def run_animation(all_sheep, sheep_dict, herd):
step = 0
target = np.array([600, 600])
r_dist = 250
r_rep = 14
speed = 2
n = len(all_sheep)
app_dist = n + 50
radius = math.sqrt(n) * r_rep
last_vector = np.zeros((n, 2), dtype=np.float32)
while True:
herd_point = herd.position2point().copy()
if common.check(all_sheep, radius):
shepherdR.driving(herd, all_sheep, speed, target, app_dist)
else:
shepherdR.collecting(herd, all_sheep, speed, app_dist)
sheepR.sheep_move(herd_point, all_sheep, r_dist, r_rep, speed,
sheep_dict, last_vector)
tk.update()
time.sleep(0.01)
if common.is_all_in_target(all_sheep) or step > 4000:
for per_sheep in sheep_dict.values():
per_sheep.delete()
herd.delete()
return common.calculate_dispersion(all_sheep)
break
step += 1
return step
y + Ra / 2, colors[1])
X = np.array(X)
shepherd = Agent(560, 560, 560 + Ra, 560 + Ra, 'red')
shepherd_point = shepherd.position2point()
step = 0
global_mean = np.array([np.mean(X[:, 0]), np.mean(X[:, 1])])
"""自适应模式切换"""
while True:
if check(X, global_mean):
X, global_mean, shepherd_point = driving(shepherd_point, target, X,
global_mean)
else:
X, global_mean, shepherd_point, = collecting(
shepherd_point, X, global_mean)
if all_sheeps_in(X) or step > 4000:
disp = common.calculate_dispersion(X)
print("dispersion: ", disp)
Y.append(disp)
break
tk.update()
time.sleep(0.01)
step += 1
for i in range(N): # 每一轮都要先删除 再重建 删除羊
agent['sheep' + str(i)].delete()
shepherd.delete() # 删除牧羊犬
# 打印数据
Y = np.array(Y)
print("[", end="")
for y in Y:
print(",", end="")
def run_animation(all_sheep, sheep_dict, herd):
step = 0
target = np.array([600, 600])
r_dist = 250
r_rep = 14
speed = 2
n = len(all_sheep)
app_dist = n + 50
theta = math.pi / 4.5
fn = math.sqrt(n) * r_rep
last_vector = np.zeros((n, 2), dtype=np.float32)
# 处理轨迹
UU = []
VV = []
XX = []
YY = []
Px = {}
Py = {}
for i in range(n):
Px['coor' + str(i)] = []
Py['coor' + str(i)] = []
for i in range(n):
Px['coor' + str(i)].append(all_sheep[i][0])
Py['coor' + str(i)].append(all_sheep[i][1])
dist_center = 0
dist_shepherd = 0
pre_mean = np.array([np.mean(all_sheep[:, 0]), np.mean(all_sheep[:, 1])])
pre_herd = herd.position2point()
while True:
herd_point = herd.position2point().copy()
if common.check_sector(all_sheep, theta, target) and common.check_dist(
all_sheep, target, fn):
shepherdR.driving(herd, all_sheep, speed, target, app_dist)
else:
shepherdR.collecting(herd, all_sheep, speed, app_dist, target)
sheepR.sheep_move(herd_point, all_sheep, r_dist, r_rep, speed,
sheep_dict, last_vector)
tk.update()
time.sleep(0.01)
if common.is_all_in_target(all_sheep) or step > 4000:
for per_sheep in sheep_dict.values():
per_sheep.delete()
herd.delete()
print("dispersion: ", common.calculate_dispersion(all_sheep))
break
# 处理轨迹
global_mean = np.array(
[np.mean(all_sheep[:, 0]),
np.mean(all_sheep[:, 1])])
dist_center += la.norm(pre_mean - global_mean)
pre_mean = global_mean
dist_shepherd += la.norm(pre_herd - herd.position2point())
pre_herd = herd.position2point()
for i in range(n):
Px['coor' + str(i)].append(all_sheep[i][0])
Py['coor' + str(i)].append(all_sheep[i][1])
XX.append(herd.position2point()[0])
YY.append(herd.position2point()[1])
UU.append(global_mean[0])
VV.append(global_mean[1])
step += 1
xx = np.array(XX)
yy = np.array(YY)
common.print_list(xx)
common.print_list(yy)
return step
def run_animation(all_sheep, sheep_dict, herd):
step = 0
# 目标值修改了 但是判定条件没有跟着修改
target = np.array([300, 300], np.float64)
r_dist = 250
r_rep = 14
speed = 2
n = len(all_sheep)
app_dist = n + 50
radius = math.sqrt(n) * r_rep
last_vector = np.zeros((n, 2), dtype=np.float32)
# 处理轨迹
UU = []
VV = []
XX = []
YY = []
Px = {}
Py = {}
for i in range(n):
Px['coor' + str(i)] = []
Py['coor' + str(i)] = []
for i in range(n):
Px['coor' + str(i)].append(all_sheep[i][0])
Py['coor' + str(i)].append(all_sheep[i][1])
dist_center = 0
dist_shepherd = 0
pre_mean = np.array([np.mean(all_sheep[:, 0]), np.mean(all_sheep[:, 1])])
pre_herd = herd.position2point()
target_radius = 75
while True:
herd_point = herd.position2point().copy()
if common.check(all_sheep, radius):
shepherdR.driving(herd, all_sheep, speed, target, app_dist)
text = canvas.create_text(220, 20, text="driving", font=("宋体", 18))
canvas.pack()
