def test_pendulum_max_time_lagrange_constrained(ode_solver): # Load pendulum_min_time_Lagrange biorbd_model_path = (TestUtils.bioptim_folder() + "/examples/optimal_time_ocp/models/pendulum.bioMod", ) # --- Options --- # biorbd_model = biorbd.Model(biorbd_model_path[0]) # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TIME, weight=-1, max_bound=1) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # ------------- # with pytest.raises( TypeError, match=re.escape( "minimize_time() got an unexpected keyword argument 'max_bound'" )): OptimalControlProgram(biorbd_model, dynamics, 10, 2, objective_functions=objective_functions)
def prepare_ocp(biorbd_model_path, final_time, number_shooting_points, ode_solver=OdeSolver.RK): # --- Options --- #nq # Model path biorbd_model = biorbd.Model(biorbd_model_path) nq = biorbd_model.nbQ() # Problem parameters tau_min, tau_max, tau_init = -100, 100, 0 # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # Constraints constraints = ConstraintList() constraints.add(ConstraintFcn.ALIGN_SEGMENT_WITH_CUSTOM_RT, node=Node.ALL, segment_idx=2, rt_idx=0) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][2, [0, -1]] = [-1.57, 1.57] x_bounds[0][nq:, [0, -1]] = 0 # Initial guess x_init = InitialGuessList() x_init.add([0] * (biorbd_model.nbQ() + biorbd_model.nbQdot())) # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * biorbd_model.nbGeneralizedTorque(), [tau_max] * biorbd_model.nbGeneralizedTorque()) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model.nbGeneralizedTorque()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, number_shooting_points, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints, ode_solver=ode_solver, )
def test_pendulum_max_time_lagrange_constrained(ode_solver): # Load pendulum_min_time_Lagrange biorbd_model_path = (TestUtils.bioptim_folder() + "/examples/optimal_time_ocp/pendulum.bioMod", ) # --- Options --- # biorbd_model = biorbd.Model(biorbd_model_path[0]) # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TIME, weigth=-1, max_bound=1) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # ------------- # with pytest.raises( RuntimeError, match= "ObjectiveFcn.Lagrange.MINIMIZE_TIME cannot have max_bound. Please either use MAYER or constraint", ): OptimalControlProgram(biorbd_model, dynamics, 10, 2, objective_functions=objective_functions)
def prepare_ocp( biorbd_model_path, phase_time, n_shooting, muscle_activations_ref, contact_forces_ref, ode_solver=OdeSolver.RK4() ): # Model path biorbd_model = biorbd.Model(biorbd_model_path) tau_min, tau_max, tau_init = -500, 500, 0 activation_min, activation_max, activation_init = 0, 1, 0.5 # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.TRACK_CONTROL, key="muscles", target=muscle_activations_ref) objective_functions.add(ObjectiveFcn.Lagrange.TRACK_CONTACT_FORCES, target=contact_forces_ref) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="qdot", weight=0.001) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", weight=0.001) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="muscles", weight=0.001) # objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="torque", weight=0.001) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.MUSCLE_DRIVEN, with_torque=True, with_contact=True) # Path constraint n_q = biorbd_model.nbQ() n_qdot = n_q pose_at_first_node = [0, 0, -0.75, 0.75] # Initialize x_bounds x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, 0] = pose_at_first_node + [0] * n_qdot # Initial guess x_init = InitialGuessList() x_init.add(pose_at_first_node + [0] * n_qdot) # Define control path constraint u_bounds = BoundsList() u_bounds.add( [tau_min] * biorbd_model.nbGeneralizedTorque() + [activation_min] * biorbd_model.nbMuscleTotal(), [tau_max] * biorbd_model.nbGeneralizedTorque() + [activation_max] * biorbd_model.nbMuscleTotal(), ) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model.nbGeneralizedTorque() + [activation_init] * biorbd_model.nbMuscleTotal()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, phase_time, x_init, u_init, x_bounds, u_bounds, objective_functions=objective_functions, ode_solver=ode_solver, )
def prepare_ocp(biorbd_model_path, final_time, number_shooting_points, ode_solver=OdeSolver.RK): # --- Options --- # # Model path biorbd_model = biorbd.Model(biorbd_model_path) tau_min, tau_max, tau_init = -1, 1, 0 muscle_min, muscle_max, muscle_init = 0, 1, 0.5 # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_MUSCLES_CONTROL) objective_functions.add(ObjectiveFcn.Mayer.ALIGN_MARKERS, first_marker_idx=0, second_marker_idx=5) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.MUSCLE_ACTIVATIONS_AND_TORQUE_DRIVEN_WITH_CONTACT) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, 0] = (0, 0.07, 1.4, 0, 0, 0) # Initial guess x_init = InitialGuessList() x_init.add([1.57] * biorbd_model.nbQ() + [0] * biorbd_model.nbQdot()) # Define control path constraint u_bounds = BoundsList() u_bounds.add( [tau_min] * biorbd_model.nbGeneralizedTorque() + [muscle_min] * biorbd_model.nbMuscleTotal(), [tau_max] * biorbd_model.nbGeneralizedTorque() + [muscle_max] * biorbd_model.nbMuscleTotal(), ) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model.nbGeneralizedTorque() + [muscle_init] * biorbd_model.nbMuscleTotal()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, number_shooting_points, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, ode_solver=ode_solver, )
