def _rate_predictions(self, predictions):
assert len(predictions) > 0
(px, py) = predictions[-1]
if len(self._yield_set) > 0:
robot_name = list(self._yield_set)[0]
rx = self._states[robot_name]['x']
ry = self._states[robot_name]['y']
return 1.0 - MeasurementUtils.distance(px, py, rx, ry) / (self.CRUISE_SPEED * PredictionUtils.CIRCLES_DELTA * PredictionUtils.CIRCLES_NUM * 0.5)
else:
tx, ty = self._target[0]
return MeasurementUtils.distance(px, py, tx, ty)
def _rate_predictions(self, predictions):
assert len(predictions) > 0
(px, py) = predictions[-1]
if len(self._yield_set) > 0:
robot_name = list(self._yield_set)[0]
rx = self._states[robot_name]['x']
ry = self._states[robot_name]['y']
return 1.0 - MeasurementUtils.distance(px, py, rx, ry) / (self.CRUISE_SPEED * PredictionUtils.CIRCLES_DELTA * PredictionUtils.CIRCLES_NUM * 0.5)
else:
tx, ty = self._target[0]
return MeasurementUtils.distance(px, py, tx, ty)
def _compute_fear_factors(self):
self._fear_factors = {}
for robot_name, state in self._states.items():
x = state['x']
y = state['y']
theta = state['theta']
fear_factor = state['fear_factor']
for robot_name2, state2 in self._states.items():
if robot_name == robot_name2:
continue
x2 = state2['x']
y2 = state2['y']
theta2 = state2['theta']
dist = MeasurementUtils.distance(x, y, x2, y2)
if dist > FearfulAlgorithm.FF_RADIUS:
continue
angle_diff = abs(MeasurementUtils.normalize_angle(theta - theta2))
if angle_diff > 0.5 * math.pi:
continue
fear_factor += (1 - dist / FearfulAlgorithm.FF_RADIUS) * math.cos(angle_diff) \
* self._states[robot_name]['fear_factor']
self._fear_factors[robot_name] = fear_factor
def _compute_fear_factors(self):
self._fear_factors = {}
for robot_name, state in self._states.items():
x = state['x']
y = state['y']
theta = state['theta']
fear_factor = state['fear_factor']
for robot_name2, state2 in self._states.items():
if robot_name == robot_name2:
continue
x2 = state2['x']
y2 = state2['y']
theta2 = state2['theta']
dist = MeasurementUtils.distance(x, y, x2, y2)
if dist > FearfulAlgorithm.FF_RADIUS:
continue
angle_diff = abs(MeasurementUtils.normalize_angle(theta - theta2))
if angle_diff > 0.5 * math.pi:
continue
fear_factor += (1 - dist / FearfulAlgorithm.FF_RADIUS) * math.cos(angle_diff) \
* self._states[robot_name]['fear_factor']
self._fear_factors[robot_name] = fear_factor
def _cut_predictions(predictions, target):
(tx, ty) = target
for i in range(len(predictions)):
(px, py) = predictions[i]
if MeasurementUtils.distance(px, py, tx, ty) < 0.2:
return predictions[:i+1] + [(px, py)] * (len(predictions) - i - 1)
return predictions
def _cut_predictions(predictions, target):
(tx, ty) = target
for i in range(len(predictions)):
(px, py) = predictions[i]
if MeasurementUtils.distance(px, py, tx, ty) < 0.2:
return predictions[:i+1] + [(px, py)] * (len(predictions) - i - 1)
return predictions
def _target_distance(self):
assert type(self._target) == type([])
assert len(self._target) > 0
(tx, ty) = self._target[0]
mx = self._own_robot['x']
my = self._own_robot['y']
return MeasurementUtils.distance(tx, ty, mx, my)
def _target_distance(self):
assert type(self._target) == type([])
assert len(self._target) > 0
(tx, ty) = self._target[0]
mx = self._own_robot['x']
my = self._own_robot['y']
return MeasurementUtils.distance(tx, ty, mx, my)
def _navigate2(self, avail_time):
