import pandas as pd
import math
import numpy as np
import matplotlib.pyplot as plt
show_animation = True
"""读取流速"""
# file_path = r"C:\Users\LEGION\Desktop\A题\vx_data_processed.xlsx"
# file_path1=r"C:\Users\LEGION\Desktop\A题\vy_data_processed.xlsx"
file_path = r"C:\Users\LEGION\Desktop\A题\processed_vx_data.xlsx"
file_path1=r"C:\Users\LEGION\Desktop\A题\processed_vy_data.xlsx"
# 读取 Excel 数据
df = pd.read_excel(file_path, header=None)
df1 = pd.read_excel(file_path1, header=None)
# 第一列和第一行是坐标值，数据部分从第二行第二列开始
data = df.values # 转换为数组
data1 = df1.values
"""读取概率"""
# file_path3 = r"C:\Users\LEGION\Desktop\A题\probability_grid A.xlsx"
# file_path4=r"C:\Users\LEGION\Desktop\A题\probability_grid B.xlsx"
file_path3 = r"C:\Users\LEGION\Desktop\A题\processed__probability_gridA.xlsx"
file_path4 = r"C:\Users\LEGION\Desktop\A题\processed__probability_gridB.xlsx"
df3 = pd.read_excel(file_path3, header=None)
df4 = pd.read_excel(file_path4, header=None)
# 第一列和第一行是坐标值，数据部分从第二行第二列开始
data3 = df3.values # 转换为数组
data4 = df4.values
class AStarPlanner:
def __init__(self, ox, oy, resolution, rr):
"""
Initialize grid map for a star planning
ox: x position list of Obstacles [m]
oy: y position list of Obstacles [m]
resolution: grid resolution [m],地图的像素
rr: robot radius[m]
"""
self.resolution = resolution
self.rr = rr
self.min_x, self.min_y = 0, 0
self.max_x, self.max_y = 0, 0
self.obstacle_map = None
self.x_width, self.y_width = 0, 0
self.motion = self.get_motion_model()
self.calc_obstacle_map(ox, oy)
class Node:
"""定义搜索区域节点类,每个Node都包含坐标x和y, 移动代价cost和父节点索引。
"""
def __init__(self, x, y, cost, parent_index):
self.x = x # index of grid
self.y = y # index of grid
self.cost = cost
self.parent_index = parent_index
def __str__(self):
return str(self.x) + "," + str(self.y) + "," + str(
self.cost) + "," + str(self.parent_index)
def planning(self, sx, sy, gx, gy, data1 , data2, data3 , data4):
"""
A star path search
输入起始点和目标点的坐标(sx,sy)和(gx,gy)，
最终输出的结果是路径包含的点的坐标集合rx和ry。
input:
s_x: start x position [m]
s_y: start y position [m]
gx: goal x position [m]
gy: goal y position [m]
output:
rx: x position list of the final path
ry: y position list of the final path
"""
start_node = self.Node(self.calc_xy_index(sx, self.min_x),
self.calc_xy_index(sy, self.min_y), 0.0, -1)
goal_node = self.Node(self.calc_xy_index(gx, self.min_x),
self.calc_xy_index(gy, self.min_y), 0.0, -1)
open_set, closed_set = dict(), dict()
open_set[self.calc_grid_index(start_node)] = start_node
while 1:
if len(open_set) == 0:
print("Open set is empty..")
break
c_id = min(
open_set,
key=lambda o: open_set[o].cost + self.calc_heuristic(data,data1,goal_node, open_set[o]))
current = open_set[c_id]
# show graph
if show_animation: # pragma: no cover
plt.plot(self.calc_grid_position(current.x, self.min_x),
self.calc_grid_position(current.y, self.min_y), "xc")
