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ergodicDemo.py
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ergodicDemo.py
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import numpy as np
import time
import matplotlib.pyplot as plt
from gridworld import Grid
import gridworld
from AcousticTag import AcousticTag
from Agent import Agent
from AcousticReciever import AcousticReciever
import socket
import threading
def ComLink():
global u, run, updateGP,t
run=True
sock.send(str.encode(str(x_range)))
while run:
agent=agentList[0]
st=agent.state
bin=E.getAbstractPos(st[0],st[1])-1
try:
if not updateGP:
sock.send(str.encode(str(round(st[0],1)/x_range)+" "+str(round(st[1],1)/x_range)+" "+str(st[2])+" "+str(st[3])+" "+str(t)+" "+"None "))
else:
updateGP=False
sock.send(str.encode(str(round(st[0],1)/x_range)+" "+str(round(st[1],1)/x_range)+" "+str(st[2])+" "+str(st[3])+" "+str(t)+" "+str(latestMeas)))
except:
run=False
t=simtime
data = sock.recv(1024)
data = data.decode('utf-8')
cmd=data.split(',')
if len(cmd)>1:
u=(float(cmd[0]),float(cmd[1]))
if st[0]>x_range or st[0]<0 or st[1]<0 or st[1]>y_range:
_,utemp=wp_track(agent.getPos(),np.array([x_range/2,y_range/2]))
u=np.clip(np.array([np.cos(utemp), np.sin(utemp)]),-0.1,0.1)
#print(st,u)
print(t,round(st[0],1),round(st[1],1),round(st[2],3),round(st[3],3),u)
'''
psuedo code
create grid world
generate tag positons based on probability map and total number of fish N
create agents
simulation:
1. detect tags
2. update tag.lastPing
3. updte agent dynamics
'''
def draw(x):
plt.figure(1)
plt.axis('scaled')
plt.grid(True)
plt.plot(x[0], x[1], 'r.')
plt.xlim([0, E.x_range])
plt.ylim([0, E.y_range])
plt.xticks(np.arange(0,E.x_range,E.x_range/5.0))
plt.yticks(np.arange(0,E.y_range,E.y_range/5.0))
plt.draw()
def drawAgent(x,r=None):
plt.figure(1)
plt.axis('scaled')
plt.grid(True)
plt.plot(x[0], x[1], 'bo')
if r==None:
pass
else:
circ=plt.Circle((x[0], x[1]), r, color='b', fill=False)
plt.gcf().gca().add_artist(circ)
plt.xlim([0, E.x_range])
plt.ylim([0, E.y_range])
plt.xticks(np.arange(0,E.x_range,E.x_range/5.0))
plt.yticks(np.arange(0,E.y_range,E.y_range/5.0))
plt.draw()
def iterative_average(x,n,ave):
return ave+(x-ave)/(n+1)
def simulate_dynamics(agent,u,tspan,dt):
inc = agent.state
for i in np.linspace(tspan[0],tspan[1],int((tspan[1]-tspan[0])/dt)):
inc+=agent.dynamics(inc,u)*dt#+world.flow(agent.getPos())*dt
return inc
def f1_plan(x0, x, N):
u = np.zeros(N)
xsim = x0.copy()
for n in range(N):
e = x-xsim
angle = np.arctan2(e[1], e[0])
u[n] = angle
xsim += 0.005*np.array([np.cos(u[n]), np.sin(u[n])])
return u
def search(wp_list,X):
#X is the center position of one of the 25 cells
offset=.375*x_range
wp_list.append(np.array(X))
wp_list.append(np.array(X)+np.array([-offset,offset]))
wp_list.append(np.array(X)+np.array([offset,offset]))
wp_list.append(np.array(X)+np.array([offset,-offset]))
wp_list.append(np.array(X)+np.array([-offset,-offset]))
wp_list.append(np.array(X))
return wp_list
def wp_track(x,wp_list):
global searchComplete
e = np.array(wp_list[0])-x
if np.linalg.norm(e) < 5 and len(wp_list) > 1:
#print(wp_list)
del wp_list[0]
if len(wp_list) == 1:
searchComplete=True
return wp_list, 1*np.arctan2(e[1],e[0])
def m2_step(x,u):
# |0 0 1 0|x |0 0|
# |0 0 0 1|y + |0 0|u1
# |0 0 -a 0|vx |1 0|u2
# |0 0 0 -b|vy |0 1|
a=0
return np.matmul(1*np.array([[0, 0, 1, 0],[0,0,0,1],[0,0,-a,0],[0,0,0,-a]]),x)+np.matmul(1*np.array([[0,0],[0,0],[1,0],[0,1]]),u)
def m1_step(x,u):
return u[0]*np.array([np.cos(u[1]), np.sin(u[1])])
density_map = np.array([0.1, 0.1, 0.4, 0.3, 0.2,
0.1, 0.3, 0.3, 0.1, 0.3,
0.2, 0.3, 0.3, 0.2, 0.1,
0.3, 0.9, 0.3, 0.2, 0.1,
0.2, 0.3, 0.2, 0.1, 0.1])
# density_map = np.array([0.1, 0.1, 0.4, 0.3, 0.2,
# 0.1, 0.9, 0.3, 0.1, 0.3,
# 0.2, 0.3, 0.3, 0.2, 0.1,
# 0.3, 0.3, 0.3, 0.2, 0.1,
# 0.2, 0.3, 0.2, 0.1, 0.1])
# density_map = np.array([0.1, 0.1, 0.4, 0.3, 0.2,
# 0.1, 0.3, 0.3, 0.1, 0.3,
# 0.2, 0.3, 0.9, 0.2, 0.1,
# 0.3, 0.1, 0.3, 0.2, 0.1,
# 0.2, 0.3, 0.2, 0.1, 0.1])
N = 1000 #how many tags present
