def multi_waypoint_scenario(Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
pos_obs = np.array([[50,0,0]])
quad0 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='ennemy', id_targ = -1, color = 'blue', pos_ini = [0,0,0], pos_goal= [0,-17,-10], pos_obs = pos_obs)
quad1 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 1, mode='ennemy', id_targ = -1, color = 'green', pos_ini = [20,0,0], pos_goal = [-20,-15,-10], pos_obs = pos_obs)
quad2 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 2, mode='ennemy', id_targ = -1, color = 'red', pos_ini = [-20,-10,0], pos_goal = [-10,0,-20], pos_obs = pos_obs)
quads = [quad0, quad1, quad2]
return pos_obs,quads
def full_scenario(Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
pos_obs = np.array([[1, 5, -2], [8, 2, -8], [5, 8, -9], [0, 0, -2], [3, 3, -1],[3, 9, -17],[5, 7, -18],[0, 0, -10],[5, 10, -16],[10,10,-12],[13,13,-13]])
quad0 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='ennemy', id_targ = -1, color = 'blue', pos_ini = [0,0,0], pos_goal= [15,15,-15], pos_obs = pos_obs)
quad1 = Quadcopter(Ti, Ts*90, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 1, mode='guided', id_targ = -1, color = 'green', pos_ini = [0,3,0], pos_goal = [15,10,-15], pos_obs = pos_obs)
quad2 = Quadcopter(Ti, Ts*100, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 2, mode='track', id_targ = 0, color = 'pink', pos_ini = [3,0,0], pos_goal = [15,20,-15], pos_obs = pos_obs)
quads = [quad0, quad1, quad2]
return pos_obs,quads
def gazebo_track():
pos_obs = np.array([[-50, 0, 0]])
quad0 = Quadcopter(Ts=Ts,
quad_id=0,
mode='ennemy',
id_targ=-1,
pos_goal=[120, 0, -10],
pos_obs=pos_obs,
channel_id='udp:127.0.0.1:14551',
global_frame=None)
# Initialize the frame at the origin by initializing the drone located at the origin
global_frame = quad0.global_frame
quad1 = Quadcopter(Ts=Ts,
quad_id=1,
mode='track',
id_targ=0,
pos_goal=[0, 0, -10],
pos_obs=pos_obs,
channel_id='udp:127.0.0.1:14561',
global_frame=global_frame)
#quad2 = Quadcopter(Ts = Ts, quad_id = 2, mode='ennemy', id_targ = -1, pos_goal= [-100,0,-10], pos_obs = pos_obs, channel_id='udp:127.0.0.1:14571', global_frame=global_frame)
#quad3 = Quadcopter(Ts = Ts, quad_id = 3, mode='ennemy', id_targ = -1, pos_goal= [-100,0,-10], pos_obs = pos_obs, channel_id='udp:127.0.0.1:14581', global_frame=global_frame)
#quad4 = Quadcopter(Ts = Ts, quad_id = 4, mode='ennemy', id_targ = -1, pos_goal= [-100,0,-10], pos_obs = pos_obs, channel_id='udp:127.0.0.1:14591', global_frame=None)
#quad5 = Quadcopter(Ts = Ts, quad_id = 5, mode='ennemy', id_targ = -1, pos_goal= [-100,0,-10], pos_obs = pos_obs, channel_id='udp:127.0.0.1:14601', global_frame=None)
#quad6 = Quadcopter(Ts = Ts, quad_id = 6, mode='ennemy', id_targ = -1, pos_goal= [-100,0,-10], pos_obs = pos_obs, channel_id='udp:127.0.0.1:14611', global_frame=None)
quads = [quad0, quad1] #, quad2, quad3]
return pos_obs, quads
def multi_tracking_scenario(Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
pos_obs = np.array([[-10,-10,0]])
quad0 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='ennemy', id_targ = -1, color = 'blue', pos_ini = [0,0,0], pos_goal = [15,15,-15], pos_obs = pos_obs)
quad1 = Quadcopter(Ti, Ts*100, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 1, mode='track', id_targ = 0, color = 'green', pos_ini = [4,0,0], pos_goal = [4,4,-10], pos_obs = pos_obs)
quad2 = Quadcopter(Ti, Ts*100, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 2, mode='track', id_targ = 0, color = 'green', pos_ini = [4,4,0], pos_goal = [4,4,-10], pos_obs = pos_obs)
quad3 = Quadcopter(Ti, Ts*100, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 3, mode='track', id_targ = 0, color = 'green', pos_ini = [4,-4,0], pos_goal = [4,4,-10], pos_obs = pos_obs)
quads = [quad0, quad1, quad2, quad3]
return pos_obs,quads
def tracking_loop_scenario(x,Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
pos_obs = np.array([[x/2,x/2,-10]])
quad0 = Quadcopter(Ti, Ts*99, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='track', id_targ = 1, color = 'blue', pos_ini = [0,0,-10], pos_goal = [0,x,-10], pos_obs = pos_obs)
quad1 = Quadcopter(Ti, Ts*100, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 1, mode='track', id_targ = 2, color = 'green', pos_ini = [x,0,-10], pos_goal = [0,0,-10], pos_obs = pos_obs)
quad2 = Quadcopter(Ti, Ts*101, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 2, mode='track', id_targ = 3, color = 'orange', pos_ini = [x,x,-10],pos_goal = [x,0,-10], pos_obs = pos_obs)
quad3 = Quadcopter(Ti, Ts*102, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 3, mode='track', id_targ = 0, color = 'pink', pos_ini = [0,x,-10], pos_goal = [x,x,-10],pos_obs = pos_obs)
quads = [quad0, quad1,quad2,quad3]
return pos_obs,quads
def dynamic_CA_scenario(Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
#Tf =8s
pos_obs = np.array([[50,0,0]])
quad0 = Quadcopter(Ti, Ts*100, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='guided', id_targ = -1, color = 'blue', pos_ini = [0,10,-5],pos_goal = [30,10,-5], pos_obs = pos_obs)