dist = None
x = self._own_robot['x']
y = self._own_robot['y']
theta = self._own_robot['theta']
Otheta = self._own_robot['org_theta']
Otheta = 1.57 - Otheta;
v = FearfulAlgorithm.CRUISE_SPEED
if self._target is None:
tx, ty = self._controller.get_new_target()
self._target = self._maze_util.find_path((x, y), (tx, ty))
target1 = self._target[0]
tx, ty = target1
bearing = math.atan2(ty - y, tx - x)
target_dist = MeasurementUtils.distance(x, y, tx, ty)
Vl, Vr = FearfulAlgorithm._get_wheel_speeds_new2(self._logger,v,Otheta,bearing,False,target_dist)
#self._send_robot_command(RobotCommand(Vl, Vr, Vl, Vr))
#if bearing < 0:
# bearing = bearing * (-1)
#target_dist = MeasurementUtils.distance(x, y, tx, ty)
#Vl = 0.0
#Vr = 0.0
#self._logger.info('ORG THETA: %f' % ( Otheta))
#self._logger.info("FindIntersction1 Vl: %f; Vr: %f; v: %f; theta: %f; bearing: %f; target_distance: %f; sub: %f" % (Vl, Vr, v, Otheta, bearing, target_dist, Otheta - bearing))
#Vl, Vr = FearfulAlgorithm._get_wheel_speeds_new2(v,theta,bearing,False,target_dist)
#if Otheta < -3.14:
# Otheta = Otheta + 6.28
#elif Otheta > 3.14:
# Otheta = Otheta - 6.28
#self._logger.info("FindIntersction2 Vl: %f; Vr: %f; v: %f; theta: %f; bearing: %f; target_distance: %f; sub: %f" % (Vl, Vr, v, Otheta, bearing, target_dist, Otheta + bearing ))
#self._logger.info("Test!!!!")
#self._send_robot_command(RobotCommand(Vl, Vr, Vl, Vr))
return
def _distances_to_robots(self):
distances = []
x = self._own_robot['x']
y = self._own_robot['y']
for robot_name, state in self._states.items():
if robot_name == self._robot_name:
continue
rx = state['x']
ry = state['y']
distance = MeasurementUtils.distance(x, y, rx, ry)
distances.append((robot_name, distance))
distances.sort(key=itemgetter(1))
return distances
def _distances_to_robots(self):
distances = []
x = self._own_robot['x']
y = self._own_robot['y']
for robot_name, state in self._states.items():
if robot_name == self._robot_name:
continue
rx = state['x']
ry = state['y']
distance = MeasurementUtils.distance(x, y, rx, ry)
distances.append((robot_name, distance))
distances.sort(key=itemgetter(1))
return distances
def _find_intersections(self, own_predictions, radius, ignored_num):
assert len(own_predictions) > 0
for (robot_name, other_predictions) in self._predictions.items():
if self._robot_name == robot_name:
continue
if self._fear_factors[self._robot_name] > self._fear_factors[robot_name]:
continue
for ((mx, my), (ox, oy)) in zip(own_predictions[ignored_num:], other_predictions[ignored_num:]):
dst = MeasurementUtils.distance(mx, my, ox, oy)
if dst <= radius:
return True
maze = self._maze_util.find_intersection(own_predictions, 0.25)
return maze
def _find_intersections(self, own_predictions, radius, ignored_num):
assert len(own_predictions) > 0
for (robot_name, other_predictions) in self._predictions.items():
if self._robot_name == robot_name:
continue
if self._states[self._robot_name]['fear_factor'] > self._states[robot_name]['fear_factor']:
continue
for ((mx, my), (ox, oy)) in zip(own_predictions[ignored_num:], other_predictions[ignored_num:]):
dst = MeasurementUtils.distance(mx, my, ox, oy)
if dst <= radius:
return True
maze = self._maze_util.find_intersection(own_predictions, 0.25)
return maze
def _update_states(self):
new_states = self._controller.request_states()