def prepare_ocp( biorbd_model_path, final_time, number_shooting_points, ode_solver=OdeSolver.RK, weight=1, min_time=0, max_time=np.inf, ): # --- Options --- # biorbd_model = biorbd.Model(biorbd_model_path) tau_min, tau_max, tau_init = -100, 100, 0 n_q = biorbd_model.nbQ() n_qdot = biorbd_model.nbQdot() n_tau = biorbd_model.nbGeneralizedTorque() # Add objective functions objective_functions = ObjectiveList() # A weight of -1 will maximize time objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_TIME, weight=weight, min_bound=min_time, max_bound=max_time) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, [0, -1]] = 0 x_bounds[0][n_q - 1, -1] = 3.14 # Initial guess x_init = InitialGuessList() x_init.add([0] * (n_q + n_qdot)) # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * n_tau, [tau_max] * n_tau) u_bounds[0][n_tau - 1, :] = 0 u_init = InitialGuessList() u_init.add([tau_init] * n_tau) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, number_shooting_points, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, ode_solver=ode_solver, )
def prepare_ocp(biorbd_model_path, final_time, n_shooting, x_warm=None, use_sx=False, n_threads=1): # --- Options --- # # Model path biorbd_model = biorbd.Model(biorbd_model_path) tau_min, tau_max, tau_init = -50, 50, 0 muscle_min, muscle_max, muscle_init = 0, 1, 0.5 # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=10) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, weight=10) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_MUSCLES_CONTROL, weight=10) objective_functions.add( ObjectiveFcn.Mayer.SUPERIMPOSE_MARKERS, weight=100000, first_marker="target", second_marker="COM_hand" ) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.MUSCLE_DRIVEN, with_residual_torque=True) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, 0] = (1.0, 1.0, 0, 0) # Initial guess if x_warm is None: x_init = InitialGuess([1.57] * biorbd_model.nbQ() + [0] * biorbd_model.nbQdot()) else: x_init = InitialGuess(x_warm, interpolation=InterpolationType.EACH_FRAME) # Define control path constraint u_bounds = BoundsList() u_bounds.add( [tau_min] * biorbd_model.nbGeneralizedTorque() + [muscle_min] * biorbd_model.nbMuscleTotal(), [tau_max] * biorbd_model.nbGeneralizedTorque() + [muscle_max] * biorbd_model.nbMuscleTotal(), ) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model.nbGeneralizedTorque() + [muscle_init] * biorbd_model.nbMuscleTotal()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, use_sx=use_sx, n_threads=n_threads, )
def prepare_ocp(biorbd_model, final_time, number_shooting_points, markers_ref, tau_ref, ode_solver=OdeSolver.RK): # --- Options --- # tau_min, tau_max, tau_init = -100, 100, 0 n_q = biorbd_model.nbQ() n_qdot = biorbd_model.nbQdot() n_tau = biorbd_model.nbGeneralizedTorque() # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.TRACK_MARKERS, axis_to_track=[Axe.Y, Axe.Z], weight=100, target=markers_ref) objective_functions.add(ObjectiveFcn.Lagrange.TRACK_TORQUE, target=tau_ref) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, 0] = 0 # Initial guess x_init = InitialGuessList() x_init.add([0] * (n_q + n_qdot)) # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * n_tau, [tau_max] * n_tau) u_init = InitialGuessList() u_init.add([tau_init] * n_tau) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, number_shooting_points, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, ode_solver=ode_solver, )
def test_double_update_bounds_and_init(): bioptim_folder = TestUtils.bioptim_folder() biorbd_model = biorbd.Model(bioptim_folder + "/examples/track/cube_and_line.bioMod") nq = biorbd_model.nbQ() ns = 10 dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) ocp = OptimalControlProgram(biorbd_model, dynamics, ns, 1.0) x_bounds = Bounds(-np.ones((nq * 2, 1)), np.ones((nq * 2, 1))) u_bounds = Bounds(-2.0 * np.ones((nq, 1)), 2.0 * np.ones((nq, 1))) ocp.update_bounds(x_bounds, u_bounds) expected = np.array([[-1] * (nq * 2) * (ns + 1) + [-2] * nq * ns]).T np.testing.assert_almost_equal(ocp.v.bounds.min, expected) expected = np.array([[1] * (nq * 2) * (ns + 1) + [2] * nq * ns]).T np.testing.assert_almost_equal(ocp.v.bounds.max, expected) x_init = InitialGuess(0.5 * np.ones((nq * 2, 1))) u_init = InitialGuess(-0.5 * np.ones((nq, 1))) ocp.update_initial_guess(x_init, u_init) expected = np.array([[0.5] * (nq * 2) * (ns + 1) + [-0.5] * nq * ns]).T np.testing.assert_almost_equal(ocp.v.init.init, expected) x_bounds = Bounds(-2.0 * np.ones((nq * 2, 1)), 2.0 * np.ones((nq * 2, 1))) u_bounds = Bounds(-4.0 * np.ones((nq, 1)), 4.0 * np.ones((nq, 1))) ocp.update_bounds(x_bounds=x_bounds) ocp.update_bounds(u_bounds=u_bounds) expected = np.array([[-2] * (nq * 2) * (ns + 1) + [-4] * nq * ns]).T np.testing.assert_almost_equal(ocp.v.bounds.min, expected) expected = np.array([[2] * (nq * 2) * (ns + 1) + [4] * nq * ns]).T np.testing.assert_almost_equal(ocp.v.bounds.max, expected) x_init = InitialGuess(0.25 * np.ones((nq * 2, 1))) u_init = InitialGuess(-0.25 * np.ones((nq, 1))) ocp.update_initial_guess(x_init, u_init) expected = np.array([[0.25] * (nq * 2) * (ns + 1) + [-0.25] * nq * ns]).T np.testing.assert_almost_equal(ocp.v.init.init, expected) with pytest.raises( RuntimeError, match= "x_init should be built from a InitialGuess or InitialGuessList"): ocp.update_initial_guess(x_bounds, u_bounds) with pytest.raises( RuntimeError, match="x_bounds should be built from a Bounds or BoundsList"): ocp.update_bounds(x_init, u_init)
def prepare_short_ocp(biorbd_model: biorbd.Model, final_time: float, n_shooting: int): """ Prepare to build a blank short ocp to use single shooting bioptim function Parameters ---------- biorbd_model: biorbd.Model biorbd model build with the bioMod final_time: float The time at the final node n_shooting: int The number of shooting points Returns ------- The blank OptimalControlProgram """ # Add objective functions objective_functions = ObjectiveList() # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.MUSCLE_ACTIVATIONS_DRIVEN) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) # Define control path constraint u_bounds = BoundsList() u_bounds.add([0] * biorbd_model.nbMuscles(), [1] * biorbd_model.nbMuscles()) x_init = InitialGuess([0] * biorbd_model.nbQ() * 2) u_init = InitialGuess([0] * biorbd_model.nbMuscles()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, use_sx=True, )
