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from pyrobot.brain import Brain
from pyrobot.brain.conx import *
from time import *
class NNBrain(Brain):
def setup(self):
self.net = Network()
self.net.addLayers(3,2,2)
self.net.setEpsilon(0.25)
self.net.setMomentum(.1)
self.maxvalue = self.robot.range.getMaxvalue()
self.counter = 0
self.doneLearning = 0
# Scale the range readings to be between 0 and 1. Also make close
# obstacles register high readings, and distance obstacles low readings.
def scale(self, val):
return (1 - (val / self.maxvalue))
# The robot will get translate and rotate values in the range [-0.5,0.5],
# but the neural network will generate outputs in the range [0,1].
def toNetworkUnits(self, val):
return (val + 0.5)
def toRobotUnits(self, val):
return (val - 0.5)
def determineTargets(self, left, front, right):
if front < 1.0:
print "front"
return([0.0, 0.5])
elif left < 1.0:
print "left"
return([0.0, -0.5])
elif right < 1.0:
print "right"
return([0.0, 0.5])
else:
print "clear"
return([0.5, 0.0])
def step(self):
if self.doneLearning:
self.net.setLearning(0)
else:
self.net.setLearning(1)
# Set inputs
left = min([s.distance() for s in self.robot.range["front-left"]])
front = min([s.distance() for s in self.robot.range["front"]])
right = min([s.distance() for s in self.robot.range["front-right"]])
inputs = map(self.scale, [left, front, right])
trnTarget,rotTarget = self.determineTargets(left, front, right)
targets = [self.toNetworkUnits(trnTarget), self.toNetworkUnits(rotTarget)]
# Learn
self.net.step(input=inputs,output=targets) # input and output are the names of the layers
trnActual = self.toRobotUnits(self.net['output'].activation[0])
rotActual = self.toRobotUnits(self.net['output'].activation[1])
# Check if the robot is stuck and give it a kick to unjam it
if self.robot.stall:
print "stuck--reversing"
self.move(-0.5, 0.0)
sleep(0.5)
else:
if self.doneLearning:
print "move", trnActual, rotActual
self.move(trnActual, rotActual)
else:
print "step", self.counter, "target", trnTarget, rotTarget, \
"network", trnActual, rotActual
self.move(trnTarget, rotTarget)
self.counter += 1
def INIT(engine):
return NNBrain('NNBrain', engine) |