quad1 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 1, mode='ennemy', id_targ = -1, color = 'green', pos_ini = [3,0,-5], pos_goal = [3,20,-5], pos_obs = pos_obs)
quad2 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 2, mode='ennemy', id_targ = -1, color = 'green', pos_ini = [8,0,-5], pos_goal = [8,20,-5], pos_obs = pos_obs)
quad3 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 3, mode='ennemy', id_targ = -1, color = 'green', pos_ini = [15,0,-5], pos_goal = [15,20,-5], pos_obs = pos_obs)
quads = [quad0, quad1,quad2,quad3]
return pos_obs,quads
def real_map(Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
xs = [-1,0,1]
ys = [-1,0,1]
zs = [0,-1,-2,-3,-4,-5,-6,-7,-8,-9,-10]
tower = [[x,y,z] for x in xs for y in ys for z in zs]
xs = [-20,5,10]
ys = [5,-10,10]
zs = [0,-1,-2,-3]
trees = [[x,y,z] for x in xs for y in ys for z in zs]
xs = [-20,5,10]
ys = [5,-10,10]
zs = [-4,-5]
tops = []
for i in range(3):
x, y = xs[i], ys[i]
for z in zs:
tops = tops + [[x-1,y,z],[x+1,y,z],[x,y,z],[x,y-1,z],[x,y+1,z]]
print(tops)
pos_obs = np.array(tower + trees + tops)
quad0 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='ennemy', id_targ = -1, color = 'blue', pos_ini = [0,0,-5], pos_goal = [-10,-10,-10], pos_obs = pos_obs)
quads = [quad0]
return pos_obs,quads
def ROS_simu(Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
fire_station=[]
fire_truck=[]
tree_1=[]
tree_2=[]
pos_obs=[]
for i in range(20):
x = random.sample(range(-10, 10), 1)[0]
y = random.sample(range(-55, -45), 1)[0]
z = random.sample(range(-12, 0), 1)[0]
fire_station.append([x,y,z])
for i in range(5):
x = random.sample(range(-19, 21), 1)[0]
y = random.sample(range(-55, -45), 1)[0]
z = random.sample(range(-3, 0), 1)[0]
fire_truck.append([x,y,z])
for i in range(5):
x = random.sample(range(-12, -8), 1)[0]
y = random.sample(range(-42,-38), 1)[0]
z = random.sample(range(-5, 0), 1)[0]
tree_1.append([x,y,z])
for i in range(5):
x = random.sample(range(8, 12), 1)[0]
y = random.sample(range(-42,-38), 1)[0]
z = random.sample(range(-5, 0), 1)[0]
tree_2.append([x,y,z])
pos_obs = fire_station + fire_truck + tree_1 + tree_2
pos_obs = np.array(pos_obs)
quad0 = Quadcopter(Ti, Ts*100, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='guided', id_targ = -1, color = 'blue', pos_ini = [0,0,0], pos_goal = [0,-100,-10], pos_obs = pos_obs)
quads = [quad0]
return(pos_obs,quads)
def simple_guided_for_PF(Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
pos_obs = []
for i in range(20):
pos_obs.append(random.sample(range(-10, 0), 3))
pos_obs = np.array(pos_obs)
quad0 = Quadcopter(Ti, Ts*100, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='guided', id_targ = -1, color = 'blue', pos_ini = [0,0,-5], pos_goal = [-10,-10,-10], pos_obs = pos_obs)
quads = [quad0]
return pos_obs,quads
def static_OA_scenario(Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
pos_obs = []
for i in range(30):
pos_obs.append(random.sample(range(-10, 0), 3))
pos_obs = np.array(pos_obs)
print(pos_obs)
quad0 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='ennemy', id_targ = -1, color = 'blue', pos_ini = [0,0,0], pos_goal= [-10,-10,-10], pos_obs = pos_obs)
quads = [quad0]
return pos_obs,quads
def simple_wp_cycle():
pos_obs = np.array([1, 1, -20])
quad0 = Quadcopter(Ts=Ts,
quad_id=0,
mode='ennemy',
id_targ=-1,
pos_goal=[10, -10, -10],
pos_obs=pos_obs,
channel_id='udp:127.0.0.1:14551',
global_frame=None)
quads = [quad0]
return pos_obs, quads
def gazebo_oa():
pos_obs = []
for i in range(30):
pos_obs.append(random.sample(range(-20, 0), 3))
pos_obs = np.array(pos_obs)
quad0 = Quadcopter(Ts=Ts,
quad_id=0,
mode='ennemy',
id_targ=-1,
pos_goal=[-20, -20, -20],
pos_obs=pos_obs,
channel_id='udp:127.0.0.1:14551',
global_frame=None)
# Initialize the frame at the origin by initializing the drone located at the origin
global_frame = quad0.global_frame
#quad1 = Quadcopter(Ts = Ts, quad_id = 1, mode='ennemy', id_targ = -1, pos_goal= [5,-100,-15], pos_obs = pos_obs, channel_id='udp:127.0.0.1:14561', global_frame=global_frame)
#quad2 = Quadcopter(Ts = Ts, quad_id = 2, mode='ennemy', id_targ = -1, pos_goal= [-5,-100,-8], pos_obs = pos_obs, channel_id='udp:127.0.0.1:14571', global_frame=global_frame)
quads = [quad0] #, quad1, quad2]
return pos_obs, quads
def main():
start_time = time.time()
# Simulation Setup
# ---------------------------
Ti = 0
Ts = 0.005
Tf = 18
ifsave = 1
# Choose trajectory settings
# ---------------------------
ctrlOptions = ["xyz_pos", "xy_vel_z_pos", "xyz_vel"]
trajSelect = np.zeros(3)
# Select Control Type (0: xyz_pos, 1: xy_vel_z_pos, 2: xyz_vel)
ctrlType = ctrlOptions[0]
# Select Position Trajectory Type (0: hover, 1: pos_waypoint_timed, 2: pos_waypoint_interp,
# 3: minimum velocity 4: minimum accel, 5: minimum jerk, 6: minimum snap
# 7: minimum accel_stop 8: minimum jerk_stop 9: minimum snap_stop
# 10: minimum jerk_full_stop 11: minimum snap_full_stop
# 12: pos_waypoint_arrived
trajSelect[0] = 6 # (changed): was 6
# Select Yaw Trajectory Type (0: none 1: yaw_waypoint_timed, 2: yaw_waypoint_interp 3: follow 4: zero)
trajSelect[1] = 6 # (changed): was 6
