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Collision-Avoidance.py
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Collision-Avoidance.py
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import torch,os
from braincog.model_zoo.rsnn import RSNN
from random import randint
import math
import random
import matplotlib
# matplotlib.use("TkAgg")
import numpy as np
import random
import matplotlib.pyplot as plt
import matplotlib.animation as animation
#os.environ["SDL_VIDEODRIVER"] = "dummy"
#parameters
N =10
WORLD_WIDTH = 500
COLLISION_THRE =25 #60 65 70
WALL_COLLISION_LIMIT=10
VISIBLE_THRE=75 #3=75/COLLISION_THRE #3*COLLISION_THRE
#eight velocity
vel_space=[[0,1],[1,0],[0,-1],[-1,0],[1,1],[1,-1],[-1,-1],[-1,1]]
vel_x_small=[[0,1],[1,0],[0,-1],[1,1],[1,-1]]
vel_x_large=[[0,1],[0,-1],[-1,0],[-1,-1],[-1,1]]
vel_y_small=[[0,1],[1,0],[-1,0],[1,1],[-1,1]]
vel_y_large=[[1,0],[0,-1],[-1,0],[1,-1],[-1,-1]]
N_action=len(vel_space)
col_robot=[i for i in range(N)]
# parameters for rl+snn
C = 50
runtime = 100 # Runtime in ms for choosing action
# parameters for snn
tau = 10 # time constant of STDP
stdpwin = 10 # STDP windows in ms
Apos = 0.925
Aneg = 0.1
vr = 0 # Reset Potential
vt = 0.1 # Judge if the neurons fire or not
tau_m = 20
Rm = 0.5
tau_e = 5
# inhibition weight between output population
s_in = np.random.rand(N_action * C, N_action * C)
for i in range(N_action):
for j in range(C):
for k in range(C):
s_in[i * C + j][i * C + k] = 0
#init boids with no collision
global boids
boids = np.zeros(N, dtype=[('pos', int, 2), ('vel', int, 2),('nn',RSNN)])
list_rand=[i for i in range(16)]
rand_int=random.sample(list_rand,N)
for i in range(len(rand_int)):
boids['pos'][i,0]=np.random.uniform(int(rand_int[i]%4)*125,(int(rand_int[i]%4)+1)*125+1,1)
boids['pos'][i,1] = np.random.uniform(int(rand_int[i]/4) * 125, (int(rand_int[i]/4) + 1) * 125 + 1, 1)
boids['vel'] = np.random.uniform(-1, 2, (N, 2))
for i_vel in range(len(boids['vel'])):
boids['nn'][i_vel] = RSNN(N_action*2,N_action*C).cuda()
while(boids['vel'][i_vel][0]==0 and boids['vel'][i_vel][1]==0):
boids['vel'][i_vel] = np.random.uniform(-1, 2, (1, 2))
#update boids parameters
do_update=np.zeros(N)
distance_pre=np.zeros((N,N))
tmp_min_robot=[i for i in range(N)]
tmp_input=[i for i in range(N)]
sum_deta_tmp=np.zeros(N)
sum_deta_new=np.zeros(N)
trace_decay = 0.8
def chooseAct(Net,input,explore):
count_group = np.zeros(N_action)
count_output = np.zeros(N_action * C)
if explore==-1:
pass
else:
pass
for i_train in range(runtime):
out, dw = Net(input[:,i_train])
# rstdp
Net.weight_trace *= trace_decay
Net.weight_trace += dw[0][0]
count_output=count_output+np.array(out)
for i in range(N_action):
count_group[i]=count_output[i*C:(i+1)*C].sum()
if count_group.max()>C/2:
action=count_group.argmax()
