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motion_planning.py
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motion_planning.py
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import argparse
import time
import msgpack
from enum import Enum, auto
import numpy as np
from planning_utils import a_star, heuristic, create_grid
from udacidrone import Drone
from udacidrone.connection import MavlinkConnection
from udacidrone.messaging import MsgID
from udacidrone.frame_utils import global_to_local
from my_utils import prune_path, adjust_bearing
class States(Enum):
MANUAL = auto()
ARMING = auto()
TAKEOFF = auto()
WAYPOINT = auto()
LANDING = auto()
DISARMING = auto()
PLANNING = auto()
class MotionPlanning(Drone):
def __init__(self, connection):
super().__init__(connection)
self.target_position = np.array([0.0, 0.0, 0.0])
self.waypoints = []
self.in_mission = True
self.check_state = {}
# initial state
self.flight_state = States.MANUAL
# register all your callbacks here
self.register_callback(MsgID.LOCAL_POSITION, self.local_position_callback)
self.register_callback(MsgID.LOCAL_VELOCITY, self.velocity_callback)
self.register_callback(MsgID.STATE, self.state_callback)
def local_position_callback(self):
if self.flight_state == States.TAKEOFF:
if -1.0 * self.local_position[2] > 0.95 * self.target_position[2]:
self.waypoint_transition()
elif self.flight_state == States.WAYPOINT:
if np.linalg.norm(self.target_position[0:2] - self.local_position[0:2]) < 1.0:
if len(self.waypoints) > 0:
self.waypoint_transition()
else:
if np.linalg.norm(self.local_velocity[0:2]) < 1.0:
self.landing_transition()
def velocity_callback(self):
if self.flight_state == States.LANDING:
if self.global_position[2] - self.global_home[2] < 0.1:
if abs(self.local_position[2]) < 0.01:
self.disarming_transition()
def state_callback(self):
if self.in_mission:
if self.flight_state == States.MANUAL:
self.arming_transition()
elif self.flight_state == States.ARMING:
if self.armed:
self.plan_path()
elif self.flight_state == States.PLANNING:
self.takeoff_transition()
elif self.flight_state == States.DISARMING:
if ~self.armed & ~self.guided:
self.manual_transition()
def arming_transition(self):
self.flight_state = States.ARMING
print("arming transition")
self.arm()
self.take_control()
def takeoff_transition(self):
self.flight_state = States.TAKEOFF
print("takeoff transition")
self.takeoff(self.target_position[2])
def waypoint_transition(self):
self.flight_state = States.WAYPOINT
print("waypoint transition")
self.target_position = self.waypoints.pop(0)
print('target position', self.target_position)
self.cmd_position(self.target_position[0], self.target_position[1], self.target_position[2],
self.target_position[3])
def landing_transition(self):
self.flight_state = States.LANDING
print("landing transition")
self.land()
def disarming_transition(self):
self.flight_state = States.DISARMING
print("disarm transition")
self.disarm()
self.release_control()
def manual_transition(self):
self.flight_state = States.MANUAL
print("manual transition")
self.stop()
self.in_mission = False
def send_waypoints(self):
print("Sending waypoints to simulator ...")
data = msgpack.dumps(self.waypoints)
self.connection._master.write(data)
def plan_path(self):
self.flight_state = States.PLANNING
print("Searching for a path ...")
TARGET_ALTITUDE = 5
SAFETY_DISTANCE = 5
self.target_position[2] = TARGET_ALTITUDE
# DONE: read lat0, lon0 from colliders into floating point values
# line below provided by mentor Christopher
with open('colliders.csv') as f:
origin_pos_data = f.readline().split(',')
lat0 = float(origin_pos_data[0].strip().split(' ')[1])
lon0 = float(origin_pos_data[1].strip().split(' ')[1])
# DONE: set home position to (lon0, lat0, 0)
self.set_home_position(lon0, lat0, 0.0)
# DONE: retrieve current global position
current_global_position = [self._longitude, self._latitude, self._altitude]
# DONE: convert to current local position using global_to_local()
current_local_position = global_to_local(current_global_position, self.global_home)
print('global home {0}, position {1}, local position {2}'.format(self.global_home, self.global_position,
self.local_position))
# Read in obstacle map
data = np.loadtxt('colliders.csv', delimiter=',', dtype='Float64', skiprows=2)
print(data[:2])
# Define a grid for a particular altitude and safety margin around obstacles
grid, north_offset, east_offset = create_grid(data, TARGET_ALTITUDE, SAFETY_DISTANCE)
print("North offset = {0}, east offset = {1}".format(north_offset, east_offset))
# Define starting point on the grid (this is just grid center)
# need to cast to integers for N and E here
start_north = int(current_local_position[0])
start_east = int(current_local_position[1])
print('Grid start N: {0} E: {1}'.format(start_north, start_east))
grid_start = ((start_north + -north_offset), (start_east + -east_offset))
# DONE: convert start position to current position rather than map center
# Set goal as some arbitrary position on the grid
# grid_goal = (-north_offset + 10, -east_offset + 10)
# Set to a grassy area just a bit SW of the original starting point.
goal_lon = -122.397745
goal_lat = 37.793837
goal_alt = 0
# The following was done with help/guidance from student Maruf Aytekin
# DONE: adapt to set goal as latitude / longitude position and convert
# set the global position for the goal
goal_global_position = [goal_lon, goal_lat, goal_alt]
# convert to the local formatted position
goal_local_position = global_to_local(goal_global_position, self.global_home)
# need to cast to an integer the values for N and E
(goal_north, goal_east) = (int(goal_local_position[0]), int(goal_local_position[1]))
# use numpy ceil to get the integer value as at the top of the rounding
# the offset is used to set the goal in the correct NE position based on the grid
grid_goal = (int(np.ceil(goal_north - north_offset)), int(np.ceil(goal_east - east_offset)))
# Run A* to find a path from start to goal
# DONE: add diagonal motions with a cost of sqrt(2) to your A* implementation
# or move to a different search space such as a graph (not done here)
print('Local Start and Goal: ', grid_start, grid_goal)
print('running A*, this takes a while... stand by...')
"""
NOTE: if the simulator craps out due to a timeout, it may be that the simulator has a memory leak somewhere
close the simulator, reopen it, then retry again.
"""
path, _ = a_star(grid, heuristic, grid_start, grid_goal)
print(path)
# DONE: prune path to minimize number of waypoints
# NOT DONE TODO (if you're feeling ambitious): Try a different approach altogether!
print('pruning the paths...')
path = prune_path(path)
print(path)
# Convert path to waypoints
waypoints = [[p[0] + north_offset, p[1] + east_offset, TARGET_ALTITUDE, 0] for p in path]
# Set self.waypoints
print('show the first waypoint: ', waypoints[0])
# Add bearing to waypoints
waypoints = adjust_bearing(waypoints)
print('waypoints with bearing: ', waypoints[1])
self.waypoints = waypoints
# DONE: send waypoints to sim (this is just for visualization of waypoints)
self.send_waypoints()
def start(self):
self.start_log("Logs", "NavLog.txt")
print("starting connection")
self.connection.start()
# Only required if they do threaded
# while self.in_mission:
# pass
self.stop_log()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--port', type=int, default=5760, help='Port number')
parser.add_argument('--host', type=str, default='127.0.0.1', help="host address, i.e. '127.0.0.1'")
args = parser.parse_args()
conn = MavlinkConnection('tcp:{0}:{1}'.format(args.host, args.port), timeout=60)
drone = MotionPlanning(conn)
time.sleep(1)
drone.start()