else:
shepherdR.collecting(herd, all_sheep, speed, app_dist)
text = canvas.create_text(220,
20,
text="collecting",
font=("宋体", 18))
canvas.pack()
sheepR.sheep_move(herd_point, all_sheep, r_dist, r_rep, speed,
sheep_dict, last_vector)
step += 1
step_txt = canvas.create_text(420,
20,
text="steps: {}/2000".format(step),
font=("宋体", 18))
tk.update()
time.sleep(0.01)
canvas.delete(text)
canvas.delete(step_txt)
if common.is_all_in_center(all_sheep, target,
target_radius) or step > 4000:
for per_sheep in sheep_dict.values():
per_sheep.delete()
herd.delete()
print("dispersion: ", common.calculate_dispersion(all_sheep))
break
# 处理轨迹
global_mean = np.array(
[np.mean(all_sheep[:, 0]),
np.mean(all_sheep[:, 1])])
dist_center += la.norm(pre_mean - global_mean)
pre_mean = global_mean
dist_shepherd += la.norm(pre_herd - herd.position2point())
pre_herd = herd.position2point()
for i in range(n):
Px['coor' + str(i)].append(all_sheep[i][0])
Py['coor' + str(i)].append(all_sheep[i][1])
XX.append(herd.position2point()[0])
YY.append(herd.position2point()[1])
UU.append(global_mean[0])
VV.append(global_mean[1])
# 处理轨迹
fig, ax = plt.subplots()
xx = np.array(XX)
yy = np.array(YY)
uu = np.array(UU)
vv = np.array(VV)
for i in range(n):
plt.plot(np.array(Px['coor' + str(i)]),
600 - np.array(Py['coor' + str(i)]),
linewidth=0.1,
c='silver')
plt.plot(xx, 600 - yy, c='darkkhaki', label='shepherd', linewidth=2)
plt.plot(uu,
600 - vv,
'-.',
c='darkcyan',
label='sheep center',
linewidth=2)
plt.legend()
y = np.arange(0, 600, 100)
plt.xticks(y)
plt.yticks(y)
plt.xlabel("X Position")
plt.ylabel("Y Position")
plt.show()
fig.savefig('E:\\我的坚果云\\latex\\doubleDistSum\\pics\\SPPL_trial60.pdf',
dpi=600,
format='pdf')
## 输出距离
print("dist_center:", dist_center)
print("dist_shepherd:", dist_shepherd)
return step
def run_animation(all_sheep, sheep_dict, herd):
step = 0
target = np.array([600, 600], np.float64)
r_dist = 250
r_rep = 14
speed = 2
n = len(all_sheep)
app_dist = n + 50
theta = cmath.pi / 6
fn = cmath.sqrt(n) * r_rep
last_vector = np.zeros((n, 2), dtype=np.float32)
# 处理轨迹
UU = []
VV = []
XX = []
YY = []
Px = {}
Py = {}
for i in range(n):
Px['coor' + str(i)] = []
Py['coor' + str(i)] = []
for i in range(n):
Px['coor' + str(i)].append(all_sheep[i][0])
Py['coor' + str(i)].append(all_sheep[i][1])
dist_center = 0
dist_shepherd = 0
pre_mean = np.array([np.mean(all_sheep[:, 0]), np.mean(all_sheep[:, 1])])
pre_herd = herd.position2point()
while True:
herd_point = herd.position2point().copy()
if common.check_sector(all_sheep, theta, target) and common.check_dist(
all_sheep, target, fn):
shepherdR.driving(herd, all_sheep, speed, target, app_dist)
else:
shepherdR.collecting(herd, all_sheep, speed, app_dist, target)
sheepR.sheep_move(herd_point, all_sheep, r_dist, r_rep, speed,
sheep_dict, last_vector)
tk.update()
time.sleep(0.01)
if common.is_all_in_target(all_sheep) or step > 4000:
for per_sheep in sheep_dict.values():
per_sheep.delete()
herd.delete()
print("dispersion: ", common.calculate_dispersion(all_sheep))
break
# 处理轨迹
global_mean = np.array(
[np.mean(all_sheep[:, 0]),
np.mean(all_sheep[:, 1])])
dist_center += la.norm(pre_mean - global_mean)
pre_mean = global_mean
dist_shepherd += la.norm(pre_herd - herd.position2point())
pre_herd = herd.position2point()
for i in range(n):
Px['coor' + str(i)].append(all_sheep[i][0])
Py['coor' + str(i)].append(all_sheep[i][1])
XX.append(herd.position2point()[0])
YY.append(herd.position2point()[1])
UU.append(global_mean[0])
VV.append(global_mean[1])
step += 1
# 处理轨迹
fig, ax = plt.subplots()
xx = np.array(XX)
yy = np.array(YY)
uu = np.array(UU)
vv = np.array(VV)
for i in range(n):
plt.plot(np.array(Px['coor' + str(i)]),
600 - np.array(Py['coor' + str(i)]),
linewidth=0.1,
c="silver")
plt.plot(xx, 600 - yy, c='darkkhaki', label='shepherd', linewidth=3)
plt.plot(uu,
600 - vv,
'-.',
c='darkcyan',
label='sheep center',
linewidth=2)
plt.legend()
y = np.arange(0, 600, 100)
plt.xticks(y)
plt.yticks(y)
plt.xlabel("X Position")
plt.ylabel("Y Position")
plt.show()
fig.savefig('E:\\我的坚果云\\latex\\doubleDistSum\\pics\\MDAF_trial60.pdf',
dpi=600,
format='pdf')
## 输出距离
print("dist_center:", dist_center)
print("dist_shepherd:", dist_shepherd)
return step