def test_one_agent_at_goal_sequential(self):
param_server = ParameterServer()
# Model Definition
dynamic_model = SingleTrackModel(param_server)
behavior_model = BehaviorMotionPrimitives(dynamic_model, param_server)
idx = behavior_model.add_motion_primitive(np.array([1, 0]))
behavior_model.action_to_behavior(idx)
execution_model = ExecutionModelInterpolate(param_server)
# Agent Definition
agent_2d_shape = CarLimousine()
init_state = np.array([0, 0, 0, 0, 0])
agent_params = param_server.addChild("agent1")
goal_frame = Polygon2d([0, 0, 0],
[Point2d(-1,-1),
Point2d(-1,1),
Point2d(1,1),
Point2d(1,-1)])
goal_polygon1 = goal_frame.translate(Point2d(10, 0))
goal_polygon2 = goal_frame.translate(Point2d(20, 0))
goal_polygon3 = goal_frame.translate(Point2d(30, 0))
goal_def1 = GoalDefinitionStateLimits(goal_polygon1, [-0.08, 0.08])
goal_def2 = GoalDefinitionStateLimits(goal_polygon2, [-0.08, 0.08])
goal_def3 = GoalDefinitionStateLimits(goal_polygon3, [-0.08, 0.08])
goal_definition = GoalDefinitionSequential([goal_def1,
goal_def2,
goal_def3])
self.assertEqual(len(goal_definition.sequential_goals),3)
agent = Agent(init_state,
behavior_model,
dynamic_model,
execution_model,
agent_2d_shape,
agent_params,
goal_definition,
None)
world = World(param_server)
world.add_agent(agent)
evaluator = EvaluatorGoalReached(agent.id)
world.add_evaluator("success", evaluator)
# just drive with the single motion primitive should be successful
for _ in range(0,1000):
world.step(0.2)
info = world.evaluate()
if info["success"]:
break
self.assertEqual(info["success"], True)
self.assertAlmostEqual(agent.state[int(StateDefinition.X_POSITION)], 30, delta=0.5)
def _build_sequential_goal_definition(self):
goal_list = []
for goal_pose in self.goal_frame_poses:
goal_polygon = Polygon2d(self.goal_frame_center,
np.array(self.goal_frame_points))
goal_polygon = goal_polygon.Transform(goal_pose)
goal_definition = GoalDefinitionPolygon(goal_polygon)
goal_list.append(goal_definition)
return GoalDefinitionSequential(goal_list)
def create_single_scenario(self):
scenario = Scenario(map_file_name=self._map_file_name,
json_params=self._params.ConvertToDict())
world = scenario.get_world_state()
agent_list = []
scenario._agent_list = []
for agent_json_ in self._local_params["Agents"]:
agent_json = agent_json_["VehicleModel"].copy()
agent_json["map_interface"] = world.map
goal_polygon = Polygon2d([0, 0, 0],
np.array(
agent_json["goal"]["polygon_points"]))
goal_polygon = goal_polygon.Translate(
Point2d(agent_json["goal"]["center_pose"][0],
agent_json["goal"]["center_pose"][1]))
sequential_goals = []
goal = GoalDefinitionPolygon(goal_polygon)
if "goal_type" in agent_json["goal"]:
goal_type = agent_json["goal"]["goal_type"]
if goal_type == "GoalDefinitionStateLimits":
goal = GoalDefinitionStateLimits(goal_polygon,
(1.49, 1.65))
# state_limit_goal = GoalDefinitionStateLimits(goal_polygon, (1.49, 1.65))
for _ in range(self._local_params["goal"]["num_reached", "num",
5]):
sequential_goals.append(goal)
sequential_goal = GoalDefinitionSequential(sequential_goals)
agent_json["goal_definition"] = sequential_goal
agent_state = np.array(agent_json["state"])
if len(np.shape(agent_state)) > 1:
agent_state = np.random.uniform(low=agent_state[:, 0],
high=agent_state[:, 1])
agent_json["state"] = agent_state.tolist()
agent = self._json_converter.agent_from_json(
agent_json, param_server=self._params)
agent.SetAgentId(agent_json["id"])
scenario._agent_list.append(agent)
# TODO(@hart): this could be mult. agents
scenario._eval_agent_ids = self._local_params[
"controlled_ids", "IDs of agents to be controlled. ", [0]]
return scenario