return action,Net
# if t==runtime-2 and len(np.where(self.count_group==0)[0])!=len(self.count_group):
# self.action=self.count_group.argmax()
def update_boids(xs, ys, xvs, yvs,frame):
global distance_pre,col_c
# Matrix off position difference and distance
xdiff = np.add.outer(xs, -xs)
ydiff = np.add.outer(ys, -ys)
distance = np.sqrt(xdiff ** 2 + ydiff ** 2)
# Calculate the boids that are visible to every other boid -pi/2 to pi/2
visible = np.zeros((N, N))
dir = np.zeros((N, N))
col_c = WORLD_WIDTH * np.ones((N, 4))
dir_c = np.zeros((N, 4))
angle_towards = np.arctan2(-ydiff, -xdiff)
angle_vel = np.arctan2(yvs, xvs)
for i in range(N):
for j in range(N):
if (xvs[i] == 1 and yvs[i] == 0) or (xvs[i] == 1 and yvs[i] == 1) or (xvs[i] == 0 and yvs[i] == 1) or (
xvs[i] == 0 and yvs[i] == -1) or (xvs[i] == 1 and yvs[i] == -1):
if angle_towards[i][j] < angle_vel[i] + np.pi / 2 and angle_towards[i][j] > angle_vel[i] - np.pi / 2:
visible[i][j] = True
if angle_towards[i][j] > angle_vel[i] - np.pi / 2 and angle_towards[i][j] < angle_vel[i]:
dir[i][j]=1#right
if angle_towards[i][j] < angle_vel[i] + np.pi / 2 and angle_towards[i][j] >= angle_vel[i]:
dir[i][j] = 2#left
if xvs[i] == -1 and yvs[i] == 1:
if (angle_towards[i][j] > angle_vel[i] - np.pi / 2 and angle_towards[i][j] < np.pi) or (
angle_towards[i][j] > -np.pi and angle_towards[i][j] < angle_vel[i] - 1.5 * np.pi):
visible[i][j] = True
if angle_towards[i][j] > angle_vel[i] - np.pi / 2 and angle_towards[i][j] < angle_vel[i]:
dir[i][j] = 1
if (angle_towards[i][j] < np.pi and angle_towards[i][j] >= angle_vel[i]) or (
angle_towards[i][j] > -np.pi and angle_towards[i][j] < angle_vel[i] - 1.5 * np.pi):
dir[i][j] = 2
if xvs[i] == -1 and yvs[i] == 0:
if (angle_towards[i][j] > np.pi / 2 and angle_towards[i][j] < np.pi) or (
angle_towards[i][j] > -np.pi and angle_towards[i][j] < -np.pi / 2):
visible[i][j] = True
if angle_towards[i][j] > np.pi / 2 and angle_towards[i][j] < np.pi:
dir[i][j] = 1
if angle_towards[i][j] >= -np.pi and angle_towards[i][j] < -np.pi / 2:
dir[i][j] = 2
if xvs[i] == -1 and yvs[i] == -1:
if (angle_towards[i][j] > -np.pi and angle_towards[i][j] < -np.pi / 4) or (
angle_towards[i][j] > 0.75 * np.pi and angle_towards[i][j] < np.pi):
visible[i][j] = True
if (angle_towards[i][j] > 0.75 * np.pi and angle_towards[i][j] < np.pi) or (
angle_towards[i][j] > -np.pi and angle_towards[i][j] < angle_vel[i]):
dir[i][j] = 1
if angle_towards[i][j] >= angle_vel[i] and angle_towards[i][j] < -np.pi / 4:
dir[i][j] = 2
v_tmp = np.diag(np.diag(visible))
visible = visible - v_tmp
# the danger of collision, considering dis=6*collision
collision = np.clip(VISIBLE_THRE/COLLISION_THRE - distance / COLLISION_THRE, 0,VISIBLE_THRE/COLLISION_THRE) * visible # visible and in some distance 3*collision_thre
c_tmp = np.diag(np.diag(collision))
