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voronoi_API.py
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import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial import Delaunay
## 函数传入参数：
## 机器人的数量: agent_num （int整型）
## 机器人的坐标: agent_pos = [[e_x, e_y], [p1_x, p2_y], ...] 索引值0对应被抓捕者坐标 （np数组形式）
## 边界坐标: bound = [[left_down_x, left_down_y], [right_up_x, right_up_y]] (np数组形式)
## 逐步着最大速度值: max_v
## 决策频率: frequency
## 函数输出:
## 抓捕者的速度: pursuer_v = [[p1_v_x, p1_v_y], ...] 索引值n对应n-1号抓捕者
def pursuer_decision(agent_num, agent_pos, bound, max_v, frequency):
tri = Delaunay(agent_pos)
circle = []
tri_lines = []
bounding_box = [bound[0][0], bound[1][0], bound[0][1], bound[1][1]]
def get_outer_circle(A, B, C):
xa, ya = A[0], A[1]
xb, yb = B[0], B[1]
xc, yc = C[0], C[1]
xab, yab = (xa + xb) / 2.0, (ya + yb) / 2.0
xbc, ybc = (xb + xc) / 2.0, (yb + yc) / 2.0
if (xb != xa):
kab = (yb - ya) / (xb - xa)
else:
kab = None
if (xc != xb):
kbc = (yc - yb) / (xc - xb)
else:
kbc = None
if (kab != None):
ab = np.arctan(kab)
else:
ab = np.pi / 2
if (kbc != None):
bc = np.arctan(kbc)
else:
bc = np.pi / 2
if (ab == 0):
kabm = None
b1 = 0
x = xab
else:
kabm = np.tan(ab + np.pi / 2)
b1 = yab * 1.0 - xab * kabm * 1.0
if (bc == 0):
kbcm = None
b2 = 0
x = xbc
else:
kbcm = np.tan(bc + np.pi / 2)
b2 = ybc * 1.0 - xbc * kbcm * 1.0
if (kabm != None and kbcm != None):
x = (b2 - b1) * 1.0 / (kabm - kbcm)
if (kabm != None):
y = kabm * x * 1.0 + b1 * 1.0
else:
y = kbcm * x * 1.0 + b2 * 1.0
r = np.sqrt((x - xa) ** 2 + (y - ya) ** 2)
return (x, y, r)
for num in range(0, tri.simplices.shape[0]):
plt.axis('equal')
plt.axis('off')
x, y, r = get_outer_circle(agent_pos[tri.simplices[num][0]], agent_pos[tri.simplices[num][1]],
agent_pos[tri.simplices[num][2]])
circle.append([x, y])
tri.simplices[num].sort()
tup = (tri.simplices[num][0], tri.simplices[num][1])
tri_lines.append(tup)
tup = (tri.simplices[num][0], tri.simplices[num][2])
tri_lines.append(tup)
tup = (tri.simplices[num][1], tri.simplices[num][2])
tri_lines.append(tup)
i = 0
dic = dict()
for tri_line in tri_lines:
if tri_line in dic.keys():
dic[tri_lines[i]].append(int(i) // int(3))
i = i + 1
else:
dic[tri_lines[i]] = [int(i) // int(3)]
i = i + 1
voronoi_graph = dict()
def get_intersect_point(a, b, c, bound):
flag = 0
x1 = y1 = x2 = y2 = 0
if b == 0:
x1 = x2 = -c / a
y1 = bound[2]
y2 = bound[3]
else:
# 斜率存在
if (-c - a * bound[0]) / b <= bound[3] and (-c - a * bound[0]) / b >= bound[2]:
# print("线和x=bound[0]存在符合要求的交点")
if flag == 0:
x1 = bound[0]
y1 = (-c - a * bound[0]) / b
flag = 1
else:
x2 = bound[0]
y2 = (-c - a * bound[0]) / b
flag = 2
if (-c - a * bound[1]) / b <= bound[3] and (-c - a * bound[1]) / b >= bound[2]:
# print("线和x=bound[1]存在符合要求的交点")
if flag == 0:
x1 = bound[1]
y1 = (-c - a * bound[1]) / b
flag = 1
else:
# 找到过符合要求的交点
x2 = bound[1]
y2 = (-c - a * bound[1]) / b
flag = 2
if (-c - b * bound[2]) / a <= bound[1] and (-c - b * bound[2]) / a >= bound[0]:
# print("线和y=bound[2]存在符合要求的交点")
if flag == 0:
y1 = bound[2]
x1 = (-c - b * bound[2]) / a
flag = 1
else:
y2 = bound[2]
x2 = (-c - b * bound[2]) / a
flag = 2
if (-c - b * bound[3]) / a <= bound[1] and (-c - b * bound[3]) / a >= bound[0]:
# print("线和y=bound[3]存在符合要求的交点")
if flag == 0:
y1 = bound[3]
x1 = (-c - b * bound[3]) / a
flag = 1
else:
y2 = bound[3]
x2 = (-c - b * bound[3]) / a
flag = 2
if flag == 1:
# 只存在一个交点
x2 = x1
y2 = y1
return flag, x1, y1, x2, y2
def intersect(A, B, bound):
C = [0, 0]
if A[0] >= bound[0] and A[0] <= bound[1] and A[1] >= bound[2] and A[1] <= bound[3]:
if B[0] >= bound[0] and B[0] <= bound[1] and B[1] >= bound[2] and B[1] <= bound[3]:
