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braitenberg2D.py
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braitenberg2D.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Apr 25 13:57:39 2016
@author: mb540
"""
import numpy as np
import matplotlib.pyplot as plt
dt = .01
T = 30
iterations = int(T/dt)
### agent ###
radius = 2
sensors_angle = np.pi/3 # angle between sensor and central body line
length_dir = 3
pos_centre = np.zeros((2,1)) # centre of mass
vel = np.zeros((2,1))
theta = 0
# sensors
sensor = np.zeros((2,1))
# network (brain)
temp_orders = 2
nodes = 3
x = np.zeros((iterations,nodes,temp_orders))
x_init = np.random.standard_normal(nodes,)
n = .0*np.random.standard_normal((iterations,nodes))
n = np.zeros((iterations,nodes))
w_orig = np.random.standard_normal((nodes,nodes))
# data (history)
pos_centre_history = np.zeros((2,iterations))
vel_centre_history = np.zeros((1,iterations))
vel_history = np.zeros((2,iterations))
theta_history = np.zeros((1,iterations))
orientation_history = np.zeros((2,2,iterations))
sensor_history = np.zeros((2,iterations))
### environment ###
# light source
pos_centre_light = np.array([[39.],[47.]])
light_intensity = 200
def light_level(point):
distance = np.linalg.norm(pos_centre_light - point)
return light_intensity/(distance**2)
# return light_intensity*np.exp(-distance)
### plot ###
# plot initial pos_centreition
plt.close('all')
#fig = plt.figure(0)
#
#plt.plot(pos_centre_light[0], pos_centre_light[1], color='orange', marker='o', markersize=20)
#
#orientation_endpoint = pos_centre + length_dir*(np.array([[np.cos(theta)], [np.sin(theta)]]))
#orientation = np.concatenate((pos_centre,orientation_endpoint), axis=1) # vector containing centre of mass and endpoint for the line representing the orientation
#
#plt.xlim((0,100))
#plt.ylim((0,100))
#
## update the plot thrpugh objects
#ax = fig.add_subplot(111)
#line1, = ax.plot(pos_centre[0], pos_centre[1], color='lightblue', marker='.', markersize=30*radius) # Returns a tuple of line objects, thus the comma
#line2, = ax.plot(orientation[0,:], orientation[1,:], color='black', linewidth=2) # Returns a tuple of line objects, thus the comma
### initialise variables ###
pos_centre = np.array([[67.],[85.]])
#pos_centre = 100*np.random.random((2,1))
omega = 0
#theta = 4*np.pi/3
#theta = np.pi*2*np.random.uniform()
x[0,:,0] = x_init
noise_sens_sdv = .32
noise_sens = noise_sens_sdv*np.random.randn(2,iterations)
noise_vel = .32*np.random.randn(2,iterations)
for i in range(iterations-1):
print(i)
# perception
sensor[0] = light_level(pos_centre + radius*(np.array([[np.cos(theta+sensors_angle)], [np.sin(theta+sensors_angle)]]))) # left sensor
sensor[1] = light_level(pos_centre + radius*(np.array([[np.cos(theta-sensors_angle)], [np.sin(theta-sensors_angle)]]))) # right sensor
sensor += noise_sens[:,i,None]
# vehicle 2
# vel[0] = np.tanh(sensor[1]) # attach neuron to motor
# vel[1] = np.tanh(sensor[0]) # attach neuron to motor
# vehicle 3
vel[0] = (1-1/(1+np.exp(-sensor[0]))) # attach neuron to motor
vel[1] = (1-1/(1+np.exp(-sensor[1]))) # attach neuron to motor
vel += noise_vel[:,i,None]
# translation
vel_centre = (vel[0]+vel[1])/2
pos_centre += dt*(vel_centre*np.array([[np.cos(theta)], [np.sin(theta)]]))
# rotation
omega = 50*np.float((vel[1]-vel[0])/(2*radius))
theta += dt*omega
# update plot
# if np.mod(i,200)==0: # don't update at each time step, too computationally expensive
# orientation_endpoint = pos_centre + length_dir*(np.array([[np.cos(theta)], [np.sin(theta)]]))
# orientation = np.concatenate((pos_centre,orientation_endpoint), axis=1)
# line1.set_xdata(pos_centre[0])
# line1.set_ydata(pos_centre[1])
# line2.set_xdata(orientation[0,:])
# line2.set_ydata(orientation[1,:])
# fig.canvas.draw()
#input("\nPress Enter to continue.") # adds a pause
# save data
vel_centre_history[0,i] = vel_centre
pos_centre_history[:,i] = pos_centre[:,0]
vel_history[:,i] = vel[:,0]
theta_history[:,i] = theta
#orientation_history[:,:,i] = orientation
sensor_history[:,i] = sensor[:,0]
plt.figure(1)
plt.plot(pos_centre_history[0,:-1], pos_centre_history[1,:-1])
plt.plot(pos_centre_light[0], pos_centre_light[1], color='orange', marker='o', markersize=20)
plt.xlim((0,100))
plt.ylim((0,100))
plt.figure(2)
data = np.zeros((100,100))
for i in range(100):
for j in range(100):
data[i,j] = light_level(np.array([i,j])) + noise_sens_sdv*np.random.randn()
plt.imshow(data, vmin=0, origin='lower')#, vmax=10)
plt.colorbar()
plt.show()
#
#
#plt.figure(3)
#plt.subplot(1,2,1)
#plt.plot(range(iterations), sensor_history[0,:], 'b')
#plt.title("Light intensity")
#plt.subplot(1,2,2)
#plt.plot(range(iterations), sensor_history[1,:], 'b')
#
#plt.figure(4)
#plt.plot(vel_history[0,:-1], vel_history[1,:-1])