# Select if waypoint time is used, or if average speed is used to calculate waypoint time (0: waypoint time, 1: average speed)
trajSelect[2] = 1
print("Control type: {}".format(ctrlType))
# Initialize Quadcopter, Controller, Wind, Result Matrixes
# ---------------------------
quad = Quadcopter(Ti)
traj = Trajectory(quad, ctrlType, trajSelect)
ctrl = Control(quad, traj.yawType)
wind = Wind("None", 2.0, 90, -15)
# Trajectory for First Desired States
# ---------------------------
sDes = traj.desiredState(0, Ts, quad) # np.zeros(19)
# Generate First Commands
# ---------------------------
ctrl.controller(traj, quad, sDes, Ts)
# Initialize Result Matrixes
# ---------------------------
numTimeStep = int(Tf / Ts + 1)
(
t_all,
s_all,
pos_all,
vel_all,
quat_all,
omega_all,
euler_all,
sDes_traj_all,
sDes_calc_all,
w_cmd_all,
wMotor_all,
thr_all,
tor_all,
) = init_data(quad, traj, ctrl, numTimeStep)
t_all[0] = Ti
s_all[0, :] = quad.state
pos_all[0, :] = quad.pos
vel_all[0, :] = quad.vel
quat_all[0, :] = quad.quat
omega_all[0, :] = quad.omega
euler_all[0, :] = quad.euler
sDes_traj_all[0, :] = traj.sDes
sDes_calc_all[0, :] = ctrl.sDesCalc
w_cmd_all[0, :] = ctrl.w_cmd
wMotor_all[0, :] = quad.wMotor
thr_all[0, :] = quad.thr
tor_all[0, :] = quad.tor
# Run Simulation
# ---------------------------
t = Ti
i = 1
while round(t, 3) < Tf:
t = quad_sim(t, Ts, quad, ctrl, wind, traj)
# print("{:.3f}".format(t))
t_all[i] = t
s_all[i, :] = quad.state
pos_all[i, :] = quad.pos
vel_all[i, :] = quad.vel
quat_all[i, :] = quad.quat
omega_all[i, :] = quad.omega
euler_all[i, :] = quad.euler
sDes_traj_all[i, :] = traj.sDes
sDes_calc_all[i, :] = ctrl.sDesCalc
w_cmd_all[i, :] = ctrl.w_cmd
wMotor_all[i, :] = quad.wMotor
thr_all[i, :] = quad.thr
tor_all[i, :] = quad.tor
i += 1
end_time = time.time()
print("Simulated {:.2f}s in {:.6f}s.".format(t, end_time - start_time))
# View Results
# ---------------------------
# utils.fullprint(sDes_traj_all[:,3:6])
utils.makeFigures(
quad.params,
t_all,
pos_all,
vel_all,
quat_all,
omega_all,
euler_all,
w_cmd_all,
wMotor_all,
thr_all,
tor_all,
sDes_traj_all,
sDes_calc_all,
)
ani = utils.sameAxisAnimation(
t_all, traj.wps, pos_all, quat_all, sDes_traj_all, Ts, quad.params, traj.xyzType, traj.yawType, ifsave
)
plt.show()
def tracking_and_kill_scenario(Ti,Ts,Tf,ctrlType,trajSelect,numTimeStep):
pos_obs = np.array([[-10,-10,0]])
quad0 = Quadcopter(Ti, Tf, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 0, mode='ennemy', id_targ = -1, color = 'blue', pos_ini = [0,0,-5], pos_goal = [20,15,-20], pos_obs = pos_obs)
quad1 = Quadcopter(Ti, Ts*100, ctrlType, trajSelect, numTimeStep, Ts, quad_id = 1, mode='track', id_targ = 0, color = 'green', pos_ini = [5,0,0], pos_goal = [4,4,-10], pos_obs = pos_obs)
quads = [quad0, quad1]
return pos_obs,quads
def main():
start_time = time.time()
# Simulation Setup
# ---------------------------
Ti = 0
Ts = 0.005
Tf = 18
ifsave = 0
# Choose trajectory settings
# ---------------------------
ctrlOptions = ["xyz_pos", "xy_vel_z_pos", "xyz_vel"]
trajSelect = np.zeros(3)
# Select Control Type (0: xyz_pos, 1: xy_vel_z_pos, 2: xyz_vel)
ctrlType = ctrlOptions[0]
# Select Position Trajectory Type (0: hover, 1: pos_waypoint_timed, 2: pos_waypoint_interp,
# 3: minimum velocity 4: minimum accel, 5: minimum jerk, 6: minimum snap
# 7: minimum accel_stop 8: minimum jerk_stop 9: minimum snap_stop
# 10: minimum jerk_full_stop 11: minimum snap_full_stop
trajSelect[0] = 6
# Select Yaw Trajectory Type (0: none 1: yaw_waypoint_timed, 2: yaw_waypoint_interp 3: follow 4: zero)
trajSelect[1] = 3
# Select if waypoint time is used, or if average speed is used to calculate waypoint time (0: waypoint time, 1: average speed)
trajSelect[2] = 0
print("Control type: {}".format(ctrlType))
# Initialize Quadcopter, Controller, Wind, Result Matrixes
# ---------------------------
quad = Quadcopter(Ti)
traj = Trajectory(quad, ctrlType, trajSelect)
ctrl = Control(quad, traj.yawType)
wind = Wind('None', 2.0, 90, -15)
# Trajectory for First Desired States
# ---------------------------
sDes = traj.desiredState(0, Ts, quad)
# Generate First Commands
# ---------------------------
ctrl.controller(traj, quad, sDes, Ts)
# Initialize Result Matrixes
# ---------------------------
t_all = Ti
s_all = quad.state.T
pos_all = quad.pos.T
vel_all = quad.vel.T
quat_all = quad.quat.T
omega_all = quad.omega.T
euler_all = quad.euler.T
sDes_traj_all = traj.sDes.T
sDes_calc_all = ctrl.sDesCalc.T
w_cmd_all = ctrl.w_cmd.T
wMotor_all = quad.wMotor.T
thr_all = quad.thr.T
tor_all = quad.tor.T
# Run Simulation
# ---------------------------
t = Ti
i = 0
while round(t, 3) < Tf:
t = quad_sim(t, Ts, quad, ctrl, wind, traj)
# print("{:.3f}".format(t))
t_all = np.vstack((t_all, t))
s_all = np.vstack((s_all, quad.state.T))
pos_all = np.vstack((pos_all, quad.pos.T))
vel_all = np.vstack((vel_all, quad.vel.T))
quat_all = np.vstack((quat_all, quad.quat.T))
omega_all = np.vstack((omega_all, quad.omega.T))
euler_all = np.vstack((euler_all, quad.euler.T))