# for stopping simulation with the esc key.
plt.gcf().canvas.mpl_connect('key_release_event', lambda event: [exit(0) if event.key == 'escape' else None])
if len(closed_set.keys()) % 10 == 0:
plt.pause(0.001)
# 通过追踪当前位置current.x和current.y来动态展示路径寻找
if current.x == goal_node.x and current.y == goal_node.y:
print("Find goal")
goal_node.parent_index = current.parent_index
goal_node.cost = current.cost
break
# Remove the item from the open set
del open_set[c_id]
# Add it to the closed set
closed_set[c_id] = current
# expand_grid search grid based on motion model
for i, _ in enumerate(self.motion):
"""计算能耗代价"""
n1x = current.x
n1y = current.y
n2x = current.x + self.motion[i][0]
n2y = current.y + self.motion[i][1]
if n2x < 0 or n2x >= len(data1) or n2y < 0 or n2y >= len(data1[0]):
continue
vx1 = data1[n2x][n2y]*1.94
vy1 = data2[n2x][n2y]*1.94
vx2 = 0 # 空值
vy2 = 0 # 空值
dis = 0 # 空值
if n2y>n1y and n2x>n1x:
vx2 = 4/np.sqrt(2)
vy2 = 4/np.sqrt(2)
dis = 1.25/2
if n2y>n1y and n2x<n1x:
vx2 = -4/np.sqrt(2)
vy2 = 4/np.sqrt(2)
dis = 1.25/2
if n2y<n1y and n2x<n1x:
vx2 = -4/np.sqrt(2)
vy2 = -4/np.sqrt(2)
dis = 1.25/2
if n2y<n1y and n2x>n1x:
vx2 = 4/np.sqrt(2)
vy2 = -4/np.sqrt(2)
dis = 1.25/2
if n2y>n1y and n2x==n1x:
vx2=0
vy2=4
dis = 0.92/2
if n2y<n1y and n2x == n1x:
vx2 = 0
vy2 = -4
dis = 0.92/2
if n2y==n1y and n2x >n1x:
vx2 = 4
vy2 = 0
dis = 0.84/2
if n2y==n1y and n2x <n1x:
vx2 = -4
vy2 = 0
dis = 0.84/2
vt = math.sqrt((vx1-vx2)**2+(vy1-vy2)**2)
if vt <= 1:
en = dis * 0.06
if vt > 1 and vt<=2:
en = dis * 0.08
if vt > 2 and vt<=3:
en = dis * 0.11
if vt > 3 and vt <= 4:
en = dis * 0.15
if vt > 4 and vt <= 5:
en = dis * 0.2
w = 0
q = 0
"""计算概率代价"""
p1=data3[n2x][n2y]
p2=data4[n2x][n2y]
P=(p1+p2)
node = self.Node(current.x + self.motion[i][0],
current.y + self.motion[i][1],
current.cost + self.motion[i][2]+en * w+P*q, c_id)
n_id = self.calc_grid_index(node)
# If the node is not safe, do nothing
if not self.verify_node(node):
continue
if n_id in closed_set:
continue
if n_id not in open_set:
open_set[n_id] = node # discovered a new node
else:
if open_set[n_id].cost > node.cost:
# This path is the best until now. record it
open_set[n_id] = node
rx, ry = self.calc_final_path(goal_node, closed_set)
return rx, ry
def calc_final_path(self, goal_node, closed_set):
# generate final course
rx, ry = [self.calc_grid_position(goal_node.x, self.min_x)], [
self.calc_grid_position(goal_node.y, self.min_y)]
parent_index = goal_node.parent_index
while parent_index != -1:
n = closed_set[parent_index]
rx.append(self.calc_grid_position(n.x, self.min_x))
ry.append(self.calc_grid_position(n.y, self.min_y))
parent_index = n.parent_index
return rx, ry
@staticmethod
def calc_heuristic(data1,data2,n1, n2):
"""计算启发函数
Args:
n1 (_type_): _description_
n2 (_type_): _description_
Returns:
_type_: _description_
"""
"""定义海水流速造成的能耗损失代价"""
"""定义目标被发现的概率代价"""
w = 1.0 # weight of heuristic
d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
return d
def calc_grid_position(self, index, min_position):
"""
calc grid position
:param index:
:param min_position:
:return:
"""