if not self._running or len(new_states) == 0:
return False
for (robot_name, new_state) in new_states.items():
org_x = new_state.get_x()
org_y = new_state.get_y()
org_theta = new_state.get_theta()
timestamp = new_state.get_timestamp()
fear_factor = new_state.get_fear_factor()
if robot_name not in self._states:
location_kalman = LocationKalman(org_x, org_y, self.INTERVAL)
angle_kalman = AngleKalman(org_theta, self.INTERVAL)
state = {'location_kalman': location_kalman,
'angle_kalman': angle_kalman,
'timestamp': timestamp,
'x': org_x,
'y': org_y,
'vx': 0.0,
'vy': 0.0,
'v': 0.0,
'theta': org_theta,
'omega': 0.0,
'epsilon': 0.0,
'fear_factor': fear_factor}
self._states[robot_name] = state
state = self._states[robot_name]
location_kalman = state['location_kalman']
angle_kalman = state['angle_kalman']
if timestamp > state['timestamp']:
location_kalman.step(org_x, org_y)
angle_kalman.step(org_theta)
else:
location_kalman.missing_step()
angle_kalman.missing_step()
(x, y, vx, vy) = location_kalman.get_means()
(theta, omega, epsilon) = angle_kalman.get_means()
v = math.sqrt(vx * vx + vy * vy)
if robot_name == self._robot_name:
self._odometer += MeasurementUtils.distance(state['x'], state['y'], x, y)
state['x'] = x
state['y'] = y
state['vx'] = vx
state['vy'] = vy
state['v'] = v
state['theta'] = theta
state['omega'] = omega
state['epsilon'] = epsilon
state['timestamp'] = timestamp
state['fear_factor'] = fear_factor
if self._f is not None and robot_name == self._robot_name:
self._f.write("%i %f %f %f %f %f %f %f %f\n" %
(timestamp, x, org_x, y, org_y, v, theta, org_theta, omega))
if self._robot_name not in self._states:
return False
self._own_robot = self._states[self._robot_name]
return True
def _navigate(self, avail_time):
dist = None
x = self._own_robot['x']
y = self._own_robot['y']
theta = self._own_robot['theta']
v = SimpleAlgorithm.CRUISE_SPEED
if self._target is None:
tx, ty = self._controller.get_new_target()
self._target = self._maze_util.find_path((x, y), (tx, ty))
if not self._target_reached and self._target_distance() < 0.1:
if len(self._target) > 1:
self._target.pop(0)
else:
new_target = self._controller.get_new_target()
if new_target is None or new_target != self._target[0]:
tx, ty = new_target
self._target = self._maze_util.find_path((x, y), (tx, ty))
else:
self._target_reached = True
self._logger.info("Target reached")
self.send_finish_msg()
space_id = self._maze_util.find_space_by_point((x, y))
if space_id != self._space_id:
if self._space_id is not None:
tx, ty = self._target[-1]
self._target = self._maze_util.find_path((x, y), (tx, ty))
self._space_id = space_id
if self._avoid_close_objects(x, y, theta):
return
target = self._target[0]
tx, ty = target
bearing = MeasurementUtils.angle(x, y, tx, ty)
preds = PredictionUtils.predict_positions(x, y, v, bearing, 0.0)
preds = SimpleAlgorithm._cut_predictions(preds, target)
self._predictions[self._robot_name] = preds
target_dist = MeasurementUtils.distance(x, y, tx, ty)
if not self._find_intersections(preds, self.CIRCLES_RADIUS * 1.5, 0):
self._own_fear_factor = self._base_fear_factor
v, omega = SimpleAlgorithm._navigate_fun(v, theta, bearing, False)
if target_dist < 0.2:
v *= target_dist
if target_dist < 0.05:
v = 0
Vl, Vr = SimpleAlgorithm._get_wheel_speeds(v, omega)
self._send_robot_command(RobotCommand(Vl, Vr, Vl, Vr))