collision = collision - c_tmp
if len(np.where(yvs[np.where(ys < (VISIBLE_THRE/COLLISION_THRE)*WALL_COLLISION_LIMIT)] == -1)[0])>0:
wall_tmp=np.where(ys < (VISIBLE_THRE/COLLISION_THRE)*WALL_COLLISION_LIMIT)[0]
for i_wall in range(len(wall_tmp)):
if yvs[wall_tmp[i_wall]] == -1:
col_c[wall_tmp[i_wall], 0] = ys[wall_tmp[i_wall]]
if xvs[wall_tmp[i_wall]] >= 0:
dir_c[wall_tmp[i_wall], 0] = 1
else:
dir_c[wall_tmp[i_wall], 1] = 2
if len(np.where(xvs[np.where(xs < (VISIBLE_THRE/COLLISION_THRE)*WALL_COLLISION_LIMIT)]==-1)[0])>0:
wall_tmp = np.where(xs < (VISIBLE_THRE/COLLISION_THRE)*WALL_COLLISION_LIMIT)[0]
for i_wall in range(len(wall_tmp)):
if xvs[wall_tmp[i_wall]] == -1:
col_c[wall_tmp[i_wall], 1] = xs[wall_tmp[i_wall]]
if yvs[wall_tmp[i_wall]] >= 0:
dir_c[wall_tmp[i_wall], 1] = 2
else:
dir_c[wall_tmp[i_wall], 1] = 1
if len(np.where(yvs[np.where((WORLD_WIDTH - ys) < (VISIBLE_THRE/COLLISION_THRE) * WALL_COLLISION_LIMIT)] == 1)[0]) > 0:
wall_tmp = np.where((WORLD_WIDTH - ys) < (VISIBLE_THRE/COLLISION_THRE) * WALL_COLLISION_LIMIT)[0]
for i_wall in range(len(wall_tmp)):
if yvs[wall_tmp[i_wall]]==1:
col_c[wall_tmp[i_wall],2] =WORLD_WIDTH - ys[wall_tmp[i_wall]]
if xvs[wall_tmp[i_wall]]>=0:
dir_c[wall_tmp[i_wall],2]=2
else:
dir_c[wall_tmp[i_wall], 2] = 1
if len(np.where(xvs[np.where((WORLD_WIDTH - xs) < (VISIBLE_THRE/COLLISION_THRE)*WALL_COLLISION_LIMIT)] ==1)[0])>0:
wall_tmp=np.where((WORLD_WIDTH - xs) < (VISIBLE_THRE/COLLISION_THRE) * WALL_COLLISION_LIMIT)[0]
for i_wall in range(len(wall_tmp)):
if xvs[wall_tmp[i_wall]]==1:
col_c[wall_tmp[i_wall],3] =WORLD_WIDTH - xs[wall_tmp[i_wall]]
if yvs[wall_tmp[i_wall]]>=0:
dir_c[wall_tmp[i_wall],3]=1
else:
dir_c[wall_tmp[i_wall], 3] = 2
# print(col_c)
col_c_tmp = np.clip(VISIBLE_THRE/COLLISION_THRE - col_c / WALL_COLLISION_LIMIT, 0, VISIBLE_THRE/COLLISION_THRE)
deta_dis_tmp = distance - distance_pre
deta_dis = deta_dis_tmp * collision # <0 and small is the obstacle
collision=np.c_[collision, col_c_tmp]
deta_dis=np.c_[deta_dis, -col_c_tmp]
dir=np.c_[dir,dir_c]
# print(collision,deta_dis)
#for every agent, choose the approaching agent as input
for i in range(N):
if frame>1 and do_update[i]>0:
sum_deta_new[i] = (tmp_input[i] * collision[i][tmp_min_robot[i]]).sum()
# print(sum_deta_new[i] ,sum_deta_tmp[i] )
if sum_deta_new[i] < sum_deta_tmp[i] :
r=10*(sum_deta_tmp[i]-sum_deta_new[i])
else:
r=-10*(sum_deta_new[i]-sum_deta_tmp[i])
boids['nn'][i].UpdateWeight(r)
if frame > 0:
do_update[i] =0
if len(np.where(deta_dis[i] < 0)[0]) > 0:
do_update[i] += 1
# then get the velocity direction of objects and the distance between them as the network input
appro_index = np.where(deta_dis[i] < 0)[0] # the input is the approching directions and distances
# print(appro_index)
input = []
for j in range(len(appro_index)):
