def update(self, delta, elapsed, state_controller, environment):
# fetch roomba odometry
target_roombas, _ = state_controller.query('RoombaState', environment)
# fetch drone odometry
drone_state = state_controller.query('DroneState', environment)
if self.land_time is not None:
if elapsed - self.land_time > cfg.PITTRAS_BLOCK_FLOOR_TIME * 1000:
self.complete(TaskState.SUCCESS)
return
if drone_state['z_pos'] == 0:
self.land_time = elapsed
if not self.target_roomba in target_roombas:
self.complete(TaskState.FAILURE, "Roomba not found")
return
roomba = target_roombas[self.target_roomba]
# calculate the target yaw so that we turn less than 45 degrees
# in either direction and land with a bumper side perpendicular
# to the direction of roomba motion
self.target_yaw = BlockRoombaTask._calculate_target_yaw(
drone_state['yaw'], roomba['heading'])
# calculate the target landing position
block_vector = geometry.rotate_vector(self.block_vector,
roomba['heading'])
self.target_xy = np.array(roomba['pos']) + block_vector
# PID calculations
control_xy = self.pid_xy.get_control(
self.target_xy - drone_state['xy_pos'], -drone_state['xy_vel'],
delta)
control_yaw = self.pid_yaw.get_control(
self.target_yaw - drone_state['yaw'], -drone_state['yaw'], delta)
# normalize acceleration vector
adjusted_xy = geometry.rotate_vector(control_xy, -drone_state['yaw'])
# perform control action
environment.agent.control(adjusted_xy, control_yaw,
cfg.PITTRAS_BLOCK_DESCENT_VEL)
def update(self, delta, elapsed, state_controller, environment):
# fetch roomba odometry
target_roombas, _ = state_controller.query('RoombaState', environment)
# fetch drone odometry
drone_state = state_controller.query('DroneState', environment)
if not self.target_roomba in target_roombas:
self.complete(TaskState.FAILURE, "Roomba not found")
return
roomba = target_roombas[self.target_roomba]
target_xy = np.array(roomba['pos'])
# check if the roomba is too far away
if np.linalg.norm(target_xy - drone_state['xy_pos']
) > cfg.PITTRAS_HIT_ROOMBA_MAX_START_DIST:
self.complete(TaskState.FAILURE, "Roomba is too far away")
return
if self.last_target_xy is None:
self.last_target_xy = target_xy
target_vel_xy = ((target_xy - self.last_target_xy) / delta)
# PID calculations
control_xy = self.pid_xy.get_control(
target_xy - drone_state['xy_pos'],
target_vel_xy - drone_state['xy_vel'], delta)
# normalize acceleration vector
adjusted_xy = geometry.rotate_vector(control_xy, -drone_state['yaw'])
# perform control action
environment.agent.control(adjusted_xy, 0,
cfg.PITTRAS_HIT_ROOMBA_DESCENT_VELOCITY)
# check if we have hit the roomba
if drone_state['z_pos'] < cfg.PITTRAS_DRONE_PAD_ACTIVIATION_HEIGHT:
if self.land_time is None:
self.land_time = elapsed
if elapsed - self.land_time > cfg.PITTRAS_HIT_ROOMBA_FLOOR_TIME * 1000:
self.complete(TaskState.SUCCESS)
return
# update delayed trackers
self.last_target_xy = target_xy
def update(self, delta, elapsed, state_controller, environment):
if (self.state == HoldPositionTaskStates.done
or self.state == HoldPositionTaskStates.failed):
return
# Fetch current odometry
drone_state = state_controller.query('DroneState', environment)
if self.state == HoldPositionTaskStates.init:
# TODO(zacyu): Remove this when `elapsed` is changed to represent
# the elipsed time since the start of the task.
self.start_time = elapsed
self.state = HoldPositionTaskStates.holding
self.hold_xy = drone_state['xy_pos']
self.hold_z = drone_state['z_pos']
return
if (self.hold_duration > 0) and (elapsed - self.start_time >
self.hold_duration * 1000):
if (np.linalg.norm(drone_state['xy_pos'] - self.hold_xy) <
cfg.PITTRAS_HOLD_POSITION_TOLERANCE
and abs(drone_state['z_pos'] - self.hold_z) <
cfg.PITTRAS_HOLD_POSITION_TOLERANCE):
self.complete(TaskState.SUCCESS)
self.state = HoldPositionTaskStates.done
else:
self.complete(TaskState.FAILURE)
self.state = HoldPositionTaskStates.failed
# PID calculations
control_xy = self.pid_xy.get_control(
self.hold_xy - drone_state['xy_pos'], -drone_state['xy_vel'],
delta)
control_z = self.pid_z.get_control(self.hold_z - drone_state['z_pos'],
-drone_state['z_vel'], delta)
# normalize acceleration vector
adjusted_xy = geometry.rotate_vector(control_xy, -drone_state['yaw'])
# Perform control action
environment.agent.control(adjusted_xy, 0, control_z)
def update(self, delta, elapsed, state_controller, environment):
# Fetch current odometry
drone_state = state_controller.query('DroneState', environment)
if np.linalg.norm(self.target_xy - drone_state['xy_vel']) < \
cfg.PITTRAS_VELOCITY_TOLERANCE:
self.complete(TaskState.SUCCESS)
return
# Calculate control acceleration vector
control_xy = self.pid_xy.get_control(
self.target_xy - drone_state['xy_vel'],
[0, 0],
delta
)
# normalize acceleration vector
adjusted_xy = geometry.rotate_vector(control_xy, -drone_state['yaw'])
# Perform control action
environment.agent.control(adjusted_xy, 0, self.target_z)
def update(self, delta, elapsed, state_controller, environment):
# fetch current odometry
drone_state = state_controller.query('DroneState', environment)
# check if we have reached the target
dist = np.linalg.norm(self.target_xy - drone_state['xy_pos'])
if dist < cfg.PITTRAS_XYZ_TRANSLATION_ACCURACY:
self.complete(TaskState.SUCCESS)
return
# PID calculations
control_xy = self.pid_xy.get_control(
self.target_xy - drone_state['xy_pos'], -drone_state['xy_vel'],
delta)
control_z = self.pid_z.get_control(
self.target_z - drone_state['z_pos'], -drone_state['z_vel'], delta)
# rotate the control xy vector counterclockwise about
# the origin by the current yaw
adjusted_xy = geometry.rotate_vector(control_xy, -drone_state['yaw'])
# perform control action
environment.agent.control(adjusted_xy, 0, control_z)