def test_update_bounds_and_init_with_param(): def my_parameter_function(biorbd_model, value, extra_value): biorbd_model.setGravity(biorbd.Vector3d(0, 0, value + extra_value)) def my_target_function(ocp, value, target_value): return value + target_value biorbd_model = biorbd.Model(TestUtils.bioptim_folder() + "/examples/track/cube_and_line.bioMod") nq = biorbd_model.nbQ() ns = 10 g_min, g_max, g_init = -10, -6, -8 dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) parameters = ParameterList() bounds_gravity = Bounds(g_min, g_max, interpolation=InterpolationType.CONSTANT) initial_gravity = InitialGuess(g_init) parameter_objective_functions = Objective( my_target_function, weight=10, quadratic=True, custom_type=ObjectiveFcn.Parameter, target_value=-8 ) parameters.add( "gravity_z", my_parameter_function, initial_gravity, bounds_gravity, size=1, penalty_list=parameter_objective_functions, extra_value=1, ) ocp = OptimalControlProgram(biorbd_model, dynamics, ns, 1.0, parameters=parameters) x_bounds = Bounds(-np.ones((nq * 2, 1)), np.ones((nq * 2, 1))) u_bounds = Bounds(-2.0 * np.ones((nq, 1)), 2.0 * np.ones((nq, 1))) ocp.update_bounds(x_bounds, u_bounds) expected = np.array([[-1] * (nq * 2) * (ns + 1) + [-2] * nq * ns]).T np.testing.assert_almost_equal(ocp.v.bounds.min, np.append(expected, [g_min])[:, np.newaxis]) expected = np.array([[1] * (nq * 2) * (ns + 1) + [2] * nq * ns]).T np.testing.assert_almost_equal(ocp.v.bounds.max, np.append(expected, [g_max])[:, np.newaxis]) x_init = InitialGuess(0.5 * np.ones((nq * 2, 1))) u_init = InitialGuess(-0.5 * np.ones((nq, 1))) ocp.update_initial_guess(x_init, u_init) expected = np.array([[0.5] * (nq * 2) * (ns + 1) + [-0.5] * nq * ns]).T np.testing.assert_almost_equal(ocp.v.init.init, np.append(expected, [g_init])[:, np.newaxis])
def prepare_ocp(biorbd_model, final_time, number_shooting_points, x0, use_sx=False, n_threads=8): # --- Options --- # # Model path activation_min, activation_max, activation_init = 0, 1, 0.1 excitation_min, excitation_max, excitation_init = 0, 1, 0.2 # Add objective functions objective_functions = ObjectiveList() # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.MUSCLE_EXCITATIONS_DRIVEN) # State path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) # add muscle activation bounds x_bounds[0].concatenate( Bounds([activation_min] * biorbd_model.nbMuscles(), [activation_max] * biorbd_model.nbMuscles()) ) # Control path constraint u_bounds = BoundsList() u_bounds.add([excitation_min] * biorbd_model.nbMuscleTotal(), [excitation_max] * biorbd_model.nbMuscleTotal()) # Initial guesses x_init = InitialGuess( np.tile(np.concatenate((x0, [activation_init] * biorbd_model.nbMuscles())), (number_shooting_points + 1, 1)).T, interpolation=InterpolationType.EACH_FRAME, ) u0 = np.array([excitation_init] * biorbd_model.nbMuscles()) u_init = InitialGuess(np.tile(u0, (number_shooting_points, 1)).T, interpolation=InterpolationType.EACH_FRAME) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, number_shooting_points, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, use_sx=use_sx, n_threads=n_threads, )
def test_pendulum_min_time_lagrange_constrained(ode_solver): # Load pendulum_min_time_Lagrange PROJECT_FOLDER = Path(__file__).parent / ".." biorbd_model_path = (str(PROJECT_FOLDER) + "/examples/optimal_time_ocp/pendulum.bioMod",) # --- Options --- # biorbd_model = biorbd.Model(biorbd_model_path[0]) # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TIME, min_bound=1) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # ------------- # with pytest.raises( RuntimeError, match="ObjectiveFcn.Lagrange.MINIMIZE_TIME cannot have min_bound. Please either use MAYER or constraint", ): OptimalControlProgram(biorbd_model, dynamics, 10, 2, objective_functions=objective_functions)
def prepare_ocp( biorbd_model_path: str, problem_type_custom: bool = True, ode_solver: OdeSolver = OdeSolver.RK4(), use_sx: bool = False, ) -> OptimalControlProgram: """ Prepare the program Parameters ---------- biorbd_model_path: str The path of the biorbd model problem_type_custom: bool If the preparation should be done using the user-defined dynamic function or the normal TORQUE_DRIVEN. They should return the same results ode_solver: OdeSolver The type of ode solver used use_sx: bool If the program should be constructed using SX instead of MX (longer to create the CasADi graph, faster to solve) Returns ------- The ocp ready to be solved """ # --- Options --- # # Model path biorbd_model = biorbd.Model(biorbd_model_path) # Problem parameters n_shooting = 30 final_time = 2 # Add objective functions objective_functions = Objective(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=100, multi_thread=False) # Dynamics dynamics = DynamicsList() expand = False if isinstance(ode_solver, OdeSolver.IRK) else True if problem_type_custom: dynamics.add(custom_configure, dynamic_function=custom_dynamic, my_additional_factor=1, expand=expand) else: dynamics.add(DynamicsFcn.TORQUE_DRIVEN, dynamic_function=custom_dynamic, expand=expand) # Constraints constraints = ConstraintList() constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.START, first_marker="m0", second_marker="m1") constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m2") # Path constraint x_bounds = QAndQDotBounds(biorbd_model) x_bounds[1:6, [0, -1]] = 0 x_bounds[2, -1] = 1.57 # Initial guess x_init = InitialGuess([0] * (biorbd_model.nbQ() + biorbd_model.nbQdot())) # Define control path constraint tau_min, tau_max, tau_init = -100, 100, 0 u_bounds = Bounds([tau_min] * biorbd_model.nbGeneralizedTorque(), [tau_max] * biorbd_model.nbGeneralizedTorque()) u_init = InitialGuess([tau_init] * biorbd_model.nbGeneralizedTorque()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints, ode_solver=ode_solver, use_sx=use_sx, )
