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distance_calculator.py
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distance_calculator.py
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import math
import numpy
from heapq import heappush, heappop
def heuristic_cost_estimate(neighbor, goal):
x = neighbor[0] - goal[0]
y = neighbor[1] - goal[1]
return abs(x) + abs(y)
def dist_between(a, b):
return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
def reconstruct_path(came_from, current):
path = [current]
while current in came_from:
current = came_from[current]
path.append(current)
return path
# astar function returns a list of points (shortest path)
def astar(array, start, goal):
debug_array = numpy.array(array)
# 8个方向 (1, 1),(1, -1), (-1, 1), (-1, -1)
directions = [(0, 1), (0, -1), (1, 0), (-1, 0)]
close_set = set()
came_from = {}
gscore = {start: 0}
fscore = {start: heuristic_cost_estimate(start, goal)}
openSet = []
openSet.append((fscore[start], start)) # 往堆中插入一条新的值
# while openSet is not empty
while openSet:
# debug_array = numpy.array(array)
# for i in range(len(openSet)):
# debug_array[openSet[i][1]] = fscore[openSet[i][1]]
# print(openSet[i][1], fscore[openSet[i][1]])
# print(debug_array)
# current := the node in openSet having the lowest fScore value
current = openSet.pop()[1]
# print(current)
# print("\n")
if current == goal:
return reconstruct_path(came_from, current)
close_set.add(current)
for i, j in directions: # 对当前节点的 8 个相邻节点一一进行检查
neighbor = current[0] + i, current[1] + j
# 判断节点是否在地图范围内，并判断是否为障碍物
if 0 <= neighbor[0] < array.shape[0]:
if 0 <= neighbor[1] < array.shape[1]:
if array[neighbor[0]][neighbor[1]] == 1: # 1为障碍物
continue
else:
# array bound y walls
continue
else:
# array bound x walls
continue
# Ignore the neighbor which is already evaluated.
if neighbor in close_set:
continue
# The distance from start to a neighbor via current
tentative_gScore = gscore[current] + \
dist_between(current, neighbor)
# Discover a new node
if neighbor not in [i[1] for i in openSet]:
came_from[neighbor] = current
gscore[neighbor] = tentative_gScore
fscore[neighbor] = tentative_gScore + heuristic_cost_estimate(neighbor, goal)
openSet.append((fscore.get(neighbor, numpy.inf), neighbor))
# print("openSet sort before:", openSet)
openSet.sort(key=lambda x:x[0], reverse=True)
# print("openSet sort after:", openSet)
elif tentative_gScore < gscore.get(neighbor, numpy.inf):
# This path is the best until now. Record it!
openSet.remove((fscore.get(neighbor, numpy.inf), neighbor))
came_from[neighbor] = current
gscore[neighbor] = tentative_gScore
fscore[neighbor] = tentative_gScore + heuristic_cost_estimate(neighbor, goal)
openSet.append((fscore.get(neighbor, numpy.inf), neighbor))
openSet.sort(key=lambda x:x[0], reverse=True)
# debug_array[neighbor[0]][neighbor[1]] = 6
# print(debug_array)
return False
# def eichilide_distance():
# distance = math.hypot((x - robotlocation.x), (y - robotlocation.y))
# return distance
def astar_distance(map_array, start, goal):
path = astar(map_array, start, goal)
if(path != False):
# for i in range(len(path)):
# map_array[path[i]] = 100
# print(map_array)
# reture the lenght of this path and the next position expected to move
return len(path), path[-2]
return 1, start
if __name__ == "__main__":
# nmap = numpy.array([
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
# [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1],
# [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
nmap = numpy.array([
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
path = astar(nmap, (4, 11), (5, 2))
print(path[-2])
for i in range(len(path)):
nmap[path[i]] = 100
print(nmap)