class ActorSource(AtomicBehavior):
"""
Implementation for a behavior that will indefinitely create actors
at a given transform if no other actor exists in a given radius
from the transform.
Important parameters:
- actor_type_list: Type of CARLA actors to be spawned
- transform: Spawn location
- threshold: Min available free distance between other actors and the spawn location
- blackboard_queue_name: Name of the blackboard used to control this behavior
- actor_limit [optional]: Maximum number of actors to be spawned (default=7)
A parallel termination behavior has to be used.
"""
def __init__(self, actor_type_list, transform, threshold, blackboard_queue_name,
actor_limit=7, name="ActorSource"):
"""
Setup class members
"""
super(ActorSource, self).__init__(name)
self._world = CarlaDataProvider.get_world()
self._actor_types = actor_type_list
self._spawn_point = transform
self._threshold = threshold
self._queue = Blackboard().get(blackboard_queue_name)
self._actor_limit = actor_limit
self._last_blocking_actor = None
def update(self):
new_status = py_trees.common.Status.RUNNING
if self._actor_limit > 0:
world_actors = self._world.get_actors()
spawn_point_blocked = False
if (self._last_blocking_actor and
self._spawn_point.location.distance(self._last_blocking_actor.get_location()) < self._threshold):
spawn_point_blocked = True
if not spawn_point_blocked:
for actor in world_actors:
if self._spawn_point.location.distance(actor.get_location()) < self._threshold:
spawn_point_blocked = True
self._last_blocking_actor = actor
break
if not spawn_point_blocked:
try:
new_actor = CarlaActorPool.request_new_actor(np.random.choice(self._actor_types), self._spawn_point)
self._actor_limit -= 1
self._queue.put(new_actor)
except:
print("ActorSource unable to spawn actor")
return new_status
class ActorSource(AtomicBehavior):
"""
Implementation for a behavior that will indefinitely create actors
at a given transform if no other actor exists in a given radius
from the transform.
"""
def __init__(self, world, actor_type_list, transform, threshold, blackboard_queue_name,
actor_limit=7, name="ActorSource"):
"""
Setup class members
"""
super(ActorSource, self).__init__(name)
self._world = world
self._actor_types = actor_type_list
self._spawn_point = transform
self._threshold = threshold
self._queue = Blackboard().get(blackboard_queue_name)
self._actor_limit = actor_limit
self._last_blocking_actor = None
def update(self):
new_status = py_trees.common.Status.RUNNING
if self._actor_limit > 0:
world_actors = self._world.get_actors()
spawn_point_blocked = False
if (self._last_blocking_actor and
self._spawn_point.location.distance(self._last_blocking_actor.get_location()) < self._threshold):
spawn_point_blocked = True
if not spawn_point_blocked:
for actor in world_actors:
if self._spawn_point.location.distance(actor.get_location()) < self._threshold:
spawn_point_blocked = True
self._last_blocking_actor = actor
break
if not spawn_point_blocked:
try:
new_actor = CarlaActorPool.request_new_actor(np.random.choice(self._actor_types), self._spawn_point)
self._actor_limit -= 1
self._queue.put(new_actor)
except:
print("ActorSource unable to spawn actor")
return new_status
class ActorSource(AtomicBehavior):
"""
Implementation for a behavior that will indefinitely create actors
at a given transform if no other actor exists in a given radius
from the transform.
Important parameters:
- actor_type_list: Type of CARLA actors to be spawned
- transform: Spawn location
- threshold: Min available free distance between other actors and the spawn location
- blackboard_queue_name: Name of the blackboard used to control this behavior
- actor_limit [optional]: Maximum number of actors to be spawned (default=7)
A parallel termination behavior has to be used.
"""
def __init__(self,
actor_type_list,
transform,
threshold,
blackboard_queue_name,
actor_limit=999,
name="ActorSource",
# ======== additional args ========
init_speed=0., # initial speed of spawned vehicle
randomize=False,
):
"""
Setup class members
"""
super(ActorSource, self).__init__(name)
self._world = CarlaDataProvider.get_world()
self._actor_types = actor_type_list
self._spawn_point = transform
self._threshold = threshold + 5. # todo fix this with accurate vehicle length
self._queue = Blackboard().get(blackboard_queue_name)
self._actor_limit = actor_limit
self._last_blocking_actor = None
# ========== additional attributes ==========
self.init_speed = init_speed
self.randomize = randomize
self.distance_gap = None
def update(self):
if not self.distance_gap:
self.distance_gap = self._threshold
new_status = py_trees.common.Status.RUNNING
# self._threshold = 20
if self._actor_limit > 0:
world_actors = self._world.get_actors()
vehicle_list = world_actors.filter('vehicle.*')
vehicles = []
for veh in vehicle_list:
if veh.attributes['role_name'] == 'scenario': # todo fix role_name if set by args
vehicles.append(veh)
spawn_point_blocked = False
if (self._last_blocking_actor and
self._spawn_point.location.distance(self._last_blocking_actor.get_location()) < self.distance_gap):
spawn_point_blocked = True
if not spawn_point_blocked:
# for actor in world_actors:
for actor in vehicles:
if self._spawn_point.location.distance(actor.get_location()) < self.distance_gap:
spawn_point_blocked = True
self._last_blocking_actor = actor
break
if not spawn_point_blocked:
try:
new_actor = CarlaDataProvider.request_new_actor(
np.random.choice(self._actor_types), self._spawn_point)
self._actor_limit -= 1
self._queue.put(new_actor)
set_vehicle_speed(new_actor, self.init_speed / 3.6)
# update distance gap after generating a new actor
if self.randomize:
sigma = 0.1
self.distance_gap = random.uniform(self._threshold * (1-sigma), self._threshold * (1+sigma))
except: # pylint: disable=bare-except
print("ActorSource unable to spawn actor")
return new_status