def prepare_ocp_phase_transitions( biorbd_model_path: str, with_constraints: bool, with_mayer: bool, with_lagrange: bool, ) -> OptimalControlProgram: """ Parameters ---------- biorbd_model_path: str The path to the bioMod Returns ------- The ocp ready to be solved """ # Model path biorbd_model = ( biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path), ) # Problem parameters n_shooting = (20, 20, 20, 20) final_time = (2, 5, 4, 2) tau_min, tau_max, tau_init = -100, 100, 0 # Add objective functions objective_functions = ObjectiveList() if with_lagrange: objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100, phase=0) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100, phase=1) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100, phase=2) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100, phase=3) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_COM_VELOCITY, weight=0, phase=3, axis=None) if with_mayer: objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_TIME) objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_COM_POSITION, phase=0, node=1) objective_functions.add( minimize_difference, custom_type=ObjectiveFcn.Mayer, node=Node.TRANSITION, weight=100, phase=1, get_all_nodes_at_once=True, quadratic=True, ) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) dynamics.add(DynamicsFcn.TORQUE_DRIVEN) dynamics.add(DynamicsFcn.TORQUE_DRIVEN) dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # Constraints constraints = ConstraintList() if with_constraints: constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.START, first_marker="m0", second_marker="m1", phase=0) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=2, first_marker="m0", second_marker="m1", phase=0) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m2", phase=0) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m1", phase=1) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m2", phase=2) constraints.add(custom_func_track_markers, node=Node.ALL, first_marker="m0", second_marker="m1", phase=3) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) x_bounds[0][[1, 3, 4, 5], 0] = 0 x_bounds[-1][[1, 3, 4, 5], -1] = 0 x_bounds[0][2, 0] = 0.0 x_bounds[2][2, [0, -1]] = [0.0, 1.57] # Initial guess x_init = InitialGuessList() x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) # Define phase transitions phase_transitions = PhaseTransitionList() phase_transitions.add(PhaseTransitionFcn.IMPACT, phase_pre_idx=1) phase_transitions.add(PhaseTransitionFcn.CONTINUOUS, phase_pre_idx=2, idx_1=1, idx_2=3) phase_transitions.add(PhaseTransitionFcn.CYCLIC) return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints, phase_transitions=phase_transitions, )
def prepare_ocp( biorbd_model_path: str, final_time: float, n_shooting: int, initialize_near_solution: bool, ode_solver: OdeSolver = OdeSolver.RK4(), constr: bool = True, use_sx: bool = False, ) -> OptimalControlProgram: """ Prepare the ocp Parameters ---------- biorbd_model_path: str The path to the bioMod file final_time: float The time at the final node n_shooting: int The number of shooting points initialize_near_solution: bool If the initial guess should be almost the solution (this is merely to reduce the time of the tests) ode_solver: OdeSolver The ode solver to use constr: bool If the constraint should be applied (this is merely to reduce the time of the tests) use_sx: bool If SX CasADi variables should be used Returns ------- The OptimalControlProgram ready to be solved """ biorbd_model = biorbd.Model(biorbd_model_path) # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # Constraints if constr: constraints = ConstraintList() constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.START, first_marker="m0", second_marker="m4") constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m5") constraints.add(ConstraintFcn.TRACK_MARKER_WITH_SEGMENT_AXIS, node=Node.ALL, marker="m1", segment="seg_rt", axis=Axis.X) else: constraints = ConstraintList() # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) for i in range(1, 8): if i != 3: x_bounds[0][i, [0, -1]] = 0 x_bounds[0][2, -1] = 1.57 # Initial guess x_init = InitialGuessList() x_init.add([0] * (biorbd_model.nbQ() + biorbd_model.nbQdot())) if initialize_near_solution: for i in range(2): x_init[0].init[i] = 1.5 for i in range(4, 6): x_init[0].init[i] = 0.7 for i in range(6, 8): x_init[0].init[i] = 0.6 # Define control path constraint tau_min, tau_max, tau_init = -100, 100, 0 u_bounds = BoundsList() u_bounds.add([tau_min] * biorbd_model.nbGeneralizedTorque(), [tau_max] * biorbd_model.nbGeneralizedTorque()) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model.nbGeneralizedTorque()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints, ode_solver=ode_solver, use_sx=use_sx, )
