candidateG
print("shouldnt be here ever")
print("barriers", graph.barriers)
print("start", start)
print("targets", targets)
return [], 0
if __name__ == "__main__":
gb = board.GameBoard(main.generate_random_base_3())
graph = AStarGraph(gb)
print(graph.barriers)
start = (0, 0)
targets = [(1, 7), (7, 7), (7, 1)]
result, cost = AStarSearch(start, targets, graph)
board_viz.viz_path(result, graph, targets)