def case_1():
# Read the vehicle model
vehicle = lomap.Ts()
vehicle.read_from_file('./vehicle_1.txt')
# Convert the vehicle model to an MDP
vehicle_mdp = lomap.Markov()
vehicle_mdp.mdp_from_det_ts(vehicle)
# Read the models of the pedestrians
targets = []
for i in range(1, 6):
t = lomap.Markov()
t.read_from_file('./target_%d.txt' % i)
targets.append(t)
formula = '! col U end'
# Construct the full-state fsa using scheck
# (full-state ensures proper MDP after product)
logger.info('Formula: %s' % formula)
fsa = lomap.Fsa()
fsa.fsa_from_cosafe_formula(formula)
fsa.add_trap_state()
# Classical
with lomap.Timer(
'Classical (non-incremental) Method: Case 1 - Avoid pedestrians'):
lomap.classical_synthesis(vehicle_mdp, fsa, targets, set_props_1)
pass
# Incremental
with lomap.Timer('Incremental Method: Case 1 - Avoid pedestrians'):
lomap.incremental_synthesis(vehicle_mdp, fsa, targets, set_props_1)
def case_2b():
# Read the vehicle model
vehicle = lomap.Ts()
vehicle.read_from_file('./vehicle_1.txt')
# Convert the vehicle model to an MDP
vehicle_mdp = lomap.Markov()
vehicle_mdp.mdp_from_det_ts(vehicle)
# Read the models of the pedestrians
targets = []
for i in range(1, 6):
t = lomap.Markov()
t.read_from_file('./target_%d.txt' % i)
targets.append(t)
formula = '( F catch0 | F catch1 | F catch2 | F catch3 ) & ( ! col4 U end )'
# Construct the full-state fsa using scheck
# (full-state ensures proper MDP after product)
logger.info('Formula: %s' % formula)
fsa = lomap.Fsa()
fsa.fsa_from_cosafe_formula(formula)
fsa.add_trap_state()
# Classical
with lomap.Timer(
'Classical (non-incremental) Method: Case 2b - Save at least one friendly'
):
lomap.classical_synthesis(vehicle_mdp, fsa, targets, set_props_2)
pass
# Incremental
targets_inc = [targets[-1]] + targets[:-1]
assumed_props = [('ped0c2', 'ped1c2', 'ped2c2', 'ped3c2'),
('ped1c2', 'ped2c2', 'ped3c2'), ('ped2c2', 'ped3c2'),
('ped3c2'), ()]
with lomap.Timer(
'Incremental Method: Case 2b - Save at least one friendly'):
lomap.incremental_synthesis(vehicle_mdp, fsa, targets_inc, set_props_2,
assumed_props)
pass
def case_3b():
# Read the vehicle model
vehicle = lomap.Ts()
vehicle.read_from_file('./vehicle_2.txt')
# Convert the vehicle model to an MDP
vehicle_mdp = lomap.Markov()
vehicle_mdp.mdp_from_det_ts(vehicle)
# Read the models of the guards
targets = []
for i in range(1, 7):
t = lomap.Markov()
t.read_from_file('./trap_%d.txt' % i)
targets.append(t)
# Reach end safely
formula = '! unsafe U end'
# Construct the full-state fsa using scheck
# (full-state ensures proper MDP after product)
logger.info('Formula: %s' % formula)
fsa = lomap.Fsa()
fsa.fsa_from_cosafe_formula(formula)
fsa.add_trap_state()
#fsa.visualize()
# Classical
with lomap.Timer('Classical (non-incremental) Method: Case 3b - Order 2'):
lomap.classical_synthesis(vehicle_mdp, fsa, targets, set_props_3)
pass
# Incremental
targets_inc = [
targets[2], targets[3], targets[0], targets[1], targets[5], targets[4]
]
with lomap.Timer('Incremental Method: Case 3b - Order 2'):
lomap.incremental_synthesis(vehicle_mdp, fsa, targets_inc, set_props_3)
pass
def animate(i):
global quad, view, planner, cmd, anim_state
logger.debug('Vehicle location - Frame %d: %s' % (i, (quad.x, quad.y)))
toggle_local_reqs(i)
quad.sense()
if not i % 2:
# Draw the quad
view.draw_quad()
else:
# Plan and the draw the path for the next time step
with lomap.Timer('Online Computation - Frame %d:' % i):
cmd, path = planner.next_command()
view.draw_path(path)
quad.move_quad(cmd)
def main():
Rho = namedtuple('Rho', ['lower', 'upper'])
rhos = [Rho(lower=0.98, upper=1.04), Rho(lower=0.98, upper=1.04)]
pdb.set_trace()
# Case Study 1
# with lomap.Timer('IJRR 2013 Case-Study 1'):
# r1 = lomap.Ts()
# r2 = lomap.Ts()
# r1.read_from_file('./robot_1.txt')
# r2.read_from_file('./robot_2.txt')
# ts_tuple = (r1, r2)
# formula = '[]<>gather && [](r1gather -> X(!r1gather U r1upload)) && [](r2gather -> X(!r2gather U r2upload))'
# opt_prop = set(['gather'])
# logger.info('Formula: %s', formula)
# logger.info('opt_prop: %s', opt_prop)
# prefix_length, prefixes, suffix_cycle_cost, suffix_cycles = lomap.multi_agent_optimal_run(ts_tuple, formula, opt_prop)
# logger.info('Cost: %d', suffix_cycle_cost)
# logger.info('Prefix length: %d', prefix_length)
# # Find the controls that will produce this run
# control_prefixes = []
# control_suffix_cycles = []
# for i in range(0, len(ts_tuple)):
# ts = ts_tuple[i]
# control_prefixes.append(ts.controls_from_run(prefixes[i]))
# control_suffix_cycles.append(ts.controls_from_run(suffix_cycles[i]))
# logger.info('%s run prefix: %s', ts.name, prefixes[i])
# logger.info('%s control perfix: %s', ts.name, control_prefixes[i])
# logger.info('%s suffix cycle: %s', ts.name, suffix_cycles[i])
# logger.info('%s control suffix cycle: %s', ts.name, control_suffix_cycles[i])
#
# logger.info('<><><> <><><> <><><>')
# Case Study 4
with lomap.Timer('IJRR 2013 Case-Study 4'):
r1 = lomap.Ts()
r2 = lomap.Ts()
r1.read_from_file('./robot_1.txt')
r2.read_from_file('./robot_2.txt')
ts_tuple = (r1, r2)
pdb.set_trace()
formula = '[]<>gather && [](gather->(r1gather4 && r2gather2)) && [](r1gather -> X(!r1gather U r1upload)) && [](r2gather -> X(!r2gather U r2upload))'
opt_prop = set(['r1gather4', 'r2gather2'])
logger.info('Formula: %s', formula)
logger.info('opt_prop: %s', opt_prop)
prefix_length, prefixes, suffix_cycle_cost, suffix_cycles = lomap.multi_agent_optimal_run(
ts_tuple, formula, opt_prop)
logger.info('Cost: %d', suffix_cycle_cost)
logger.info('Prefix length: %d', prefix_length)
# Find the controls that will produce this run
control_prefixes = []
control_suffix_cycles = []
for i in range(0, len(ts_tuple)):
ts = ts_tuple[i]
control_prefixes.append(ts.controls_from_run(prefixes[i]))
control_suffix_cycles.append(ts.controls_from_run(
suffix_cycles[i]))
logger.info('%s run prefix: %s', ts.name, prefixes[i])
logger.info('%s control perfix: %s', ts.name, control_prefixes[i])
logger.info('%s suffix cycle: %s', ts.name, suffix_cycles[i])
logger.info('%s control suffix cycle: %s', ts.name,
control_suffix_cycles[i])
logger.info('<><><> <><><> <><><>')
# Select the case-study to run
case = 'case2'
# Set the mission specification
global_spec, local_spec, prio, init_x, init_y, sensing_range = case_studies[
case]
# Create the environment
env = Environment(case)
# Create the quadrotor
quad = Quadrotor(env, init_x, init_y, sensing_range)
# Create the view
view = View(env, quad)
with lomap.Timer('Offline Computation'):
planner = Planner(env, quad, global_spec, local_spec, prio)
# Animation
anim = animation.FuncAnimation(view.fig,
animate,
frames=list(range(0, 200)),
interval=250,
repeat=False)
if video_name:
anim.save(video_name)
exit(0)
plt.show()