def prepare_ocp( biorbd_model_path: str, n_shooting: int, final_time: float, ode_solver: OdeSolver = OdeSolver.RK4() ) -> OptimalControlProgram: """ Prepare the ocp Parameters ---------- biorbd_model_path: str The path to the bioMod file n_shooting: int The number of shooting points final_time: float The time at the final node ode_solver: OdeSolver The ode solver to use Returns ------- The OptimalControlProgram ready to be solved """ biorbd_model = biorbd.Model(biorbd_model_path) # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_MARKERS, index=1, weight=-1) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) # Define control path constraint n_tau = biorbd_model.nbGeneralizedTorque( ) # biorbd_model.nbGeneralizedTorque() tau_min, tau_max, tau_init = -100, 100, 0 u_bounds = BoundsList() u_bounds.add([tau_min] * n_tau, [tau_max] * n_tau) # Initial guesses # TODO put this in a function defined before and explain what it does, and what are the variables x = np.vstack((np.zeros( (biorbd_model.nbQ(), 2)), np.ones((biorbd_model.nbQdot(), 2)))) Arm_init_D = np.zeros((3, 2)) Arm_init_D[1, 0] = 0 Arm_init_D[1, 1] = -np.pi + 0.01 Arm_init_G = np.zeros((3, 2)) Arm_init_G[1, 0] = 0 Arm_init_G[1, 1] = np.pi - 0.01 for i in range(2): Arm_Quat_D = eul2quat(Arm_init_D[:, i]) Arm_Quat_G = eul2quat(Arm_init_G[:, i]) x[6:9, i] = Arm_Quat_D[1:] x[12, i] = Arm_Quat_D[0] x[9:12, i] = Arm_Quat_G[1:] x[13, i] = Arm_Quat_G[0] x_init = InitialGuessList() x_init.add(x, interpolation=InterpolationType.LINEAR) u_init = InitialGuessList() u_init.add([tau_init] * n_tau) return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, ode_solver=ode_solver, )
def prepare_ocp( biorbd_model_path: str, final_time: float, n_shooting: int, ode_solver: OdeSolver = OdeSolver.RK4(), weight: float = 1, ) -> OptimalControlProgram: """ Prepare the optimal control program Parameters ---------- biorbd_model_path: str The path to the bioMod final_time: float The initial guess for the final time n_shooting: int The number of shooting points ode_solver: OdeSolver The ode solver to use weight: float The weighting of the minimize time objective function Returns ------- The OptimalControlProgram ready to be solved """ biorbd_model = biorbd.Model(biorbd_model_path) # Add objective functions objective_functions = ObjectiveList() # A weight of -1 will maximize time objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TIME, weight=weight) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # Path constraint n_q = biorbd_model.nbQ() n_qdot = biorbd_model.nbQdot() x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, [0, -1]] = 0 x_bounds[0][n_q - 1, -1] = 3.14 # Initial guess x_init = InitialGuessList() x_init.add([0] * (n_q + n_qdot)) # Define control path constraint n_tau = biorbd_model.nbGeneralizedTorque() tau_min, tau_max, tau_init = -100, 100, 0 u_bounds = BoundsList() u_bounds.add([tau_min] * n_tau, [tau_max] * n_tau) u_bounds[0][n_tau - 1, :] = 0 u_init = InitialGuessList() u_init.add([tau_init] * n_tau) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, ode_solver=ode_solver, )
def prepare_ocp(biorbd_model_path, phase_time, n_shooting, min_bound, max_bound): # --- Options --- # # Model path biorbd_model = biorbd.Model(biorbd_model_path) tau_min, tau_max, tau_init = -500, 500, 0 activation_min, activation_max, activation_init = 0, 1, 0.5 dof_mapping = BiMappingList() dof_mapping.add("tau", [None, None, None, 0], [3]) # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_PREDICTED_COM_HEIGHT) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.MUSCLE_DRIVEN, with_torque=True, with_contact=True) # Constraints constraints = ConstraintList() constraints.add( ConstraintFcn.TRACK_CONTACT_FORCES, min_bound=min_bound, max_bound=max_bound, node=Node.ALL_SHOOTING, contact_index=1, ) constraints.add( ConstraintFcn.TRACK_CONTACT_FORCES, min_bound=min_bound, max_bound=max_bound, node=Node.ALL_SHOOTING, contact_index=2, ) # Path constraint n_q = biorbd_model.nbQ() n_qdot = n_q pose_at_first_node = [0, 0, -0.75, 0.75] # Initialize x_bounds x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, 0] = pose_at_first_node + [0] * n_qdot # Initial guess x_init = InitialGuessList() x_init.add(pose_at_first_node + [0] * n_qdot) # Define control path constraint u_bounds = BoundsList() u_bounds.add( [tau_min] * len(dof_mapping["tau"].to_first) + [activation_min] * biorbd_model.nbMuscleTotal(), [tau_max] * len(dof_mapping["tau"].to_first) + [activation_max] * biorbd_model.nbMuscleTotal(), ) u_init = InitialGuessList() u_init.add([tau_init] * len(dof_mapping["tau"].to_first) + [activation_init] * biorbd_model.nbMuscleTotal()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, phase_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints=constraints, variable_mappings=dof_mapping, )
def prepare_ocp( biorbd_model_path: str = "models/cube.bioMod", ode_solver: OdeSolver = OdeSolver.RK4() ) -> OptimalControlProgram: """ Parameters ---------- biorbd_model_path: str The path to the bioMod ode_solver: OdeSolver The type of ode solver used Returns ------- The ocp ready to be solved """ # Model path biorbd_model = ( biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path), ) # Problem parameters n_shooting = (20, 20, 20, 20) final_time = (2, 5, 4, 2) tau_min, tau_max, tau_init = -100, 100, 0 # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=100, phase=0) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=100, phase=1) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=100, phase=2) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=100, phase=3) # Dynamics dynamics = DynamicsList() expand = False if isinstance(ode_solver, OdeSolver.IRK) else True dynamics.add(DynamicsFcn.TORQUE_DRIVEN, expand=expand) dynamics.add(DynamicsFcn.TORQUE_DRIVEN, expand=expand) dynamics.add(DynamicsFcn.TORQUE_DRIVEN, expand=expand) dynamics.add(DynamicsFcn.TORQUE_DRIVEN, expand=expand) # Constraints constraints = ConstraintList() constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.START, first_marker="m0", second_marker="m1", phase=0) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m2", phase=0) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m1", phase=1) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m2", phase=2) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m1", phase=3) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) x_bounds[0][[1, 