sDes_traj_all = np.vstack((sDes_traj_all, traj.sDes.T))
sDes_calc_all = np.vstack((sDes_calc_all, ctrl.sDesCalc.T))
w_cmd_all = np.vstack((w_cmd_all, ctrl.w_cmd.T))
wMotor_all = np.vstack((wMotor_all, quad.wMotor.T))
thr_all = np.vstack((thr_all, quad.thr.T))
tor_all = np.vstack((tor_all, quad.tor.T))
i += 1
end_time = time.time()
print("Simulated {:.2f}s in {:.6f}s.".format(t, end_time - start_time))
# View Results
# ---------------------------
# utils.fullprint(sDes_traj_all[:,3:6])
utils.makeFigures(quad.params, t_all, pos_all, vel_all, quat_all,
omega_all, euler_all, w_cmd_all, wMotor_all, thr_all,
tor_all, sDes_traj_all, sDes_calc_all)
ani = utils.sameAxisAnimation(t_all, traj.wps, pos_all, quat_all,
sDes_traj_all, Ts, quad.params, traj.xyzType,
traj.yawType, ifsave)
plt.show()
ctrlType = ctrlOptions[1]
# Select Position Trajectory Type (0: hover, 1: pos_waypoint_timed, 2: pos_waypoint_interp,
# 3: minimum velocity 4: minimum accel, 5: minimum jerk, 6: minimum snap
# 7: minimum accel_stop 8: minimum jerk_stop 9: minimum snap_stop
# 10: minimum jerk_full_stop 11: minimum snap_full_stop
# 12: pos_waypoint_arrived
trajSelect[0] = 6 # (changed): was 6
# Select Yaw Trajectory Type (0: none 1: yaw_waypoint_timed, 2: yaw_waypoint_interp 3: follow 4: zero)
trajSelect[1] = 6 # (changed): was 6
# Select if waypoint time is used, or if average speed is used to calculate waypoint time (0: waypoint time, 1: average speed)
trajSelect[2] = 0
print("Control type: {}".format(ctrlType))
# Initialize Quadcopter, Controller, Wind, Result Matrixes
# ---------------------------
quad = Quadcopter(Ti)
traj = Trajectory(quad, ctrlType, trajSelect)
ctrl = Control(quad, traj.yawType)
wind = Wind("None", 2.0, 90, -15)
# Trajectory for First Desired States
# ---------------------------
sDes = traj.desiredState(0, Ts, quad) # np.zeros(19)
# Generate First Commands
# ---------------------------
ctrl.controller(traj, quad, sDes, Ts)
# Initialize Result Matrixes
# ---------------------------
numTimeStep = int(Tf / Ts + 1)
def __init__(self):
self.default_range = default_range = (-10, 10)
self.velocity_range = velocity_range = (-10, 10)
self.ang_velocity_range = ang_velocity_range = (-1, 1)
self.q_range = qrange = (-0.3, 0.3)
self.motor_range = motor_range = (400, 700)
self.motor_d_range = motor_d_range = (-2000, 2000)
self.observation_space_domain = {
"x": default_range,
"y": default_range,
"z": default_range,
"q0": (0.9, 1),
"q1": qrange,
"q2": qrange,
"q3": qrange,
"u": velocity_range,
"v": velocity_range,
"w": velocity_range,
"p": ang_velocity_range,
"q": ang_velocity_range,
"r": ang_velocity_range,
"wM1": motor_range,
"wdotM1": motor_d_range,
"wM2": motor_range,
"wdotM2": motor_d_range,
"wM3": motor_range,
"wdotM3": motor_d_range,
"wM4": motor_range,
"wdotM4": motor_d_range,
"deltacol_p": motor_range,
"deltalat_p": motor_range,
"deltalon_p": motor_range,
"deltaped_p": motor_range,
}
self.states_str = list(self.observation_space_domain.keys())
# observation space to [-1,1]
self.low_obs_space = np.array(tuple(
zip(*self.observation_space_domain.values()))[0],
dtype=float) * 0 - 1
self.high_obs_space = np.array(tuple(
zip(*self.observation_space_domain.values()))[1],
dtype=float) * 0 + 1
self.observation_space = spaces.Box(low=self.low_obs_space,
high=self.high_obs_space,
dtype=float)
self.default_act_range = default_act_range = motor_range
self.action_space_domain = {
"deltacol": default_act_range,
"deltalat": default_act_range,
"deltalon": default_act_range,
"deltaped": default_act_range,
# "f1": (0.1, 5), "f2": (0.5, 20), "f3": (0.5, 20), "f4": (0.5, 10),
# "lambda1": (0.5, 10), "lambda2": (0.1, 5), "lambda3": (0.1, 5), "lambda4": (0.1, 5),
# "eta1": (0.2, 5), "eta2": (0.1, 5), "eta3": (0.1, 5), "eta4": (0.1, 5),
}
self.low_action = np.array(tuple(
zip(*self.action_space_domain.values()))[0],
dtype=float)
self.high_action = np.array(tuple(
zip(*self.action_space_domain.values()))[1],
dtype=float)
self.low_action_space = self.low_action * 0 - 1
self.high_action_space = self.high_action * 0 + 1
self.action_space = spaces.Box(low=self.low_action_space,
high=self.high_action_space,
dtype=float)
self.min_reward = -11
self.high_action_diff = 0.2
obs_header = str(list(self.observation_space_domain.keys()))[1:-1]
act_header = str(list(self.action_space_domain.keys()))[1:-1]
self.header = "time, " + act_header + ", " + obs_header + ", reward" + ", control reward"
self.saver = save_files()
self.reward_array = np.array((0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
dtype=float)
self.constant_dict = {
"u": 0.0,
"v": 0.0,
"w": 0.0,
"p": 0.0,
"q": 0.0,
"r": 0.0,
"fi": 0.0,
"theta": 0.0,
"si": 0.0,
"x": 0.0,
"y": 0.0,
"z": 0.0,
"a": 0.0,
"b": 0.0,
"c": 0.0,
"d": 0.0,
}
self.start_time = time.time()
self.Ti = 0
self.Ts = 0.005
self.Tf = 5
self.reward_check_time = 0.7 * self.Tf
self.ifsave = 0
self.ctrlOptions = ["xyz_pos", "xy_vel_z_pos", "xyz_vel"]
self.trajSelect = np.zeros(3)
self.ctrlType = self.ctrlOptions[0]
self.trajSelect[0] = 0
self.trajSelect[1] = 4
