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stA_star.py
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stA_star.py
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import heapq
import numpy as np
def heuristic(a, b, array,raw_orientation=None,new_orientation=None):
"""
计算两个点之间的曼哈顿距离
"""
return abs(a[0] - b[0]) + abs(a[1] - b[1])
def find_neighbors(point,array=None):
# 小型地图
neighbors=[]
if point[0] in [1,4,7]:
neighbors.append((0,1))
if point[0] in [2,5,8]:
neighbors.append((0,-1))
if point[1] in [0,3,6]:
neighbors.append((1,0))
if point[1] in [1,4,7]:
neighbors.append((-1,0))
'''# 大型地图
if point[0] in [1,4,7,10,13,16,19,22,25]:
neighbors.append((0,1))
if point[0] in [2,5,8,11,14,17,20,23,26]:
neighbors.append((0,-1))
if point[1] in [0,3,6,9,12,15,18,21,24,27]:
neighbors.append((1,0))
if point[1] in [1,4,7,10,13,16,19,22,25,28]:
neighbors.append((-1,0))'''
return neighbors
def update_StTable(raw_map, StTable, path):
if len(StTable) - 1 < path[-1][2]:
new_time_step = path[-1][2] - len(StTable) + 1
new_time_step_map = np.repeat(np.expand_dims(raw_map,axis=0), new_time_step, axis=0)
for node in path[len(path) - new_time_step :]:
new_time_step_map[node[2]-len(StTable)][node[0]][node[1]] = 6
StTable = np.concatenate([StTable,new_time_step_map],axis=0)
for node in path[:len(path) - new_time_step]:
StTable[node[2]][node[0]][node[1]] = 6
else:
for node in path:
StTable[node[2]][node[0]][node[1]] = 6
return StTable
def update_start(StTable, start):
if len(StTable) - 1 >= start[2]:
# 检查新的start是否已经被使用过
if is_start_used(StTable, start):
return update_start(StTable, (start[0], start[1], start[2] + 1))
return start
def is_start_used(StTable, start):
# 检查start是否占用
return StTable[start[2]][start[0]][start[1]] == 6
def StAstar(array, start, goal, StTable):
"""
A* 寻路算法
"""
if len(StTable) - 1 >= start[2]:
while StTable[start[2]][start[0]][start[1]] == 6:
start = update_start(StTable,start)
close_set = set()
came_from = {}
gscore = {start:0}
fscore = {start:heuristic(start, goal, array)}
oheap = []
# 初始化方向，位于上方入口（行索引为0），初始方向向下；位于下方入口，初始方向向上
if start[0] == 0:
last_orientation = (1,0)
if start[0] == len(array)-1:
last_orientation = (-1,0)
heapq.heappush(oheap, (fscore[start], start))
while oheap:
# 标记一下这次有没有找到路
signal=0
current = heapq.heappop(oheap)[1]
close_set.add(current)
# 判断是否成功到达卸货单元
for neighbor in [(0,1),(0,-1),(1,0),(-1,0)]:
if (current[0]+neighbor[0],current[1]+neighbor[1]) == goal:
data = []
data.append(goal)
while current in came_from:
data.append(current)
current = came_from[current]
data.append(start)
return data[::-1], data[1][2] - data[-1][2]
# 判断该点可达的邻居
neighbors=find_neighbors(current,array)
for i, j in neighbors:
# 判断是否转向
if current in came_from.keys():
if current[0] != came_from[current][0] or current[1] != came_from[current][1]:
last_orientation=(current[0]-came_from[current][0],current[1]-came_from[current][1]) #若停留的话last_orientation会成为(0,0),修改后停留时方向不变
#判断是否转向,若转向则时间步+2,否则时间步+1
if last_orientation != (i,j):
neighbor = current[0] + i, current[1] + j, current[2] + 2
if last_orientation == (i,j):
neighbor = current[0] + i, current[1] + j, current[2] + 1
# 约束：1.静态障碍物; 2.地图边界； 3.动态障碍
if 0 <= neighbor[0] < array.shape[0]:
if 0 <= neighbor[1] < array.shape[1]:
# 判断是否为障碍物 此处可以改成其他限制条件
if array[neighbor[0]][neighbor[1]] == 5:
signal = 0
continue
if array[neighbor[0]][neighbor[1]] == 4 and neighbor != goal:
signal = 0
continue
#判断是否为动态障碍物，即优先级高的AGV在该时间步的位置
if neighbor[2]+1 <= len(StTable):
if StTable[neighbor[2]][neighbor[0]][neighbor[1]] == 6:
signal = 0
continue
#若要在原地转向等待一个时间步，需要判断当前节点时间步+1是否被优先级更高任务占用
if last_orientation != (i,j):
if StTable[current[2]+1][current[0]][current[1]] == 6:
signal = 0
continue
else:
# 超出边界
signal = 0
continue
else:
# 超出边界
signal = 0
continue
# 如果要转向，距离额外加1
if last_orientation!=(i,j):
tentative_g_score = gscore[current] + heuristic(current, neighbor, array) + 1
else:
tentative_g_score = gscore[current] + heuristic(current, neighbor, array)
# 如果距离更远，排除
if neighbor in close_set and tentative_g_score > gscore.get(neighbor, 0):
signal = 0
continue
# 如果距离更近，更新
if tentative_g_score <= gscore.get(neighbor, 0) or (neighbor not in [i[1]for i in oheap]):
#若时间步差1，则表示无转向和等待
if neighbor[2] - current[2] == 1:
came_from[neighbor] = current
gscore[neighbor] = tentative_g_score
fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal,array)
heapq.heappush(oheap, (fscore[neighbor], neighbor))
signal=1
#若时间步差2，则表示转向和等待
if neighbor[2] - current[2] > 1:
tmp = current[0], current[1], current[2] + 1
came_from[tmp] = current
came_from[neighbor] = tmp
gscore[neighbor] = tentative_g_score
fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal,array)
heapq.heappush(oheap, (fscore[neighbor], neighbor))
signal=1
'''
came_from[neighbor] = current
gscore[neighbor] = tentative_g_score
fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal,array)
heapq.heappush(oheap, (fscore[neighbor], neighbor))
signal=1
'''
#若没有搜寻到路径则在原地等待
if signal == 0:
if len(StTable)-1 >= current[2]+1:
if StTable[current[2]+1][current[0]][current[1]] != 6:
tentative_g_score = gscore[current]+1
current=(current[0] , current[1] , current[2]+1)
came_from[current] = (current[0] , current[1] , current[2]-1)
gscore[current] = tentative_g_score
fscore[current] = tentative_g_score + heuristic(current, goal,array)
heapq.heappush(oheap, (fscore[current], current))
#下一时刻被前序任务占用
else:
continue
else:
tentative_g_score = gscore[current]+1
current=(current[0] , current[1] , current[2]+1)
came_from[current] = (current[0] , current[1] , current[2]-1)
gscore[current] = tentative_g_score
fscore[current] = tentative_g_score + heuristic(current, goal,array)
heapq.heappush(oheap, (fscore[current], current))
return None