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Movecars.py
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import math
import numpy
from ApplyGA import ApplyGA
from Feedforward import Feedforward
# Prerequisites Chromosomes, Chromosomes_Fitness
# Outputs Fitness(standing vector: an element for each car)
# Initializations
def MoveCars(env, nbrOfTimeStepsToTimeout, GA, dt, sensor, car, num, smallXYVariance, Chromosomes_Fitness, Chromosomes,
Network_Arch, unipolarBipolarSelector, collison_value):
carLocations = env.start_points # Car Initial Location[X, Y] in [Meters]
carHeadings = env.start_headings # Car Initial Heading Counter Clock Wise[Degrees]
steerAngles = env.start_steerAngles # [Degrees] Counter Clock Wise(Same for all cars)
# timesteps = 1
# Old_Locations = []
# for i in range(int(nbrOfTimeStepsToTimeout) - 1):
# l = []
# for j in range(2):
# l.append(0)
# Old_Locations.append(l)
Generation_ids = 0 #At which generation
Chromosome_ids = 1 #At which chromosome
timeStepsDone = 0 #How many time steps passed
prev_carLines = []
BestFitnessChromoID = 1
Car_Finished_Pool = 0
nbrOfParentsToKeep = math.ceil(GA.PercentBestParentsToKeep * GA.populationSize / 100) #For replacement
All_Chromosomes = [] #All chromosome weights
All_Chromosomes_Fitness = [] #Fitness of each chromosome (in terms of time)
#To store things from surviving chromosomes in later on
for i in range(GA.populationSize):
l = []
for j in range(GA.chromosomeLength):
l.append(0)
All_Chromosomes.append(l)
All_Chromosomes_Fitness.append(0)
# Iterating Generations
while (1):
# Move Car and Draw Environment - Get Sensor Readings and Collision State
LifeTimes = 0 # In number of draw steps(multiple of GA.dt)
sensor_readings = []
y = 0
print("Sensor readings: ") ###############input sensor readings with angles - spectrum - distance
for i in range(sensor.size):
sensor_readings.append(int(input()))
dist = min(sensor_readings) #will be used to determines if there is a collision
id = sensor_readings.index(dist)
collison_bools = False
if dist <= collison_value:
collison_bools = True
else:
collison_bools = False
timeStepsDone = timeStepsDone + 1
# Increase lifetimes by 1
# Update Fitness
Fitness = LifeTimes #fitness used as a measure of time steps (steps is an iteration of this while loop)
LifeTimes = LifeTimes + 1
Fitness += 1
# If car is almost in same place after nbrOfTimeStepsToTimeout has passed, set rotating_around_my_self_bool
rotating_around_my_self_bool = 0
# if (LifeTimes >= nbrOfTimeStepsToTimeout):
# Old_Locations.append(carLocations)
# mean_x = statistics.mean(Old_Locations[:][0])
# mean_y = statistics.mean(Old_Locations[:][1])
# x = Old_Locations[0]
# for i in range(len(x)):
# try:
# x[i] = math.pow((x[i] - mean_x), 2)
# except OverflowError:
# x[i] = float('inf')
# var_x = statistics.mean(x) # numpy.mean(( - mean_x) ^ 2)
# x = Old_Locations[1]
# for i in range(len(x)):
# try:
# x[i] = math.pow((x[i] - mean_y), 2)
# except OverflowError:
# x[i] = float('inf')
# var_y = statistics.mean(x)
#
# if var_x <= smallXYVariance and var_y <= smallXYVariance:
# rotating_around_my_self_bool = 1
# else:
# Old_Locations[LifeTimes - 1][0] = carLocations[0]
# Old_Locations[LifeTimes - 1][1] = carLocations[1]
if (collison_bools):
if (Fitness > max(Chromosomes_Fitness)):
BestFitnessChromoID = Chromosome_ids # Save Best Fitness
Chromosomes_Fitness[Chromosome_ids] = Fitness
if (Fitness >= GA.goodFitness): #if fitness is better than good fitness, save it
Car_Finished_Pool = 1
BestFitnessChromoID = Chromosome_ids
# ResetCarAndLifeTime(carLocations, env, 0, carHeadings, steerAngles, LifeTimes, prev_carLines)
if (Car_Finished_Pool != 1):
Chromosome_ids = Chromosome_ids + 1
elif (rotating_around_my_self_bool == 1):
All_Chromosomes_Fitness[Chromosome_ids] = 0 # TODO Is this good ?