self.trajSelect[2] = 0
self.quad = Quadcopter(self.Ti)
self.traj = Trajectory(self.quad, self.ctrlType, self.trajSelect)
self.ctrl = Control(self.quad, self.traj.yawType)
self.wind = Wind("None", 2.0, 90, -15)
self.numTimeStep = int(self.Tf / self.Ts + 1)
self.longest_num_step = 0
self.best_reward = float("-inf")
self.diverge_counter = 0
self.diverge_list = []
class CustomEnv(gym.Env, ABC):
def __init__(self):
self.default_range = default_range = (-10, 10)
self.velocity_range = velocity_range = (-10, 10)
self.ang_velocity_range = ang_velocity_range = (-1, 1)
self.q_range = qrange = (-0.3, 0.3)
self.motor_range = motor_range = (400, 700)
self.motor_d_range = motor_d_range = (-2000, 2000)
self.observation_space_domain = {
"x": default_range,
"y": default_range,
"z": default_range,
"q0": (0.9, 1),
"q1": qrange,
"q2": qrange,
"q3": qrange,
"u": velocity_range,
"v": velocity_range,
"w": velocity_range,
"p": ang_velocity_range,
"q": ang_velocity_range,
"r": ang_velocity_range,
"wM1": motor_range,
"wdotM1": motor_d_range,
"wM2": motor_range,
"wdotM2": motor_d_range,
"wM3": motor_range,
"wdotM3": motor_d_range,
"wM4": motor_range,
"wdotM4": motor_d_range,
"deltacol_p": motor_range,
"deltalat_p": motor_range,
"deltalon_p": motor_range,
"deltaped_p": motor_range,
}
self.states_str = list(self.observation_space_domain.keys())
# observation space to [-1,1]
self.low_obs_space = np.array(tuple(
zip(*self.observation_space_domain.values()))[0],
dtype=float) * 0 - 1
self.high_obs_space = np.array(tuple(
zip(*self.observation_space_domain.values()))[1],
dtype=float) * 0 + 1
self.observation_space = spaces.Box(low=self.low_obs_space,
high=self.high_obs_space,
dtype=float)
self.default_act_range = default_act_range = motor_range
self.action_space_domain = {
"deltacol": default_act_range,
"deltalat": default_act_range,
"deltalon": default_act_range,
"deltaped": default_act_range,
# "f1": (0.1, 5), "f2": (0.5, 20), "f3": (0.5, 20), "f4": (0.5, 10),
# "lambda1": (0.5, 10), "lambda2": (0.1, 5), "lambda3": (0.1, 5), "lambda4": (0.1, 5),
# "eta1": (0.2, 5), "eta2": (0.1, 5), "eta3": (0.1, 5), "eta4": (0.1, 5),
}
self.low_action = np.array(tuple(
zip(*self.action_space_domain.values()))[0],
dtype=float)
self.high_action = np.array(tuple(
zip(*self.action_space_domain.values()))[1],
dtype=float)
self.low_action_space = self.low_action * 0 - 1
self.high_action_space = self.high_action * 0 + 1
self.action_space = spaces.Box(low=self.low_action_space,
high=self.high_action_space,
dtype=float)
self.min_reward = -11
self.high_action_diff = 0.2
obs_header = str(list(self.observation_space_domain.keys()))[1:-1]
act_header = str(list(self.action_space_domain.keys()))[1:-1]
self.header = "time, " + act_header + ", " + obs_header + ", reward" + ", control reward"
self.saver = save_files()
self.reward_array = np.array((0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
dtype=float)
self.constant_dict = {
"u": 0.0,
"v": 0.0,
"w": 0.0,
"p": 0.0,
"q": 0.0,
"r": 0.0,
"fi": 0.0,
"theta": 0.0,
"si": 0.0,
"x": 0.0,
"y": 0.0,
"z": 0.0,
"a": 0.0,
"b": 0.0,
"c": 0.0,
"d": 0.0,
}
self.start_time = time.time()
self.Ti = 0
self.Ts = 0.005
self.Tf = 5
self.reward_check_time = 0.7 * self.Tf
self.ifsave = 0
self.ctrlOptions = ["xyz_pos", "xy_vel_z_pos", "xyz_vel"]
self.trajSelect = np.zeros(3)
self.ctrlType = self.ctrlOptions[0]
self.trajSelect[0] = 0
self.trajSelect[1] = 4
self.trajSelect[2] = 0
self.quad = Quadcopter(self.Ti)
self.traj = Trajectory(self.quad, self.ctrlType, self.trajSelect)
self.ctrl = Control(self.quad, self.traj.yawType)
self.wind = Wind("None", 2.0, 90, -15)
self.numTimeStep = int(self.Tf / self.Ts + 1)
self.longest_num_step = 0
self.best_reward = float("-inf")
self.diverge_counter = 0
self.diverge_list = []
def reset(self):
# initialization
self.t = 0
self.sDes = self.traj.desiredState(self.t, self.Ts, self.quad)
self.ctrl.controller(self.traj, self.quad, self.sDes, self.Ts)
self.control_input = self.ctrl.w_cmd.copy()
(
self.t_all,
self.s_all,
self.pos_all,
self.vel_all,
self.quat_all,
self.omega_all,
self.euler_all,
self.sDes_traj_all,
self.sDes_calc_all,
self.w_cmd_all,
self.wMotor_all,
self.thr_all,
self.tor_all,
) = init_data(self.quad, self.traj, self.ctrl, self.numTimeStep)
self.all_actions = np.zeros(
(self.numTimeStep, len(self.high_action_space)))
self.control_rewards = np.zeros((self.numTimeStep, 1))
self.all_rewards = np.zeros((self.numTimeStep, 1))
self.t_all[0] = self.Ti
observation = self.quad.state.copy()
observation = np.concatenate((observation, self.control_input), axis=0)
self.pos_all[0, :] = self.quad.pos
self.vel_all[0, :] = self.quad.vel
self.quat_all[0, :] = self.quad.quat
self.omega_all[0, :] = self.quad.omega
self.euler_all[0, :] = self.quad.euler
self.sDes_traj_all[0, :] = self.traj.sDes
self.sDes_calc_all[0, :] = self.ctrl.sDesCalc
self.w_cmd_all[0, :] = self.control_input
self.wMotor_all[0, :] = self.quad.wMotor
self.thr_all[0, :] = self.quad.thr
self.tor_all[0, :] = self.quad.tor
self.counter = 1
self.save_counter = 0
self.find_next_state()
self.jj = 0