return
for robot_name in list(self._yield_set):
rx = self._states[robot_name]['x']
ry = self._states[robot_name]['y']
if MeasurementUtils.distance(x, y, rx, ry) > 2.0:
self._yield_set.remove(robot_name)
best_v = None
best_bearing = None
best_midpoint = None
best_rate = None
best_preds = None
self._predictions[self._robot_name] = None
if self._last_params is not None:
best_v, best_bearing, best_midpoint = self._last_params
best_preds = PredictionUtils.predict_positions(
x, y, best_v, best_bearing, 0.0)
best_preds = SimpleAlgorithm._cut_predictions(
best_preds, best_midpoint)
best_rate = self._rate_predictions(best_preds)
if self._find_intersections(best_preds, self.CIRCLES_RADIUS, 1):
best_v = None
best_bearing = None
best_midpoint = None
best_rate = None
best_preds = None
try_stop = True
variants_stop_time = time.time() + avail_time - 0.001
var_num = 0
while time.time() < variants_stop_time:
var_num += 1
if try_stop:
rand_v = 0.0
rand_bearing = theta
rand_radius = 0.0
try_stop = False
else:
rand_v = random.uniform(0.0, self.CRUISE_SPEED * 2.0)
rand_bearing = random.uniform(-math.pi, math.pi)
rand_radius = random.uniform(
0.0, self.CRUISE_SPEED * PredictionUtils.CIRCLES_DELTA *
PredictionUtils.CIRCLES_NUM)
mp_x = rand_radius * math.cos(rand_bearing) + x
mp_y = rand_radius * math.sin(rand_bearing) + y
rand_midpoint = mp_x, mp_y
rand_preds = PredictionUtils.predict_positions(
x, y, rand_v, rand_bearing, 0.0)
rand_preds = SimpleAlgorithm._cut_predictions(
rand_preds, rand_midpoint)
rand_rate = self._rate_predictions(rand_preds)
if self._find_intersections(rand_preds, self.CIRCLES_RADIUS * 1.5,
1):
continue
if best_rate is None or rand_rate < best_rate - 0.5:
best_v, best_bearing, best_preds, best_rate, best_midpoint \
= rand_v, rand_bearing, rand_preds, rand_rate, rand_midpoint
self._last_params = best_v, best_bearing, best_midpoint
if best_preds is None:
self._send_stop_command()
return
else:
self._predictions[self._robot_name] = best_preds
best_v, omega = SimpleAlgorithm._navigate_fun(
best_v, theta, best_bearing, False)
mp_x, mp_y = best_midpoint
midpoint_dist = MeasurementUtils.distance(x, y, mp_x, mp_y)
if midpoint_dist < 0.5:
best_v *= midpoint_dist
Vl, Vr = SimpleAlgorithm._get_wheel_speeds(best_v, omega)
self._send_robot_command(RobotCommand(Vl, Vr, Vl, Vr))
def _navigate(self, avail_time):
dist = None
x = self._own_robot['x']
y = self._own_robot['y']
theta = self._own_robot['theta']
v = SimpleAlgorithm.CRUISE_SPEED
if self._target is None:
tx, ty = self._controller.get_new_target()
self._target = self._maze_util.find_path((x, y), (tx, ty))
if not self._target_reached and self._target_distance() < 0.1:
if len(self._target) > 1:
self._target.pop(0)
else:
new_target = self._controller.get_new_target()
if new_target is None or new_target != self._target[0]:
tx, ty = new_target
self._target = self._maze_util.find_path((x, y), (tx, ty))
else:
self._target_reached = True
self._logger.info("Target reached")
self.send_finish_msg()
space_id = self._maze_util.find_space_by_point((x, y))
if space_id != self._space_id:
if self._space_id is not None:
tx, ty = self._target[-1]
self._target = self._maze_util.find_path((x, y), (tx, ty))
self._space_id = space_id
if self._avoid_close_objects(x, y, theta):
return
target = self._target[0]
tx, ty = target
