def test_direct_transcription_continuous_time(self):
# Test that the continuous-time constructor is also spelled correctly.
plant = LinearSystem(A=[0.], B=[1.], C=[1.], D=[0.])
context = plant.CreateDefaultContext()
dirtran = DirectTranscription(plant, context, num_time_samples=3,
fixed_timestep=TimeStep(0.1))
self.assertEqual(len(dirtran.linear_equality_constraints()), 3)
def test_direct_transcription_continuous_time(self):
# Test that the continuous-time constructor is also spelled correctly.
plant = LinearSystem(A=[0.], B=[1.], C=[1.], D=[0.])
context = plant.CreateDefaultContext()
dirtran = DirectTranscription(plant, context, num_time_samples=3,
fixed_timestep=TimeStep(0.1))
self.assertEqual(len(dirtran.linear_equality_constraints()), 3)
def test_direct_transcription_continuous_time(self):
# Test that the continuous-time constructor is also spelled correctly.
plant = LinearSystem(A=[0.], B=[1.], C=[1.], D=[0.])
context = plant.CreateDefaultContext()
dirtran = DirectTranscription(plant,
context,
num_time_samples=3,
fixed_timestep=TimeStep(0.1))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("once", DrakeDeprecationWarning)
self.assertEqual(len(dirtran.linear_equality_constraints()), 3)
self.assertEqual(len(dirtran.prog().linear_equality_constraints()), 3)
def test_deprecated_math_prog_methods(self):
plant = LinearSystem(A=[0.], B=[1.], C=[1.], D=[0.])
context = plant.CreateDefaultContext()
dirtran = DirectTranscription(plant,
context,
num_time_samples=3,
fixed_timestep=TimeStep(0.1))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("once", DrakeDeprecationWarning)
dirtran.num_vars()
self.assertEqual(len(w), 1)
expected_message = ("Use trajopt.prog().num_vars(...) instead "
"of trajopt.num_vars(...).")
self.assertIn(expected_message, str(w[0].message))
# Test a small subset of the exhaustive list.
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("once", DrakeDeprecationWarning)
x = dirtran.NewContinuousVariables(3, 'x')
dirtran.AddCost(x[0] + 2)
dirtran.AddLinearCost(x[0])
dirtran.AddConstraint(x[1] == x[2])
self.assertEqual(len(w), 4)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("once", DrakeDeprecationWarning)
result = mp.Solve(dirtran)
self.assertEqual(len(w), 1)
expected_message = (
"The trajectory optimization classes no longer derive from "
"MathematicalProgram. Use Solve(trajopt.prog()).")
self.assertIn(expected_message, str(w[0].message))
def _do_test_direct_transcription(self, use_deprecated_solve):
# Integrator.
plant = LinearSystem(A=[0.], B=[1.], C=[1.], D=[0.], time_period=0.1)
context = plant.CreateDefaultContext()
dirtran = DirectTranscription(plant, context, num_time_samples=21)
# Spell out most of the methods, regardless of whether they make sense
# as a consistent optimization. The goal is to check the bindings,
# not the implementation.
t = dirtran.time()
dt = dirtran.fixed_timestep()
x = dirtran.state()
x2 = dirtran.state(2)
x0 = dirtran.initial_state()
xf = dirtran.final_state()
u = dirtran.input()
u2 = dirtran.input(2)
dirtran.AddRunningCost(x.dot(x))
dirtran.AddConstraintToAllKnotPoints(u[0] == 0)
dirtran.AddFinalCost(2*x.dot(x))
initial_u = PiecewisePolynomial.ZeroOrderHold([0, .3*21],
np.zeros((1, 2)))
initial_x = PiecewisePolynomial()
dirtran.SetInitialTrajectory(initial_u, initial_x)
if use_deprecated_solve:
with catch_drake_warnings(expected_count=6):
dirtran.Solve()
times = dirtran.GetSampleTimes()
inputs = dirtran.GetInputSamples()
states = dirtran.GetStateSamples()
input_traj = dirtran.ReconstructInputTrajectory()
state_traj = dirtran.ReconstructStateTrajectory()
else:
result = mp.Solve(dirtran)
times = dirtran.GetSampleTimes(result)
inputs = dirtran.GetInputSamples(result)
states = dirtran.GetStateSamples(result)
input_traj = dirtran.ReconstructInputTrajectory(result)
state_traj = dirtran.ReconstructStateTrajectory(result)
def test_direct_transcription(self):
# Integrator.
plant = LinearSystem(A=[0.], B=[1.], C=[1.], D=[0.], time_period=0.1)
context = plant.CreateDefaultContext()
dirtran = DirectTranscription(plant, context, num_time_samples=21)
# Spell out most of the methods, regardless of whether they make sense
# as a consistent optimization. The goal is to check the bindings,
# not the implementation.
t = dirtran.time()
dt = dirtran.fixed_timestep()
x = dirtran.state()
x2 = dirtran.state(2)
x0 = dirtran.initial_state()
xf = dirtran.final_state()
u = dirtran.input()
u2 = dirtran.input(2)
dirtran.AddRunningCost(x.dot(x))
dirtran.AddConstraintToAllKnotPoints(u[0] == 0)
dirtran.AddFinalCost(2 * x.dot(x))
initial_u = PiecewisePolynomial.ZeroOrderHold([0, .3 * 21],
np.zeros((1, 2)))
initial_x = PiecewisePolynomial()
dirtran.SetInitialTrajectory(initial_u, initial_x)
result = mp.Solve(dirtran.prog())
times = dirtran.GetSampleTimes(result)
inputs = dirtran.GetInputSamples(result)
states = dirtran.GetStateSamples(result)
input_traj = dirtran.ReconstructInputTrajectory(result)
state_traj = dirtran.ReconstructStateTrajectory(result)
def _do_test_direct_transcription(self, use_deprecated_solve):
# Integrator.
plant = LinearSystem(A=[0.], B=[1.], C=[1.], D=[0.], time_period=0.1)
context = plant.CreateDefaultContext()
dirtran = DirectTranscription(plant, context, num_time_samples=21)
# Spell out most of the methods, regardless of whether they make sense
# as a consistent optimization. The goal is to check the bindings,
# not the implementation.
t = dirtran.time()
dt = dirtran.fixed_timestep()
x = dirtran.state()
x2 = dirtran.state(2)
x0 = dirtran.initial_state()
xf = dirtran.final_state()
u = dirtran.input()
u2 = dirtran.input(2)
dirtran.AddRunningCost(x.dot(x))
dirtran.AddConstraintToAllKnotPoints(u[0] == 0)
dirtran.AddFinalCost(2*x.dot(x))
initial_u = PiecewisePolynomial.ZeroOrderHold([0, .3*21],
np.zeros((1, 2)))
initial_x = PiecewisePolynomial()
dirtran.SetInitialTrajectory(initial_u, initial_x)
if use_deprecated_solve:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always', DrakeDeprecationWarning)
dirtran.Solve()
times = dirtran.GetSampleTimes()
inputs = dirtran.GetInputSamples()
states = dirtran.GetStateSamples()
input_traj = dirtran.ReconstructInputTrajectory()
state_traj = dirtran.ReconstructStateTrajectory()
self.assertEqual(len(w), 6)
else:
result = mp.Solve(dirtran)
times = dirtran.GetSampleTimes(result)
inputs = dirtran.GetInputSamples(result)
states = dirtran.GetStateSamples(result)
input_traj = dirtran.ReconstructInputTrajectory(result)
state_traj = dirtran.ReconstructStateTrajectory(result)