# self.quad.state[0:12] = self.initial_states = list((np.random.rand(12) * 0.02 - 0.01))
self.quad.state[0:12] = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
self.done = False
for iii in range(len(self.high_obs_space)):
current_range = self.observation_space_domain[self.states_str[iii]]
observation[iii] = 2 * (observation[iii] - current_range[0]) / (
current_range[1] - current_range[0]) - 1
self.s_all[0, :] = observation
self.integral_error = 0
return list(observation)
def action_wrapper(self, current_action: list) -> np.array:
current_action = np.clip(current_action, -1, 1)
self.normilized_actions = current_action
self.control_input = ((current_action + 1) *
(self.high_action - self.low_action) / 2 +
self.low_action)
def find_next_state(self) -> list:
self.quad.update(self.t, self.Ts, self.control_input,
self.wind) # self.control_input = self.ctrl.w_cmd
# self.quad.update(self.t, self.Ts, self.ctrl.w_cmd, self.wind)
self.t += self.Ts
# self.control_input = self.ctrl.w_cmd.copy()
self.sDes = self.traj.desiredState(self.t, self.Ts, self.quad)
# print('state', self.quad.state)
# print(' ctrl', self.ctrl.w_cmd)
self.ctrl.controller(self.traj, self.quad, self.sDes, self.Ts)
def observation_function(self) -> list:
i = self.counter
quad = self.quad
self.t_all[i] = self.t
self.s_all[i, :] = np.concatenate((quad.state, self.control_input),
axis=0)
self.pos_all[i, :] = quad.pos
self.vel_all[i, :] = quad.vel
self.quat_all[i, :] = quad.quat
self.omega_all[i, :] = quad.omega
self.euler_all[i, :] = quad.euler
self.sDes_traj_all[i, :] = self.traj.sDes
self.sDes_calc_all[i, :] = self.ctrl.sDesCalc
self.w_cmd_all[i, :] = self.control_input
self.wMotor_all[i, :] = quad.wMotor
self.thr_all[i, :] = quad.thr
self.tor_all[i, :] = quad.tor
observation = quad.state.copy()
observation = np.concatenate((observation, self.control_input), axis=0)
for iii in range(len(self.high_obs_space)):
current_range = self.observation_space_domain[self.states_str[iii]]
observation[iii] = 2 * (observation[iii] - current_range[0]) / (
current_range[1] - current_range[0]) - 1
self.s_all[i, :] = observation
return list(observation)
def reward_function(self, observation) -> float:
# add reward slope to the reward
# TODO: normalizing reward
# TODO: adding reward gap
# error_x = np.linalg.norm((abs(self.quad.state[0:3]).reshape(12)), 1)
# error_ang = np.linalg.norm((abs(self.quad.state[3:7]).reshape(12)), 1)
# error_v = np.linalg.norm((abs(self.quad.state[7:10]).reshape(12)), 1)
# error_vang = np.linalg.norm((abs(self.quad.state[10:12]).reshape(12)), 1)
# error = -np.linalg.norm((abs(self.s_all[self.counter, 0:12]).reshape(12)), 1) + 0.05
error = 10 * (-np.linalg.norm(observation[0:12], 1) + 1)
self.control_rewards[self.counter] = error
self.integral_error = 0.1 * (
0.99 * self.control_rewards[self.counter - 1] +
0.99 * self.integral_error)
reward = error.copy()
reward += self.integral_error
reward += -self.min_reward / self.numTimeStep
reward -= 0.000001 * sum(
abs(self.control_input - self.w_cmd_all[self.counter - 1, :]))
reward += -0.0001 * np.linalg.norm(self.normilized_actions, 2)
self.all_rewards[self.counter] = reward
# for i in range(12):
# self.reward_array[i] = abs(self.current_states[i]) / self.default_range[1]
# reward = self.all_rewards[self.counter] = -sum(self.reward_array) + 0.17 / self.default_range[1]
# # control reward
# reward += 0.05 * float(
# self.control_rewards[self.counter] - self.control_rewards[self.counter - 1]
# ) # control slope
# reward += -0.005 * sum(abs(self.all_actions[self.counter])) # input reward
# for i in (self.high_action_diff - self.all_actions[self.counter] - self.all_actions[self.counter - 1]) ** 2:
# reward += -min(0, i)
if self.counter > 100:
print(reward)
return reward
def check_diverge(self) -> bool:
for i in range(len(self.high_obs_space)):
if (abs(self.s_all[self.counter, i])) > self.high_obs_space[i]:
self.diverge_list.append(
(tuple(self.observation_space_domain.keys())[i],
self.quad.state[i]))
self.saver.diverge_save(
tuple(self.observation_space_domain.keys())[i],
self.quad.state[i])
self.diverge_counter += 1
if self.diverge_counter == 2000:
self.diverge_counter = 0
print((tuple(self.observation_space_domain.keys())[i],
self.quad.state[i]))
self.jj = 1
return True
if self.counter >= self.numTimeStep - 1: # number of timesteps
print("successful")
return True
# after self.reward_check_time it checks whether or not the reward is decreasing
# if self.counter > self.reward_check_time / self.Ts:
# prev_time = int(self.counter - self.reward_check_time / self.Ts)
# diverge_criteria = (
# self.all_rewards[self.counter] - sum(self.all_rewards[prev_time:prev_time - 10]) / 10
# )
# if diverge_criteria < -1:
# print("reward_diverge")
# self.jj = 1
# return True
bool_1 = any(np.isnan(self.quad.state))
bool_2 = any(np.isinf(self.quad.state))
if bool_1 or bool_2:
self.jj = 1
print("state_inf_nan_diverge")
return False
def done_jobs(self) -> None:
counter = self.counter
self.save_counter += 1
current_total_reward = sum(self.all_rewards)