3, 4, 5], 0] = 0 x_bounds[-1][[1, 3, 4, 5], -1] = 0 x_bounds[0][2, 0] = 0.0 x_bounds[2][2, [0, -1]] = [0.0, 1.57] # Initial guess x_init = InitialGuessList() x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) """ By default, all phase transitions (here phase 0 to phase 1, phase 1 to phase 2 and phase 2 to phase 3) are continuous. In the event that one (or more) phase transition(s) is desired to be discontinuous, as for example IMPACT or CUSTOM can be used as below. "phase_pre_idx" corresponds to the index of the phase preceding the transition. IMPACT will cause an impact related discontinuity when defining one or more contact points in the model. CUSTOM will allow to call the custom function previously presented in order to have its own phase transition. Finally, if you want a phase transition (continuous or not) between the last and the first phase (cyclicity) you can use the dedicated PhaseTransitionFcn.Cyclic or use a continuous set at the last phase_pre_idx. If for some reason, you don't want the phase transition to be hard constraint, you can specify a weight higher than zero. It will thereafter be treated as a Mayer objective function with the specified weight. """ phase_transitions = PhaseTransitionList() phase_transitions.add(PhaseTransitionFcn.CONTINUOUS, phase_pre_idx=0, states_mapping=BiMapping(range(3), range(3))) phase_transitions.add(PhaseTransitionFcn.IMPACT, phase_pre_idx=1) phase_transitions.add(custom_phase_transition, phase_pre_idx=2, coef=0.5) phase_transitions.add(PhaseTransitionFcn.CYCLIC) return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints, ode_solver=ode_solver, phase_transitions=phase_transitions, )
def prepare_ocp( biorbd_model_path: str, n_shooting: int, final_time: float, actuator_type: int = None, ode_solver: OdeSolver = OdeSolver.RK4(), ) -> OptimalControlProgram: """ Prepare the ocp Parameters ---------- biorbd_model_path: str Path to the bioMod n_shooting: int The number of shooting points final_time: float The time at final node actuator_type: int The type of actuator to use: 1 (torque activations) or 2 (torque max constraints) ode_solver: OdeSolver The ode solver to use Returns ------- The OptimalControlProgram ready to be solved """ # --- Options --- # # Model path biorbd_model = biorbd.Model(biorbd_model_path) # Problem parameters if actuator_type and actuator_type == 1: tau_min, tau_max, tau_init = -1, 1, 0 else: tau_min, tau_max, tau_init = -100, 100, 0 # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=100) # Dynamics dynamics = DynamicsList() if actuator_type: if actuator_type == 1: dynamics.add(DynamicsFcn.TORQUE_ACTIVATIONS_DRIVEN) elif actuator_type == 2: dynamics.add(DynamicsFcn.TORQUE_DRIVEN) else: raise ValueError( "actuator_type is 1 (torque activations) or 2 (torque max constraints)" ) else: expand = False if isinstance(ode_solver, OdeSolver.IRK) else True dynamics.add(DynamicsFcn.TORQUE_DRIVEN, expand=expand) # Constraints constraints = ConstraintList() constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.START, first_marker="m0", second_marker="m1") constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m2") if actuator_type == 2: constraints.add(ConstraintFcn.TORQUE_MAX_FROM_Q_AND_QDOT, node=Node.ALL_SHOOTING, min_torque=7.5) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][3:6, [0, -1]] = 0 x_bounds[0][2, [0, -1]] = [0, 1.57] # Initial guess x_init = InitialGuessList() x_init.add([0] * (biorbd_model.nbQ() + biorbd_model.nbQdot())) # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * biorbd_model.nbGeneralizedTorque(), [tau_max] * biorbd_model.nbGeneralizedTorque()) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model.nbGeneralizedTorque()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints, ode_solver=ode_solver, )
def prepare_test_ocp(with_muscles=False, with_contact=False, with_actuator=False): bioptim_folder = TestUtils.bioptim_folder() if with_muscles and with_contact or with_muscles and with_actuator or with_contact and with_actuator: raise RuntimeError( "With muscles and with contact and with_actuator together is not defined" ) elif with_muscles: biorbd_model = biorbd.Model( bioptim_folder + "/examples/muscle_driven_ocp/models/arm26.bioMod") dynamics = DynamicsList() dynamics.add(DynamicsFcn.MUSCLE_DRIVEN, with_torque=True) nx = biorbd_model.nbQ() + biorbd_model.nbQdot() nu = biorbd_model.nbGeneralizedTorque() + biorbd_model.nbMuscles() elif with_contact: biorbd_model = biorbd.Model( bioptim_folder + "/examples/muscle_driven_with_contact/models/2segments_4dof_2contacts_1muscle.bioMod" ) dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN, with_contact=True, expand=False) nx = biorbd_model.nbQ() + biorbd_model.nbQdot() nu = biorbd_model.nbGeneralizedTorque() elif with_actuator: biorbd_model = biorbd.Model( bioptim_folder + "/examples/torque_driven_ocp/models/cube.bioMod") dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) nx = biorbd_model.nbQ() + biorbd_model.nbQdot() nu = biorbd_model.nbGeneralizedTorque() else: biorbd_model = biorbd.Model( bioptim_folder + "/examples/track/models/cube_and_line.bioMod") dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) nx = biorbd_model.nbQ() + biorbd_model.nbQdot() nu = biorbd_model.nbGeneralizedTorque() x_init = InitialGuess(np.zeros((nx, 1))) u_init = InitialGuess(np.zeros((nu, 1))) x_bounds = Bounds(np.zeros((nx, 1)), np.zeros((nx, 1))) u_bounds = Bounds(np.zeros((nu, 1)), np.zeros((nu, 1))) ocp = OptimalControlProgram(biorbd_model, dynamics, 10, 1.0, x_init, u_init, x_bounds, u_bounds, use_sx=True) ocp.nlp[0].J = [[]] ocp.nlp[0].g = [[]] return ocp
