def __init__(self,
_client,
_drones,
_initial_positions,
_malicious,
_anomaly_type,
_time_factor=1):
Scenario.__init__(self, _client, _drones, _initial_positions,
_malicious, _anomaly_type, _time_factor)
num_of_sim_drones = 5
# Create a rectangle of size 200 x 200 and split randomly to all drones
initial_rectangle = Rectangle((-25, -25), (25, 25))
rectangles = initial_rectangle.divide(num_of_sim_drones)
self.paths = [to_airsim_path(r.get_path()) for r in rectangles]
# Set the time of the scenario to be the time it will take to survey the average rectangle
# (2 is the velocity of the drones)
distance_list = [r.get_distance() for r in rectangles]
avg_distance = sum(distance_list) / len(distance_list)
self.time = avg_distance / (2 * _time_factor)
# The initial position each drone is the initial point in its path
self.goto_initial_locations([path[0] for path in self.paths])
self.started = False
self.started_anomaly = False
# Get the beginning time of the scenario
self.start = time.perf_counter()
def __init__(self, sniff_thread, tcp_handshake, dpid):
Scenario.__init__(self, sniff_thread, tcp_handshake)
self.echo = False
self.xid = 0
self.dpid = dpid
self.answer_features = False
self.answer_config = False
self.answer_barrier = False
self.answer_role = False
self.answer_stats = False
self.answer_stats_ports = False
self.answer_request_port_desc = False
self.answer_stats_manufacturer = False
self.answer_flow = False
self.counter_dict = {
'echo': 0,
'config': 0,
'features': 0,
'barrier': 0,
'role': 0,
'stats': 0,
'port_desc': 0,
'flows': 0
}
self.rules = {}
def __init__(self,
_client,
_drones,
_initial_positions,
_malicious,
_anomaly_type,
_time_factor=1):
Scenario.__init__(self, _client, _drones, _initial_positions,
_malicious, _anomaly_type, _time_factor)
self.started_anomaly = False
self.pos = {}
# the new initial positions will be later determined according to the goal & base axis
self.initial_positions = []
self.yaw = {}
for drone in _drones:
self.yaw[drone] = 0
# The maximum step a drone can move in a single action
self.step = 0.5
# The distance to keep from obstacles
self.coll_thres = 1.5
# The first item in goal represents the axis (0 = x, 1 = y), the second item represents the index
# (for example [1, 60] would represent that the goal is 60 on the y axis)
self.goal = [0, 0]
self.goal[0] = np.random.randint(0, 2)
self.goal[1] = np.random.randint(-30, 30)
# base represents the initial axis of the drones
# its axis is the same as the goal, and its distance from the goal is 80-120 meters
base_range = random.choice([(-120, -80), (80, 120)])
self.base = self.goal[1] + random.randint(*base_range)
# The time of the scenario is the minimum time needed to get from the base to the goal
# (theoretically going in straight line, without obstacles on the way)
self.time = abs(self.base - self.goal[1]) / (2 * _time_factor)
# Set the drones in their initial locations
self.goto_initial_locations()
# Get the scenario's obstacles
self.obstacles = self.get_obstacles()
# Get the beginning time of the scenario
self.start = time.perf_counter()
def __init__(self,
_client,
_drones,
_initial_positions,
_malicious,
_anomaly_type,
_time_factor=1):
Scenario.__init__(self, _client, _drones, _initial_positions,
_malicious, _anomaly_type, _time_factor)
# if the time factor is 1, the scenario time should be 50 seconds
self.monitoring_time = 50 / _time_factor
self.path = {}
self.generate_path()
# Set the drones to be in their initial locations
self.goto_initial_locations()
self.started = False
self.started_anomaly = False
# Get the beginning time of the scenario
self.start = time.perf_counter()
def __init__(self,
_client,
_drones,
_initial_positions,
_malicious,
_anomaly_type,
_time_factor=1):
Scenario.__init__(self, _client, _drones, _initial_positions,
_malicious, _anomaly_type, _time_factor)
self.Y = 100 # meter
self.V = 2 # Ms
self.monitoring_time = (self.Y / self.V) / _time_factor
self.X = np.random.randint((0), (10))
self.Z = np.random.randint((-4), (-2))
# Set the drones to be in their initial locations
self.goto_initial_locations()
self.started = False
self.started_anomaly = False
# Get the beginning time of the scenario
self.start = time.perf_counter()
def __init__(self, **attrs):
Scenario.__init__(self, **attrs)
self.full_reset()