flag = 1;
return A[0], A[1], B[0], B[1], flag
else:
flag = 1
if (A[0] == B[0]):
if (B[1] > bound[3]):
x = A[0]
y = bound[3]
else:
x = A[0]
y = bound[2]
C[0] = x
C[1] = y
else:
a = A[1] - B[1]
b = B[0] - A[0]
c = B[1] * A[0] - A[1] * B[0]
num, x1, y1, x2, y2 = get_intersect_point(a, b, c, bound)
if x1 >= min(A[0], B[0]) and x1 <= max(A[0], B[0]) and y1 >= min(A[1], B[1]) and y1 <= max(A[1],
B[1]):
C[0] = x1
C[1] = y1
else:
C[0] = x2
C[1] = y2
return A[0], A[1], C[0], C[1], flag
else:
if B[0] >= bound[0] and B[0] <= bound[1] and B[1] >= bound[2] and B[1] <= bound[3]:
flag = 1
if (A[0] == B[0]):
if (A[1] > bound[3]):
x = B[0]
y = bound[3]
else:
x = B[0]
y = bound[2]
C = [x, y]
else:
a = A[1] - B[1]
b = B[0] - A[0]
c = B[1] * A[0] - A[1] * B[0]
num, x1, y1, x2, y2 = get_intersect_point(a, b, c, bound)
if x1 >= min(A[0], B[0]) and x1 <= max(A[0], B[0]) and y1 >= min(A[1], B[1]) and y1 <= max(A[1],
B[1]):
C[0] = x1
C[1] = y1
else:
C[0] = x2
C[1] = y2
return B[0], B[1], C[0], C[1], flag
else:
flag = 0
if (A[0] == B[0]):
return A[0], A[1], B[0], B[1], flag
else:
a = A[1] - B[1]
b = B[0] - A[0]
c = B[1] * A[0] - A[1] * B[0]
num, x1, y1, x2, y2 = get_intersect_point(a, b, c, bound)
if num > 0:
return x1, y1, x2, y2, flag
else:
return A[0], A[1], B[0], B[1], flag
def IsIntersec(p1, p2, p3, p4):
a = p2[1] - p1[1]
b = p1[0] - p2[0]
c = p2[0] * p1[1] - p1[0] * p2[1]
# print(a, b, c)
if (a * p3[0] + b * p3[1] + c) * (a * p4[0] + b * p4[1] + c) <= 0:
return 1
else:
return 0
def midline(A, B, C, bound):
a = 2 * (B[0] - A[0])
b = 2 * (B[1] - A[1])
c = A[0] ** 2 - B[0] ** 2 + A[1] ** 2 - B[1] ** 2
num, x1, y1, x2, y2 = get_intersect_point(a, b, c, bound)
D = [x1, y1]
if IsIntersec(A, B, C, D):
D = [x1, y1]
else:
D = [x2, y2]
return D
for key, value in dic.items():
if len(value) == 2:
x1, y1, x2, y2, flag = intersect(circle[value[0]], circle[value[1]], bounding_box)
voronoi_graph[key] = [[x1, y1], [x2, y2], flag]
else:
for i in range(0, 3):
if (tri.simplices[value[0]][i] != key[0] and tri.simplices[value[0]][i] != key[1]):
peak = [agent_pos[tri.simplices[value[0]][i]][0], agent_pos[tri.simplices[value[0]][i]][1]]
break
if circle[value[0]][0] < bounding_box[0] or circle[value[0]][0] > bounding_box[1] or circle[value[0]][
1] < \
bounding_box[2] or circle[value[0]][1] > bounding_box[3]:
x1, y1, x2, y2, flag = intersect(circle[value[0]],
midline(agent_pos[key[0]], agent_pos[key[1]], peak, bounding_box),
bounding_box)
else:
x1, y1 = circle[value[0]][0], circle[value[0]][1]
x2, y2 = midline(agent_pos[key[0]], agent_pos[key[1]], peak, bounding_box)
flag = 1
voronoi_graph[key] = [[x1, y1], [x2, y2], flag]
neighbor = []
unneighbor = []
for tri_line in tri_lines:
if (tri_line[0] == 0 or tri_line[1] == 0):
if tri_line[1] + tri_line[0] not in neighbor:
if voronoi_graph[tri_line][2] != 0:
if voronoi_graph[tri_line][0][0] != voronoi_graph[tri_line][1][0] or voronoi_graph[tri_line][0][
1] != voronoi_graph[tri_line][1][1]:
neighbor.append(tri_line[1] + tri_line[0])
for i in range(1, agent_num):
if i not in neighbor:
unneighbor.append(i)
vp = []
for i in range(1, agent_num):
if i in neighbor:
mid = np.array([(voronoi_graph[(0, i)][0][0] + voronoi_graph[(0, i)][1][0]) / 2,
(voronoi_graph[(0, i)][0][1] + voronoi_graph[(0, i)][1][1]) / 2])
vp.append((mid - agent_pos[i]) * max_v / (np.sqrt(np.sum(np.square(mid - agent_pos[i]))) * frequency) )
else:
vp.append((agent_pos[0] - agent_pos[i]) * max_v / (np.sqrt(np.sum(np.square(agent_pos[0] - agent_pos[i]))) * frequency) )
pursuer_v = np.array(vp)
return pursuer_v