def prepare_ocp(biorbd_model_path, phase_time, n_shooting, min_bound, max_bound, mu, ode_solver=OdeSolver.RK4()): # --- Options --- # # Model path biorbd_model = biorbd.Model(biorbd_model_path) tau_min, tau_max, tau_init = -500, 500, 0 tau_mapping = BiMapping([None, None, None, 0], [3]) # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_PREDICTED_COM_HEIGHT, weight=-1) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN_WITH_CONTACT) # Constraints constraints = ConstraintList() constraints.add( ConstraintFcn.CONTACT_FORCE, min_bound=min_bound, max_bound=max_bound, node=Node.ALL, contact_force_idx=1, ) constraints.add( ConstraintFcn.CONTACT_FORCE, min_bound=min_bound, max_bound=max_bound, node=Node.ALL, contact_force_idx=2, ) constraints.add( ConstraintFcn.NON_SLIPPING, node=Node.ALL, normal_component_idx=(1, 2), tangential_component_idx=0, static_friction_coefficient=mu, ) # Path constraint n_q = biorbd_model.nbQ() n_qdot = n_q pose_at_first_node = [0, 0, -0.75, 0.75] # Initialize x_bounds x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, 0] = pose_at_first_node + [0] * n_qdot # Initial guess x_init = InitialGuessList() x_init.add(pose_at_first_node + [0] * n_qdot) # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * tau_mapping.to_first.len, [tau_max] * tau_mapping.to_first.len) u_init = InitialGuessList() u_init.add([tau_init] * tau_mapping.to_first.len) return OptimalControlProgram( biorbd_model, dynamics, n_shooting, phase_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints, tau_mapping=tau_mapping, ode_solver=ode_solver, )
def prepare_ocp( biorbd_model_path: str, phase_time: float, n_shooting: int, use_actuators: bool = False, ode_solver: OdeSolver = OdeSolver.RK4(), objective_name: str = "MINIMIZE_PREDICTED_COM_HEIGHT", com_constraints: bool = False, ) -> OptimalControlProgram: """ Prepare the ocp Parameters ---------- biorbd_model_path: str The path to the bioMod file phase_time: float The time at the final node n_shooting: int The number of shooting points use_actuators: bool If torque or torque activation should be used for the dynamics ode_solver: OdeSolver The ode solver to use objective_name: str The objective function to run ('MINIMIZE_PREDICTED_COM_HEIGHT', 'MINIMIZE_COM_POSITION' or 'MINIMIZE_COM_VELOCITY') com_constraints: bool If a constraint on the COM should be applied Returns ------- The OptimalControlProgram ready to be solved """ biorbd_model = biorbd.Model(biorbd_model_path) if use_actuators: tau_min, tau_max, tau_init = -1, 1, 0 else: tau_min, tau_max, tau_init = -500, 500, 0 dof_mapping = BiMappingList() dof_mapping.add("tau", [None, None, None, 0], [3]) # Add objective functions objective_functions = ObjectiveList() if objective_name == "MINIMIZE_PREDICTED_COM_HEIGHT": objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_PREDICTED_COM_HEIGHT, weight=-1) elif objective_name == "MINIMIZE_COM_POSITION": objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_COM_POSITION, node=Node.ALL, axes=Axis.Z, weight=-1) elif objective_name == "MINIMIZE_COM_VELOCITY": objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_COM_VELOCITY, node=Node.ALL, axes=Axis.Z, weight=-1) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1 / 100) # Dynamics dynamics = DynamicsList() if use_actuators: dynamics.add(DynamicsFcn.TORQUE_ACTIVATIONS_DRIVEN, with_contact=True) else: dynamics.add(DynamicsFcn.TORQUE_DRIVEN, with_contact=True) # Constraints constraints = ConstraintList() if com_constraints: constraints.add( ConstraintFcn.TRACK_COM_VELOCITY, node=Node.ALL, min_bound=np.array([-100, -100, -100]), max_bound=np.array([100, 100, 100]), ) constraints.add( ConstraintFcn.TRACK_COM_POSITION, node=Node.ALL, min_bound=np.array([-1, -1, -1]), max_bound=np.array([1, 1, 1]), ) # Path constraint n_q = biorbd_model.nbQ() n_qdot = n_q pose_at_first_node = [0, 0, -0.5, 0.5] # Initialize x_bounds x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, 0] = pose_at_first_node + [0] * n_qdot # Initial guess x_init = InitialGuessList() x_init.add(pose_at_first_node + [0] * n_qdot) # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * len(dof_mapping["tau"].to_first), [tau_max] * len(dof_mapping["tau"].to_first)) u_init = InitialGuessList() u_init.add([tau_init] * len(dof_mapping["tau"].to_first)) return OptimalControlProgram( biorbd_model, dynamics, n_shooting, phase_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints=constraints, variable_mappings=dof_mapping, ode_solver=ode_solver, )
def prepare_ocp( biorbd_model_path, final_time, number_shooting_points, initialize_near_solution, ode_solver=OdeSolver.RK, constr=True, use_SX=False, ): # --- Options --- # # Model path biorbd_model = biorbd.Model(biorbd_model_path) # Problem parameters tau_min, tau_max, tau_init = -100, 100, 0 # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # Constraints if constr is True: constraints = ConstraintList() constraints.add(ConstraintFcn.ALIGN_MARKERS, node=Node.START, first_marker_idx=0, second_marker_idx=4) constraints.add(ConstraintFcn.ALIGN_MARKERS, node=Node.END, first_marker_idx=0, second_marker_idx=5) constraints.add( ConstraintFcn.ALIGN_MARKER_WITH_SEGMENT_AXIS, node=Node.ALL, marker_idx=1, segment_idx=2, axis=(Axe.X) ) else: constraints = ConstraintList() # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) for i in range(1, 8): if i != 3: x_bounds[0][i, [0, -1]] = 0 x_bounds[0][2, -1] = 1.57 # Initial guess x_init = InitialGuessList() x_init.add([0] * (biorbd_model.nbQ() + biorbd_model.nbQdot())) if initialize_near_solution: for i in range(2): x_init[0].init[i] = 1.5 for i in range(4, 6): x_init[0].init[i] = 0.7 for i in range(6, 8): x_init[0].init[i] = 0.6 # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * biorbd_model.nbGeneralizedTorque(), [tau_max] * biorbd_model.nbGeneralizedTorque()) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model.nbGeneralizedTorque()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, number_shooting_points, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, constraints, ode_solver=ode_solver, use_SX=use_SX, )
