def main():
Rho = namedtuple('Rho', ['lower', 'upper'])
rhos = [Rho(lower=0.98, upper=1.04), Rho(lower=0.98, upper=1.04)]
# # Case Study 1
# with Timer('IJRR 2013 Case-Study 1'):
# r1 = Ts.load('./robot_1.yaml')
# r2 = Ts.load('./robot_2.yaml')
# 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 2
with Timer('IJRR 2013 Case-Study 2'):
r1 = Ts.load('./robot_1.yaml')
r2 = Ts.load('./robot_2.yaml')
ts_tuple = (r1, r2)
formula = ('[]<>gather && [](gather->(r1gather && r2gather)) '
'&& [](r1gather -> X(!r1gather U r1upload)) '
'&& [](r2gather -> X(!r2gather U r2upload))')
opt_prop = set(['r1gather','r2gather'])
logger.info('Formula: %s', formula)
logger.info('opt_prop: %s', opt_prop)
prefix_length, prefixes, suffix_cycle_cost, suffix_cycles = \
lomap.robust_multi_agent_optimal_run(ts_tuple, rhos, 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 3
with Timer('IJRR 2013 Case-Study 3'):
r1 = Ts.load('./robot_1.yaml')
r2 = Ts.load('./robot_2.yaml')
ts_tuple = (r1, r2)
formula = ('[]<>gather && [](gather->(r1gather && r2gather)) '
'&& [](r1gather -> X(!r1gather U r1upload)) '
'&& [](r2gather -> X(!r2gather U r2upload)) '
'&& [](!(r1gather1 && r2gather1) && !(r1gather2 && r2gather2)'
'&& !(r1gather3 && r2gather3) && !(r1gather4 && r2gather4))')
opt_prop = set(['r1gather','r2gather'])
logger.info('Formula: %s', formula)
logger.info('opt_prop: %s', opt_prop)
prefix_length, prefixes, suffix_cycle_cost, suffix_cycles = \
lomap.robust_multi_agent_optimal_run(ts_tuple, rhos, 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 Timer('IJRR 2013 Case-Study 4'):
r1 = Ts.load('./robot_1.yaml')
r2 = Ts.load('./robot_2.yaml')
ts_tuple = (r1, r2)
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('<><><> <><><> <><><>')
# Case Study 4 w/ sync
with Timer('IJRR 2013 Case-Study 4 (w/ sync)'):
r1 = Ts.load('./robot_1.yaml')
r2 = Ts.load('./robot_2.yaml')
ts_tuple = (r1, r2)
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.robust_multi_agent_optimal_run(ts_tuple, rhos, 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])
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('<><><> <><><> <><><>')
def main():
Rho = namedtuple('Rho', ['lower', 'upper'])
rhos = [Rho(lower=0.98, upper=1.04), Rho(lower=0.98, upper=1.04)]
# 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 2
with lomap.Timer('IJRR 2013 Case-Study 2'):
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 && [](gather->(r1gather && r2gather)) && [](r1gather -> X(!r1gather U r1upload)) && [](r2gather -> X(!r2gather U r2upload))'
opt_prop = set(['r1gather','r2gather'])
logger.info('Formula: %s', formula)
logger.info('opt_prop: %s', opt_prop)
prefix_length, prefixes, suffix_cycle_cost, suffix_cycles = lomap.robust_multi_agent_optimal_run(ts_tuple, rhos, 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 3
with lomap.Timer('IJRR 2013 Case-Study 3'):
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 && [](gather->(r1gather && r2gather)) && [](r1gather -> X(!r1gather U r1upload)) && [](r2gather -> X(!r2gather U r2upload)) && [](!(r1gather1 && r2gather1) && !(r1gather2 && r2gather2) && !(r1gather3 && r2gather3) && !(r1gather4 && r2gather4))'
opt_prop = set(['r1gather','r2gather'])
logger.info('Formula: %s', formula)
logger.info('opt_prop: %s', opt_prop)
prefix_length, prefixes, suffix_cycle_cost, suffix_cycles = lomap.robust_multi_agent_optimal_run(ts_tuple, rhos, 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)
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('<><><> <><><> <><><>')
# Case Study 4 w/ sync
with lomap.Timer('IJRR 2013 Case-Study 4 (w/ sync)'):
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 && [](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.robust_multi_agent_optimal_run(ts_tuple, rhos, 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])