simtime=1500 #max simulation time
numAgents=1 #number of agents exploring
sensorRange=2
x_range=20.0
y_range=20.0
measurement_time = 2
time_step=.5
t=0
last_meas=t
run=False
running=False
searchComplete=False
updateGP=False
latestMeas=0
taglist=[]
agentList=[]
tagx=np.zeros(N)
tagy=np.zeros(N)
for i in range(N):
#taglist.append(AcousticTag(i,last_ping=np.random.randn()),ping_delay=max(2,30*np.random.randn())) # most realistic
taglist.append(AcousticTag(i,last_ping=17*np.random.randn())) # more realistic (pings are not aligned in time)
#taglist.append(AcousticTag(i)) #better for understanding because pings are aligned in time and all have same ping interval
x,y,_ = taglist[i].pos
tagx[i]=x
tagy[i]=y
E = Grid(taglist,x_range=x_range, y_range=y_range)
E.setMap(density_map)
for i in range(numAgents):
s= AcousticReciever(np.array([0,0,0]),sensorRange)
agentList.append(Agent(np.array([np.random.rand()*x_range,np.random.rand()*y_range,0,0]),s,E,dim=2))
agentList[i].dynamics=m2_step
u=[0,0]
for i in range(N):
x,y,_ = taglist[i].pos
tagx[i]=x
tagy[i]=y
#draw((tagx,tagy))
'''
for t in range(N):
draw(taglist[t].pos)
#plt.pause(.1)
'''
# simulation
#input('Enter to begin simulation')
########################################################################
# create an INET, STREAMing socket
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# connect to ergodic controller
sock.connect(('localhost', 8080))
# now do something with the clientsocket
# in this case, we'll pretend this is a threaded server
x = threading.Thread(target=ComLink)
x.start()
##########################################################################
det_count=[0,0,0]
while t<=simtime:#or running: #change to better simulation stopping criteria
posx=np.zeros(numAgents)
posy=np.zeros(numAgents)
for i in range(len(agentList)):
agent=agentList[i]
pos=agent.getPos()
#state = agent.getPos()#
#srange=agent.sensor.range
state=simulate_dynamics(agent,u, [0,time_step],.1)
dets=agent.updateAgent(state,t)
det_count[i]+=dets
if last_meas+measurement_time<=t:
updateGP = True
bin=E.getAbstractPos(pos[0],pos[1])-1
dtSet=agent.sensor.detectionSet
rate_meas = len(dtSet)*1.0/measurement_time
latestMeas=rate_meas
agent.belief_count[bin]+=1
agent.belief_map[bin]= iterative_average(rate_meas,agent.belief_count[bin],round(agent.belief_map[bin],3)) #iteratively average rate measurement
if len(agent.sensor.detectionSet)>0:
#print("agent ",i,", rate = ",rate_meas,",average rate = ",agent.belief_map[bin], " in bin ", bin)
#print(last_meas,t,dtSet)
agent.sensor.detectionSet=set()
posx[i]=pos[0]
posy[i]=pos[1]
if last_meas+measurement_time<=t:
last_meas=t
for tag in taglist:
tag.updatePing(t)
t+=time_step
plt.clf()
draw((tagx,tagy))
drawAgent((posx,posy),r=sensorRange)
plt.pause(0.00001)#plt.pause(time_step)
run=False
sock.send("end ".encode('utf-8'))
for i in range(len(agentList)):
agent=agentList[i]
print("agent ",i," rate estimates")
agent.belief_map.shape=(5,5)
print(np.flip(agent.belief_map,0))
print("and measurements taken per cell")
agent.belief_count.shape=(5,5)
print(np.flip(agent.belief_count,0))
print("True probability density map")
E.p.shape=(5,5)
print(np.flip(E.p,0))
#spacing=(50,50)
spacing=(1,1)
print("Rate field approximation for sensor with range",sensorRange," spaced at intervals of",spacing)
approx,pnts=E.approximateField(measurement_time,spacing=spacing,sensorRange=sensorRange,get_points=True)
#print(np.round(approx,decimals=2))
plt.figure(2)
plt.axis('scaled')
plt.grid(True)
#print('\n',pnts[:,:,0],'\n',pnts[:,:,1])
#plt.plot(pnts[:,:,0].flatten(), pnts[:,:,1].flatten(), 'r.',cmap='coolwarm')
plt.contourf(pnts[:,:,0], pnts[:,:,1], np.flip(np.round(approx,decimals=2),(0,1)).transpose(), 20, cmap='coolwarm')# cmap='inferno'), cmap='RdGy')
cbar = plt.colorbar()
cbar.set_label('Detection rate')
plt.xlim([0, x_range])
plt.ylim([0, y_range])
plt.xticks(np.arange(0,x_range,spacing[0]))
plt.yticks(np.arange(0,y_range,spacing[1]))
plt.draw()
plt.pause(0.00001)
input('done')