# ResetCarAndLifeTime(carLocations, env, 0, carHeadings, steerAngles, LifeTimes, prev_carLines)
if (Car_Finished_Pool != 1):
Chromosome_ids = Chromosome_ids + 1
rotating_around_my_self_bool = 0
# Jump to car next Generation if necessary
if (Chromosome_ids >= GA.populationSize and (Car_Finished_Pool != 1)):
if (Generation_ids >= GA.nbrOfGenerations_max):
Car_Finished_Pool = 1
Chromosome_ids = BestFitnessChromoID
else:
# if (GA.replacement_option == 0)
All_Chromosomes[(i - 1) * GA.populationSize: i * GA.populationSize] = Chromosomes
x = 0
for i in range((i - 1) * GA.populationSize, i * GA.populationSize):
All_Chromosomes_Fitness[i] = Chromosomes_Fitness[x]
x += 1
y += 1
tmp = All_Chromosomes_Fitness.copy()
idx = numpy.argsort(tmp, kind='mergesort', axis=0).tolist()[::-1]
idx2 = numpy.array(idx).tolist()[0:nbrOfParentsToKeep]
ParentsToKeep = []
for i in range(len(idx2)):
ParentsToKeep.append(All_Chromosomes[idx2[i]])
tmp = Chromosomes_Fitness.copy()
idx = numpy.argsort(tmp, kind='mergesort', axis=0).tolist()[::-1]
idx2 = numpy.array(idx).tolist()[0:len(idx) - nbrOfParentsToKeep]
Current_Chromosomes = []
Current_Fitness = []
for i in range(len(idx2)):
Current_Chromosomes.append(Chromosomes[idx2[i]])
Current_Fitness.append(Chromosomes_Fitness[idx2[i]])
Chromosomes_Childs = []
Chromosomes_Childs = ApplyGA(GA, Current_Chromosomes, Current_Fitness)
Chromosomes = []
for i in range(len(ParentsToKeep)):
Chromosomes.append(ParentsToKeep[i])
for i in range(len(Chromosomes_Childs)):
Chromosomes.append(Chromosomes_Childs[i])
Chromosome_ids = 1
Generation_ids = Generation_ids + 1
for i in range(len(Chromosomes_Fitness)):
Chromosomes_Fitness[i] = 0
BestFitnessChromoID = 1
current_chromosome = Chromosomes[Chromosome_ids]
# Apply sensor reading to ANN to calculate steerAngle
outputs = Feedforward(sensor_readings, current_chromosome, Network_Arch, unipolarBipolarSelector)
steerAngles = numpy.pi / 2 * (outputs[1] - outputs[0]) # From - 90 to 90 degrees
frontWheel = []
backWheel = []
# 2D car steering physics(Calculate carLocation and carHeading)
frontWheel.append(float(carLocations[0] + car.wheelBase / 2 * math.cos(carHeadings)))
frontWheel.append(float(carLocations[1] + car.wheelBase / 2 * math.sin(carHeadings)))
backWheel.append(float(carLocations[0] - car.wheelBase / 2 * math.cos(carHeadings)))
backWheel.append(float(carLocations[1] - car.wheelBase / 2 * math.sin(carHeadings)))
backWheel[0] = backWheel[0] + car.speed * dt * math.cos(carHeadings)
backWheel[1] = backWheel[1] + car.speed * dt * math.sin(carHeadings)
frontWheel[0] = frontWheel[0] + car.speed * dt * math.cos(carHeadings + steerAngles)
frontWheel[1] = frontWheel[1] + car.speed * dt * math.sin(carHeadings + steerAngles)
for i in range(len(carLocations)):
carLocations[i] = (frontWheel[i] + backWheel[i]) / 2
carHeadings = math.atan2(frontWheel[1] - backWheel[1], frontWheel[0] - backWheel[0])
# print("Front Wheel: ", frontWheel)
# print("Back Wheel: ", backWheel)
print("Steering Angles: ", steerAngles)