bearing = MeasurementUtils.angle(x, y, tx, ty)
preds = PredictionUtils.predict_positions(x, y, v, bearing, 0.0)
preds = SimpleAlgorithm._cut_predictions(preds, target)
self._predictions[self._robot_name] = preds
target_dist = MeasurementUtils.distance(x, y, tx, ty)
if not self._find_intersections(preds, self.CIRCLES_RADIUS * 1.5, 0):
self._own_fear_factor = self._base_fear_factor
v, omega = SimpleAlgorithm._navigate_fun(v, theta, bearing, False)
if target_dist < 0.2:
v *= target_dist
if target_dist < 0.05:
v = 0
Vl, Vr = SimpleAlgorithm._get_wheel_speeds(v, omega)
self._send_robot_command(
RobotCommand(Vl, Vr, Vl, Vr))
return
for robot_name in list(self._yield_set):
rx = self._states[robot_name]['x']
ry = self._states[robot_name]['y']
if MeasurementUtils.distance(x, y, rx, ry) > 2.0:
self._yield_set.remove(robot_name)
best_v = None
best_bearing = None
best_midpoint = None
best_rate = None
best_preds = None
self._predictions[self._robot_name] = None
if self._last_params is not None:
best_v, best_bearing, best_midpoint = self._last_params
best_preds = PredictionUtils.predict_positions(x, y, best_v, best_bearing, 0.0)
best_preds = SimpleAlgorithm._cut_predictions(best_preds, best_midpoint)
best_rate = self._rate_predictions(best_preds)
if self._find_intersections(best_preds, self.CIRCLES_RADIUS, 1):
best_v = None
best_bearing = None
best_midpoint = None
best_rate = None
best_preds = None
try_stop = True
variants_stop_time = time.time() + avail_time - 0.001
var_num = 0
while time.time() < variants_stop_time:
var_num += 1
if try_stop:
rand_v = 0.0
rand_bearing = theta
rand_radius = 0.0
try_stop = False
else:
rand_v = random.uniform(0.0, self.CRUISE_SPEED * 2.0)
rand_bearing = random.uniform(-math.pi, math.pi)
rand_radius = random.uniform(0.0, self.CRUISE_SPEED * PredictionUtils.CIRCLES_DELTA * PredictionUtils.CIRCLES_NUM)
mp_x = rand_radius * math.cos(rand_bearing) + x
mp_y = rand_radius * math.sin(rand_bearing) + y
rand_midpoint = mp_x, mp_y
rand_preds = PredictionUtils.predict_positions(x, y, rand_v, rand_bearing, 0.0)
rand_preds = SimpleAlgorithm._cut_predictions(rand_preds, rand_midpoint)
rand_rate = self._rate_predictions(rand_preds)
if self._find_intersections(rand_preds, self.CIRCLES_RADIUS * 1.5, 1):
continue
if best_rate is None or rand_rate < best_rate - 0.5:
best_v, best_bearing, best_preds, best_rate, best_midpoint \
= rand_v, rand_bearing, rand_preds, rand_rate, rand_midpoint
self._last_params = best_v, best_bearing, best_midpoint
if best_preds is None:
self._send_stop_command()
return
else:
self._predictions[self._robot_name] = best_preds
best_v, omega = SimpleAlgorithm._navigate_fun(best_v, theta, best_bearing, False)
mp_x, mp_y = best_midpoint
midpoint_dist = MeasurementUtils.distance(x, y, mp_x, mp_y)
if midpoint_dist < 0.5:
best_v *= midpoint_dist
Vl, Vr = SimpleAlgorithm._get_wheel_speeds(best_v, omega)
self._send_robot_command(
RobotCommand(Vl, Vr, Vl, Vr))
def _navigate(self, avail_time):
dist = None
#self._logger.info("Nowy kod")
x = self._own_robot['x']
y = self._own_robot['y']
theta = self._own_robot['theta']
Otheta = self._own_robot['org_theta']
Otheta = 1.57 - Otheta
v = FearfulAlgorithm.CRUISE_SPEED
if self._target is None:
tx, ty = self._controller.get_new_target()
self._target = self._maze_util.find_path((x, y), (tx, ty))
