def __init__(self, x, y):
self.pose = m2d.Pose2D()
self.pose.translation = m2d.Vector2(x, y)
self.pose.rotation = math.radians(0)
self.ball_position = m2d.Vector2(100.0, 0.0)
self.obstacle_list = ([]) # is in global coordinates
def __init__(self):
self.pose = m2d.Pose2D()
self.pose.translation = m2d.Vector2(0, 0)
self.pose.rotation = math.radians(0)
self.ball_position = m2d.Vector2(2500.0, -450.0) # relative to robot
self.rotation_vel = 60 # degrees per sec
self.walking_vel = 200 # degrees per sec
def __init__(self):
self.pose = m2d.Pose2D()
self.pose.translation = m2d.Vector2(-4000, -2000)
self.pose.rotation = math.radians(0)
self.rotation_vel = 60 # degrees per sec
self.walking_vel = 200 # mm per sec
self.ball_position = m2d.Vector2(100.0, 0.0)
self.obstacle_list = ([]) # is in global coordinates
def __init__(self):
self.pose = m2d.Pose2D()
self.pose.translation = m2d.Vector2(-4000, -700)
self.pose.rotation = math.radians(-40)
self.ball_position = m2d.Vector2(100.0, 0.0)
self.obstacle_list = ([]) # is in global coordinates
self.next_action = 0
def __init__(self):
self.pose = m2d.Pose2D()
self.pose.translation = m2d.Vector2(-2900, -2200)
self.pose.rotation = math.radians(45)
self.ball_position = m2d.Vector2(100.0, 0.0)
self.obstacle_list = ([]) # is in global coordinates
self.own_robots = [m2d.Vector2(-2250, -1000)]
self.opp_robots = [m2d.Vector2(-1800, -1000)]
def __init__(self, x=1000, y=100):
self.pose = m2d.Pose2D()
self.pose.translation = m2d.Vector2(x, y)
self.pose.rotation = math.radians(0)
self.rotation_vel = 60 # degrees per sec
self.walking_vel = 200 # mm per sec
self.ball_position = m2d.Vector2(0.0, 0.0)
# Possible options: normal, influence_01, generated
self.potential_field_function = "normal"
self.opp_robots = ([]) # is in global coordinates
self.own_robots = ([]) # is in global coordinates
def __init__(self):
self.pose = m2d.Pose2D()
self.pose.translation = m2d.Vector2(-2900, -2200)
self.pose.rotation = math.radians(55)
self.ball_position = m2d.Vector2(100.0, 0.0)
self.obstacle_list = ([]) # is in global coordinates
self.potential_field_function = "influence_01"
self.own_robots = [m2d.Vector2(-2000, -1000)]
self.opp_robots = [] # m2d.Vector2(-1800, -1000)
self.actions = [no_action, kick_short, sidekick_left, sidekick_right
] # possible actions the robot can perform
def main():
num_random_pos = 20
random_x = [randint(int(-field.x_length / 2 + 25), int(field.x_length / 2 - 25)) for p in range(num_random_pos)]
random_y = [randint(int(-field.y_length / 2 + 25), int(field.y_length / 2 - 25)) for p in range(num_random_pos)]
random_r = np.random.randint(360, size=num_random_pos)
random_r = np.radians(random_r)
# record the experiment header
experiment = {
'kind': 'random',
'actions': actions,
'frames': []
}
positions = [m2d.Pose2D(m2d.Vector2(x, y), r) for (x, y, r) in zip(random_x, random_y, random_r)]
counter = mp.Value('i', 0)
pool = mp.Pool(initializer=init, initargs=(counter, ), processes=4)
runner = pool.map_async(make_run, positions)
#wait until done
# NOTE: this has to be done this way, so the programm can be interrupted by keyboard
# http://xcodest.me/interrupt-the-python-multiprocessing-pool-in-graceful-way.html
while not runner.ready():
try:
experiment['frames'] = runner.get(1) # a very long timeout
except mp.TimeoutError as ex:
pass
pool.close()
# make sure not to overwrite anything
file_idx = 0
while True:
output_file = 'data/simulation_{0}.pickle'.format(file_idx)
if os.path.exists(output_file):
file_idx += 1
else:
pickle.dump(experiment, open(output_file, "wb"))
break
import math
import numpy as np
from matplotlib import pyplot as plt
from tools import field_info as field
from naoth import math2d as m2d
from tools import potential_field as pf
from tools import tools
from state import State
if __name__ == "__main__":
state = State()
state.opp_robots.append(
m2d.Pose2D(m2d.Vector2(2000, 1000), math.radians(0)))
state.own_robots.append(
m2d.Pose2D(m2d.Vector2(-2000, 1000), math.radians(0)))
plt.clf()
x_val = np.arange(-field.x_field_length / 2, field.x_field_length / 2, 10)
y_val = np.arange(-field.y_field_length / 2, field.y_field_length / 2, 10)
potentials = np.zeros((len(y_val), len(x_val)))
# There is probably a better implementation for this
step_x = 0
step_y = 0
for x in x_val:
for y in y_val:
# potentials[step_y][step_x] = pf.evaluate_single_pos(m2d.Vector2(x, y))
potentials[step_y][step_x] = pf.evaluate_single_pos_with_robots(
m2d.Vector2(x, y), state.opp_robots, state.own_robots)
step_y += 1