if appro_index[j]<=N-1:
xvs_input = xvs[appro_index[j]]
yvs_input = yvs[appro_index[j]]
input.append(vel_space.index([xvs_input, yvs_input]))
else:
vel_tmp=int(appro_index[j]%N)
input.append(vel_tmp)
dis_tmp=np.c_[distance,col_c]
weight = -1 * dis_tmp[i][np.where(deta_dis[i] < 0)]
# input=input[np.argmin(weight)]
if weight.max() - weight.min() == 0:
weight = np.random.randint(1, 5, weight.shape)
weight[0] = 4
else:
k = (4 - 1) / (weight.max() - weight.min())
weight = 1 + k * (weight - weight.min())
# print(input,weight)
I = np.zeros((N_action*2, runtime))
for j in range(len(input)):
# print(appro_index,input,appro_index[j],dir[i][appro_index[j]],input[j]*dir[i][appro_index[j]])
I[int(input[j]+N_action*(dir[i][appro_index[j]]-1))][0:runtime] = max(I[int(input[j]+N_action*(dir[i][appro_index[j]]-1))][0], weight[j])
if random.random()<0.7:
action_index,boids['nn'][i] = chooseAct(boids['nn'][i],I,-1)#exploitation
else:
action_index,boids['nn'][i] = chooseAct(boids['nn'][i],I, 1) #exploration
xvs[i] = vel_space[action_index][0]
yvs[i] = vel_space[action_index][1]
tmp_min_robot[i] = np.where(deta_dis[i] < 0)[0]
tmp_input[i] = weight
sum_deta_tmp[i] = (tmp_input[i] * collision[i][tmp_min_robot[i]]).sum()
xs+=xvs
ys+=yvs
xs=np.clip(xs,0,WORLD_WIDTH)
ys = np.clip(ys, 0, WORLD_WIDTH)
distance_pre = distance
if frame>=10000:
for i in range(N):
for j in range(N_action*2):
I = np.zeros((N_action * 2, runtime))
I[j][0:runtime]=4
a=chooseAct(boids['nn'][i],I,-1)
# print(a)
aaa=1
def animate(frame):
update_boids(boids['pos'][:, 0], boids['pos'][:, 1], boids['vel'][:, 0], boids['vel'][:, 1],frame)
scatter.set_offsets(boids['pos'])
scatter1.set_offsets(boids['pos'])
#build background
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(111)
ax1.set_title('Scatter Plot')
plt.xlim(-20,520)
plt.ylim(-20,520)
plt.grid(ls='--',c='gray')
plt.xlabel('X')
plt.ylabel('Y')
# Use a scatter plot to visualize the boids
color_list=['r','b','g','y','m','c','deeppink','tomato','gold','crimson','cornsilk','darkred','greenyellow','lightcoral','mintcream',
'rosybrown']
colors=color_list[0:N]
#colors=random.sample(color_list,N)
lines=np.zeros(N)+5
scatter = ax1.scatter(boids['pos'][:, 0], boids['pos'][:, 1],s=500,alpha=0.5,linewidths=lines)
scatter1 = ax1.scatter(boids['pos'][:, 0], boids['pos'][:, 1],s=2500,c=colors,alpha=0.5)
boids_newp=boids['pos']+boids['vel']*10
for i in range(N):
boids_linex=np.hstack((boids['pos'][i, 0],boids_newp[i,0]))
boids_liney=np.hstack((boids['pos'][i, 1],boids_newp[i,1]))
#line,=plt.plot(boids_linex,boids_liney,linewidth=5)
#lines = [ax1.plot(np.hstack((boids['pos'][i, 0],boids_newp[i,0])), np.hstack((boids['pos'][i, 1],boids_newp[i,1])),linewidth=5) for i in range(N)]
animation = animation.FuncAnimation(fig, animate,interval=0.001)
plt.show()