if not self._target_reached and self._target_distance() < FearfulAlgorithm.DISTANCE_GOAL:
if len(self._target) > 1:
self._target.pop(0)
else:
new_target = self._controller.get_new_target()
if new_target is None or new_target != self._target[0]:
tx, ty = new_target
self._target = self._maze_util.find_path((x, y), (tx, ty))
else:
self._target_reached = True
self._logger.info("Target reached")
self.send_finish_msg()
space_id = self._maze_util.find_space_by_point((x, y))
if space_id != self._space_id:
if self._space_id is not None:
tx, ty = self._target[-1]
self._target = self._maze_util.find_path((x, y), (tx, ty))
self._space_id = space_id
# ominiecie robota lub robotow
if self._avoid_close_objects(x, y, theta,Otheta):
return
target = self._target[0]
tx, ty = target
bearing = MeasurementUtils.angle(x, y, tx, ty)
bearingNew = math.atan2(ty - y, tx - x) #tylko dla nawigacji
preds = PredictionUtils.predict_positions(x, y, v, bearing, 0.0)
preds = FearfulAlgorithm._cut_predictions(preds, target)
self._predictions[self._robot_name] = preds
target_dist = MeasurementUtils.distance(x, y, tx, ty)
if not self._find_intersections(preds, self.CIRCLES_RADIUS * 1.5, 0):
self._own_fear_factor = self._base_fear_factor
self._logger.info("find intersectio")
Vl, Vr = FearfulAlgorithm._get_wheel_speeds_new2(self._logger,v,Otheta,bearingNew,False,target_dist)
self._send_robot_command(RobotCommand(Vl, Vr, Vl, Vr))
#v, omega = FearfulAlgorithm._navigate_fun(v, theta, bearing, False)
#if target_dist < 0.2:
# v *= target_dist
#if target_dist < 0.05:
# v = 0
##Vl, Vr = FearfulAlgorithm._get_wheel_speeds(v, omega)
#Vl, Vr = FearfulAlgorithm._get_wheel_speeds_new2(v,Otheta,bearing,False,target_dist,tx - x,ty - y)
#self._logger.info("FindIntersction Vl: %f; Vr: %f; v: %f; theta: %f; bearing: %f; target_distance: %f; sub: %f,x: %f, y: %f" % (Vl, Vr, v, Otheta, bearing, target_dist, Otheta - bearing,x,y))
#self._send_robot_command(RobotCommand(Vl, Vr, Vl, Vr))
return
for robot_name in list(self._yield_set):
rx = self._states[robot_name]['x']
ry = self._states[robot_name]['y']
if MeasurementUtils.distance(x, y, rx, ry) > 2.0:
self._yield_set.remove(robot_name)
best_v = None
best_bearing = None
best_midpoint = None
best_rate = None
best_preds = None
self._predictions[self._robot_name] = None
if self._last_params is not None:
best_v, best_bearing, best_midpoint = self._last_params
best_preds = PredictionUtils.predict_positions(x, y, best_v, best_bearing, 0.0)
best_preds = FearfulAlgorithm._cut_predictions(best_preds, best_midpoint)
best_rate = self._rate_predictions(best_preds)
if self._find_intersections(best_preds, self.CIRCLES_RADIUS, 1):
best_v = None
best_bearing = None
best_midpoint = None
best_rate = None
best_preds = None
try_stop = True
variants_stop_time = time.time() + avail_time - 0.001
var_num = 0
while time.time() < variants_stop_time:
var_num += 1
if try_stop:
rand_v = 0.0
rand_bearing = theta
rand_radius = 0.0
try_stop = False
else:
rand_v = random.uniform(0.0, self.CRUISE_SPEED * 2.0)
rand_bearing = random.uniform(-math.pi, math.pi)
rand_radius = random.uniform(0.0, self.CRUISE_SPEED * PredictionUtils.CIRCLES_DELTA * PredictionUtils.CIRCLES_NUM)
mp_x = rand_radius * math.cos(rand_bearing) + x
mp_y = rand_radius * math.sin(rand_bearing) + y
rand_midpoint = mp_x, mp_y