if self.save_counter >= 100:
print("current_total_reward: ", current_total_reward)
self.save_counter = 0
self.saver.reward_step_save(self.best_reward,
self.longest_num_step,
current_total_reward, counter)
if counter >= self.longest_num_step:
self.longest_num_step = counter
if current_total_reward >= self.best_reward:
self.best_reward = sum(self.all_rewards)
ii = self.counter + 1
self.saver.best_reward_save(
self.t_all[0:ii],
self.w_cmd_all[0:ii],
self.s_all[0:ii],
self.all_rewards[0:ii],
self.control_rewards[0:ii],
self.header,
)
def step(self, current_action):
self.action_wrapper(current_action)
try:
self.find_next_state()
except OverflowError or ValueError or IndexError:
self.jj = 1
observation = self.observation_function()
reward = self.reward_function(observation)
self.done = self.check_diverge()
if self.jj == 1:
reward = self.min_reward
if self.done:
self.done_jobs()
self.counter += 1
# self.make_constant(list(self.constant_dict.values()))
return list(observation), float(reward), self.done, {}
def make_constant(self, true_list):
for i in range(len(true_list)):
if i == 1:
self.quad.state[i] = self.initial_states[i]
def main():
start_time = time.time()
# Simulation Setup
# ---------------------------
Ti = 0
Ts = 0.005
Tf = 95
ifsave = 0
# Choose trajectory settings
# ---------------------------
ctrlOptions = ["xyz_pos", "xy_vel_z_pos", "xyz_vel"]
trajSelect = np.zeros(3)
# Select Control Type (0: xyz_pos, 1: xy_vel_z_pos, 2: xyz_vel)
ctrlType = ctrlOptions[0]
# Select Position Trajectory Type (0: hover, 1: pos_waypoint_timed, 2: pos_waypoint_arrived
trajSelect[0] = 2
# Select Yaw Trajectory Type (0: none, 1: yaw_waypoint_timed, 2: yaw_waypoint_interp, 3: zero, 4: Follow)
trajSelect[1] = 4
# Select if waypoint time is used, or if average speed is used to calculate waypoint time (0: waypoint time, 1: average speed)
trajSelect[2] = 0
print("Control type: {}".format(ctrlType))
# Initialize Quadcopter, Controller, Wind, Result Matrixes
# ---------------------------
quad = Quadcopter(Ti)
traj = Trajectory(quad, ctrlType, trajSelect)
potfld = PotField(1)
ctrl = Control(quad, traj.yawType)
wind = Wind('None', 2.0, 90, -15)
# Trajectory for First Desired States
# ---------------------------
traj.desiredState(0, Ts, quad)
# First Potential Field Calculation
# ---------------------------
potfld.isWithinRange(quad)
potfld.isWithinField(quad)
potfld.rep_force(quad, traj)
# Generate First Commands
# ---------------------------
ctrl.controller(traj, quad, potfld, Ts)
# Initialize Result Matrixes
# ---------------------------
numTimeStep = int(Tf / Ts + 1)
t_all = np.zeros(numTimeStep)
s_all = np.zeros([numTimeStep, len(quad.state)])
pos_all = np.zeros([numTimeStep, len(quad.pos)])
vel_all = np.zeros([numTimeStep, len(quad.vel)])
quat_all = np.zeros([numTimeStep, len(quad.quat)])
omega_all = np.zeros([numTimeStep, len(quad.omega)])
euler_all = np.zeros([numTimeStep, len(quad.euler)])
sDes_traj_all = np.zeros([numTimeStep, len(traj.sDes)])
sDes_calc_all = np.zeros([numTimeStep, len(ctrl.sDesCalc)])
w_cmd_all = np.zeros([numTimeStep, len(ctrl.w_cmd)])
wMotor_all = np.zeros([numTimeStep, len(quad.wMotor)])
thr_all = np.zeros([numTimeStep, len(quad.thr)])
tor_all = np.zeros([numTimeStep, len(quad.tor)])
notInRange_all = np.zeros([numTimeStep, potfld.num_points], dtype=bool)
inRange_all = np.zeros([numTimeStep, potfld.num_points], dtype=bool)
inField_all = np.zeros([numTimeStep, potfld.num_points], dtype=bool)
minDist_all = np.zeros(numTimeStep)
t_all[0] = Ti
s_all[0, :] = quad.state
pos_all[0, :] = quad.pos
vel_all[0, :] = quad.vel
quat_all[0, :] = quad.quat
omega_all[0, :] = quad.omega
euler_all[0, :] = quad.euler
sDes_traj_all[0, :] = traj.sDes
sDes_calc_all[0, :] = ctrl.sDesCalc
w_cmd_all[0, :] = ctrl.w_cmd
wMotor_all[0, :] = quad.wMotor
thr_all[0, :] = quad.thr
tor_all[0, :] = quad.tor
notInRange_all[0, :] = potfld.notWithinRange
inRange_all[0, :] = potfld.inRangeNotField
inField_all[0, :] = potfld.withinField
minDist_all[0] = potfld.distanceMin
# Run Simulation
# ---------------------------
t = Ti
i = 1
while round(t, 3) < Tf:
t = quad_sim(t, Ts, quad, ctrl, wind, traj, potfld)
# print("{:.3f}".format(t))
t_all[i] = t
s_all[i, :] = quad.state
pos_all[i, :] = quad.pos
vel_all[i, :] = quad.vel
quat_all[i, :] = quad.quat
omega_all[i, :] = quad.omega
euler_all[i, :] = quad.euler
sDes_traj_all[i, :] = traj.sDes
sDes_calc_all[i, :] = ctrl.sDesCalc
w_cmd_all[i, :] = ctrl.w_cmd
wMotor_all[i, :] = quad.wMotor
thr_all[i, :] = quad.thr
tor_all[i, :] = quad.tor
notInRange_all[i, :] = potfld.notWithinRange
inRange_all[i, :] = potfld.inRangeNotField
inField_all[i, :] = potfld.withinField
minDist_all[i] = potfld.distanceMin
i += 1
end_time = time.time()
print("Simulated {:.2f}s in {:.6f}s.".format(t, end_time - start_time))
# View Results
# ---------------------------
def figures():
utils.makeFigures(quad.params, t_all, pos_all, vel_all, quat_all,
omega_all, euler_all, w_cmd_all, wMotor_all, thr_all,
tor_all, sDes_traj_all, sDes_calc_all, potfld,
minDist_all)
plt.show()