pos = index * self.resolution + min_position
return pos
def calc_xy_index(self, position, min_pos):
return round((position - min_pos) / self.resolution)
def calc_grid_index(self, node):
return (node.y - self.min_y) * self.x_width + (node.x - self.min_x)
def verify_node(self, node):
px = self.calc_grid_position(node.x, self.min_x)
py = self.calc_grid_position(node.y, self.min_y)
if node.x < 0 or node.x >= self.x_width or node.y < 0 or node.y >= self.y_width:
return False # 如果超出范围，返回 False
# 检查节点是否碰到障碍物
if self.obstacle_map[node.x][node.y]:
return False # 如果该位置是障碍物，返回 False
if px < self.min_x:
return False
elif py < self.min_y:
return False
elif px >= self.max_x:
return False
elif py >= self.max_y:
return False
# collision check
if self.obstacle_map[node.x][node.y]:
return False
return True
def calc_obstacle_map(self, ox, oy):
self.min_x = round(min(ox))
self.min_y = round(min(oy))
self.max_x = round(max(ox))
self.max_y = round(max(oy))
print("min_x:", self.min_x)
print("min_y:", self.min_y)
print("max_x:", self.max_x)
print("max_y:", self.max_y)
self.x_width = round((self.max_x - self.min_x) / self.resolution)
self.y_width = round((self.max_y - self.min_y) / self.resolution)
print("x_width:", self.x_width)
print("y_width:", self.y_width)
# obstacle map generation
self.obstacle_map = [[False for _ in range(self.y_width)]
for _ in range(self.x_width)]
for ix in range(self.x_width):
x = self.calc_grid_position(ix, self.min_x)
for iy in range(self.y_width):
y = self.calc_grid_position(iy, self.min_y)
for iox, ioy in zip(ox, oy):
d = math.hypot(iox - x, ioy - y)
if d <= self.rr:
self.obstacle_map[ix][iy] = True
break
@staticmethod
def get_motion_model():
# dx, dy, cost
motion = [[1, 0, 841/925],
[0, 1, 1],
[-1, 0, 841/925],
[0, -1, 1],
[-1, -1, np.sqrt(841**2+925**2)/925],
[-1, 1, np.sqrt(841**2+925**2)/925],
[1, -1, np.sqrt(841**2+925**2)/925],
[1, 1, np.sqrt(841**2+925**2)/925]]
# motion = [[0, 1, 841 / 925],
# [1, 0, 1],
# [0, -1, 841 / 925],
# [-1, 0, 1],
# [-1, -1, 1250 / 925],
# [1, -1, 1250 / 925],
# [-1, 1, 1250 / 925],
# [1, 1, 1250 / 925]]
return motion
def main():
print(__file__ + " start!!")
# # start and goal position
sx = 165 # [m]
sy = 37 # [m]145 29
gx =26 # [m]
gy = 74 # [m]31,101
# sx = 470 # [m]
# sy = 88 # [m]145 29
# gx = 84 # [m]
# gy =242 # [m]31,101
grid_size = 2.0 # [m]
robot_radius = 1.0 # [m]
ox, oy = [], []
# obstacle_x = []
# obstacle_y = []
rows, cols = data.shape
for i in range(1, rows):
for j in range(1, cols):
if data[i][j] == 0: # 检查是否为空
oy.append(i) # y坐标
ox.append(j) # x坐标
if show_animation: # pragma: no cover
plt.plot(ox, oy, ".k")
plt.plot(sx, sy, "og")
plt.plot(gx, gy, "xb")
plt.grid(True)
plt.axis("equal")
a_star = AStarPlanner(ox, oy, grid_size, robot_radius)
rx, ry = a_star.planning(sx, sy, gx, gy,data,data1,data3,data4)
if show_animation: # pragma: no cover
plt.plot(rx, ry, "-r")
plt.pause(0.001)
plt.show()
if __name__ == '__main__':
main() 分析一下这段代码，告诉哪些地方设置的不合理或者有问题
最新发布
本文介绍了一道关于机器人路径模拟的编程题目。该题目要求通过分析预设的指令网格，判断机器人是否能成功退出网格或者陷入循环，并给出了具体的实现代码。
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