rand_preds = PredictionUtils.predict_positions(x, y, rand_v, rand_bearing, 0.0)
rand_preds = FearfulAlgorithm._cut_predictions(rand_preds, rand_midpoint)
rand_rate = self._rate_predictions(rand_preds)
if self._find_intersections(rand_preds, self.CIRCLES_RADIUS * 1.5, 1):
continue
if best_rate is None or rand_rate < best_rate - 0.5:
best_v, best_bearing, best_preds, best_rate, best_midpoint \
= rand_v, rand_bearing, rand_preds, rand_rate, rand_midpoint
self._last_params = best_v, best_bearing, best_midpoint
if best_preds is None:
self._logger.info("best pred is None -->>>>> Stop command:")
self._send_stop_command()
return
else:
self._predictions[self._robot_name] = best_preds
#best_v, omega = FearfulAlgorithm._navigate_fun(best_v, theta, best_bearing, False)
mp_x, mp_y = best_midpoint
midpoint_dist = MeasurementUtils.distance(x, y, mp_x, mp_y)
#if midpoint_dist < 0.5:
# best_v *= midpoint_dist
#Vl, Vr = FearfulAlgorithm._get_wheel_speeds(best_v, omega)
##Vl, Vr = FearfulAlgorithm._get_wheel_speeds_new2(v,Otheta,best_bearing,False,target_dist, mp_x - x, mp_y - y)
##self._logger.info("Prediction Vl: %f; Vr: %f; v: %f; theta: %f; best_bearing: %f; target_distance: %f; sub: %f,x: %f,y: %f" % (Vl, Vr, v, Otheta, best_bearing, target_dist,Otheta - best_bearing,x,y))
self._logger.info("Prediction:")
Vl, Vr = FearfulAlgorithm._get_wheel_speeds_new2(self._logger,v,Otheta,best_bearing,False,target_dist)
self._send_robot_command(RobotCommand(Vl, Vr, Vl, Vr))
def _update_states(self):
new_states = self._controller.request_states()
if not self._running or len(new_states) == 0:
return False
for (robot_name, new_state) in new_states.items():
org_x = new_state.get_x()
org_y = new_state.get_y()
org_theta = new_state.get_theta()
timestamp = new_state.get_timestamp()
fear_factor = new_state.get_fear_factor()
self._logger.debug("org_theta: %f" % (org_theta))
if robot_name not in self._states:
location_kalman = LocationKalman(org_x, org_y, self.INTERVAL)
angle_kalman = AngleKalman(org_theta, self.INTERVAL)
state = {'location_kalman': location_kalman,
'angle_kalman': angle_kalman,
'timestamp': timestamp,
'x': org_x,
'y': org_y,
'vx': 0.0,
'vy': 0.0,
'v': 0.0,
'theta': org_theta,
'omega': 0.0,
'epsilon': 0.0,
'fear_factor': fear_factor,
'org_theta': org_theta}
self._states[robot_name] = state
state = self._states[robot_name]
location_kalman = state['location_kalman']
angle_kalman = state['angle_kalman']
if timestamp > state['timestamp']:
location_kalman.step(org_x, org_y)
angle_kalman.step(org_theta)
else:
location_kalman.missing_step()
angle_kalman.missing_step()
(x, y, vx, vy) = location_kalman.get_means()
(theta, omega, epsilon) = angle_kalman.get_means()
v = math.sqrt(vx * vx + vy * vy)
if robot_name == self._robot_name:
self._odometer += MeasurementUtils.distance(state['x'], state['y'], x, y)
state['x'] = x
state['y'] = y
state['vx'] = vx
state['vy'] = vy
state['v'] = v
state['theta'] = theta
state['omega'] = omega
state['epsilon'] = epsilon
state['timestamp'] = timestamp
state['fear_factor'] = fear_factor
state['org_theta'] = org_theta
if self._f is not None and robot_name == self._robot_name:
self._f.write("%i %f %f %f %f %f %f %f %f\n" %
(timestamp, x, org_x, y, org_y, v, theta, org_theta, omega))
if self._robot_name not in self._states:
return False
self._own_robot = self._states[self._robot_name]
return True