utils.third_PV_animation(t_all, traj.wps, pos_all, quat_all, euler_all,
sDes_traj_all, Ts, quad.params, traj.xyzType,
traj.yawType, potfld, notInRange_all, inRange_all,
inField_all, ifsave, figures)
def main():
start_time = time.time()
# Import the simulation setup
# ---------------------------
config = simConfig.config()
# Initialize wind (untested)
# --------------------------
wind = Wind('None', 2.0, 90, -15)
# Initialize list for objects
# ---------------------------
numTimeStep = int(config.Tf / config.Ts + 1)
quadList = []
trajList = []
ctrlList = []
sDesList = []
obsPFList = []
myDataList = []
# For the number of vehicles
# --------------------------
for objectIndex in range(0, config.nVeh):
quadList.append(Quadcopter(config))
trajList.append(
Trajectory(quadList[objectIndex], config.ctrlType,
config.trajSelect, config))
ctrlList.append(
Control(quadList[objectIndex], trajList[objectIndex].yawType))
sDesList.append(trajList[objectIndex].desiredState(
0, config.Ts, quadList[objectIndex]))
obsPFList.append(
pf(trajList[objectIndex],
np.vstack((config.o1, config.o2, config.o3,
quadList[objectIndex - 1].state[0:3])).transpose(),
gamma=config.gamma,
eta=config.eta,
obsRad=config.obsRad,
obsRange=config.obsRange))
# generate first command
ctrlList[objectIndex].controller(trajList[objectIndex],
quadList[objectIndex],
sDesList[objectIndex], config)
# Create learning object
# ---------------------------
fala = falaObj(config)
# Initialize Result Matrixes
# ---------------------------
for objectIndex in range(0, config.nVeh):
# initialize result matrices
myDataList.append(
collect.quadata(quadList[objectIndex], trajList[objectIndex],
ctrlList[objectIndex], fala, config.Ti,
numTimeStep))
# Run Simulation
# ---------------------------
t = config.Ti * np.ones(config.nVeh)
i = 1
while round(t[0], 3) < config.Tf:
# Update the obstacle positions
# -----------------------------
if t[0] > 0.1:
# recall these, as they could move with time
o1 = config.o1 # np.array([-2.1, 0, -3],) # obstacle 1 (x,y,z)
o2 = config.o2 # np.array([2, -1.2, 0.9]) # obstacle 2 (x,y,z)
o3 = config.o3 # np.array([0, 2.5, -2.5]) # obstacle 2 (x,y,z)
# if just one vehicle
if config.nVeh == 1:
obsPFList[0].Po = np.vstack((o1, o2, o3)).transpose()
# if two vehicles, make sure to avoid other dudeBot
#if config.nVeh == 2:
# obsPFList[0].Po = np.vstack((o1,o2,o3,quadList[1].state[0:3])).transpose()
# obsPFList[1].Po = np.vstack((o1,o2,o3,quadList[0].state[0:3])).transpose()
# if more than one vehicle, make sure to avoid other dudeBot
for io in range(0, config.nVeh):
for jo in range(0, config.nVeh - 1):
obsPFList[io].Po = np.vstack(
(o1, o2, o3,
quadList[io - jo].state[0:3])).transpose()
# Integrate through the dynamics
# ------------------------------
for objectIndex in range(0, config.nVeh):
t[objectIndex] = quad_sim(t[objectIndex], config.Ts,
quadList[objectIndex],
ctrlList[objectIndex], wind,
trajList[objectIndex], fala,
obsPFList[objectIndex], config)
myDataList[objectIndex].collect(t[objectIndex],
quadList[objectIndex],
trajList[objectIndex],
ctrlList[objectIndex], fala, i)
i += 1
end_time = time.time()
print("Simulated {:.2f}s in {:.6f}s.".format(t[0], end_time - start_time))
# View Results
# ---------------------------
if config.ifsave:
# make figures
utils.makeFigures(quadList[0].params, myDataList[0])
# make animation (this should be generalized later)
if config.ifsaveplots:
if config.nVeh == 1:
ani = sameAxisAnimation(config,
myDataList[0],
trajList[0],
quadList[0].params,
obsPFList[0],
myColour='blue')
#if config.nVeh == 2:
# ani = sameAxisAnimation2(config, myDataList[0], trajList[0], quadList[0].params, myDataList[1], trajList[1], quadList[1].params, obsPFList[0], 'blue', 'green')
if config.nVeh > 1:
ani = sameAxisAnimationN(config, myDataList, trajList,
quadList, obsPFList[0], 'blue')
plt.show()
# dump the learned parameters
if config.doLearn:
np.savetxt("Data/Qtable.csv",
fala.Qtable,
delimiter=",",
header=" ")
np.savetxt("Data/errors.csv",
myDataList[0].falaError_all,
delimiter=",",
header=" ")
# save energy draw
torque0 = myDataList[0].tor_all
eDraw0 = np.cumsum(np.cumsum(torque0, axis=0), axis=1)[:, 3]
np.save('Data/energyDepletion_veh0', eDraw0)
if config.nVeh == 2:
torque1 = myDataList[1].tor_all
eDraw1 = np.cumsum(np.cumsum(torque1, axis=0), axis=1)[:, 3]
np.save('Data/energyDepletion_veh1', eDraw1)
# save the data
if config.ifsavedata:
#save states
x = myDataList[0].pos_all[:, 0]
y = myDataList[0].pos_all[:, 1]
z = myDataList[0].pos_all[:, 2]
x_sp = myDataList[0].sDes_calc_all[:, 0]
y_sp = myDataList[0].sDes_calc_all[:, 1]
z_sp = myDataList[0].sDes_calc_all[:, 2]
states0 = np.vstack([x, y, z, x_sp, y_sp, z_sp]).transpose()
np.save('Data/states0', states0)
if config.nVeh == 2:
x = myDataList[1].pos_all[:, 0]
y = myDataList[1].pos_all[:, 1]
z = myDataList[1].pos_all[:, 2]
x_sp = myDataList[1].sDes_calc_all[:, 0]
y_sp = myDataList[1].sDes_calc_all[:, 1]
z_sp = myDataList[1].sDes_calc_all[:, 2]
states1 = np.vstack([x, y, z, x_sp, y_sp, z_sp]).transpose()
np.save('Data/states1', states1)