def partial_ocp_parameters(nb_phases): if nb_phases != 1 and nb_phases != 3: raise RuntimeError("nb_phases should be 1 or 3") biorbd_model_path = str(PROJECT_FOLDER) + "/examples/optimal_time_ocp/cube.bioMod" biorbd_model = biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path) number_shooting_points = (2, 2, 2) final_time = (2, 5, 4) time_min = [1, 3, 0.1] time_max = [2, 4, 0.8] tau_min, tau_max, tau_init = -100, 100, 0 dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) if nb_phases > 1: dynamics.add(DynamicsFcn.TORQUE_DRIVEN) dynamics.add(DynamicsFcn.TORQUE_DRIVEN) x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) if nb_phases > 1: x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) for bounds in x_bounds: for i in [1, 3, 4, 5]: bounds.min[i, [0, -1]] = 0 bounds.max[i, [0, -1]] = 0 x_bounds[0].min[2, 0] = 0.0 x_bounds[0].max[2, 0] = 0.0 if nb_phases > 1: x_bounds[2].min[2, [0, -1]] = [0.0, 1.57] x_bounds[2].max[2, [0, -1]] = [0.0, 1.57] x_init = InitialGuessList() x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) if nb_phases > 1: x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) u_bounds = BoundsList() u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) if nb_phases > 1: u_bounds.add( [tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque() ) u_bounds.add( [tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque() ) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) if nb_phases > 1: u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) return ( biorbd_model[:nb_phases], number_shooting_points[:nb_phases], final_time[:nb_phases], time_min[:nb_phases], time_max[:nb_phases], tau_min, tau_max, tau_init, dynamics, x_bounds, x_init, u_bounds, u_init, )
def prepare_ocp(phase_time_constraint, use_parameter): # --- Inputs --- # final_time = (2, 5, 4) time_min = [1, 3, 0.1] time_max = [2, 4, 0.8] ns = (20, 30, 20) biorbd_model_path = TestUtils.bioptim_folder( ) + "/examples/optimal_time_ocp/cube.bioMod" ode_solver = OdeSolver.RK4() # --- Options --- # n_phases = len(ns) # Model path biorbd_model = (biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path), biorbd.Model(biorbd_model_path)) # Problem parameters tau_min, tau_max, tau_init = -100, 100, 0 # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100, phase=0) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100, phase=1) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100, phase=2) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN, phase=0) dynamics.add(DynamicsFcn.TORQUE_DRIVEN, phase=1) dynamics.add(DynamicsFcn.TORQUE_DRIVEN, phase=2) # Constraints constraints = ConstraintList() constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.START, first_marker="m0", second_marker="m1", phase=0) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m2", phase=0) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m1", phase=1) constraints.add(ConstraintFcn.SUPERIMPOSE_MARKERS, node=Node.END, first_marker="m0", second_marker="m2", phase=2) constraints.add( ConstraintFcn.TIME_CONSTRAINT, node=Node.END, minimum=time_min[0], maximum=time_max[0], phase=phase_time_constraint, ) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) # Phase 0 x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) # Phase 1 x_bounds.add(bounds=QAndQDotBounds(biorbd_model[0])) # Phase 2 for bounds in x_bounds: for i in [1, 3, 4, 5]: bounds[i, [0, -1]] = 0 x_bounds[0][2, 0] = 0.0 x_bounds[2][2, [0, -1]] = [0.0, 1.57] # Initial guess x_init = InitialGuessList() x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) x_init.add([0] * (biorbd_model[0].nbQ() + biorbd_model[0].nbQdot())) # Define control path constraint u_bounds = BoundsList() u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_bounds.add([tau_min] * biorbd_model[0].nbGeneralizedTorque(), [tau_max] * biorbd_model[0].nbGeneralizedTorque()) u_init = InitialGuessList() u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) u_init.add([tau_init] * biorbd_model[0].nbGeneralizedTorque()) parameters = ParameterList() if use_parameter: def my_target_function(ocp, value, target_value): return value - target_value def my_parameter_function(biorbd_model, value, extra_value): new_gravity = MX.zeros(3, 1) new_gravity[2] = value + extra_value biorbd_model.setGravity(new_gravity) min_g = -10 max_g = -6 target_g = -8 bound_gravity = Bounds(min_g, max_g, interpolation=InterpolationType.CONSTANT) initial_gravity = InitialGuess((min_g + max_g) / 2) parameter_objective_functions = Objective( my_target_function, weight=10, quadratic=True, custom_type=ObjectiveFcn.Parameter, target_value=target_g) parameters.add( "gravity_z", my_parameter_function, initial_gravity, bound_gravity, size=1, penalty_list=parameter_objective_functions, extra_value=1, ) # ------------- # return OptimalControlProgram( biorbd_model[:n_phases], dynamics, ns, final_time[:n_phases], x_init, u_init, x_bounds, u_bounds, objective_functions, constraints, ode_solver=ode_solver, parameters=parameters, )
def prepare_ocp(model_path, phase_time, number_shooting_points, min_bound, ode_solver=OdeSolver.RK): # --- Options --- # # Model path biorbd_model = biorbd.Model(model_path) torque_min, torque_max, torque_init = -500, 500, 0 activation_min, activation_max, activation_init = 0, 1, 0.5 tau_mapping = BidirectionalMapping(Mapping([-1, -1, -1, 0]), Mapping([3])) # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_PREDICTED_COM_HEIGHT, weight=-1) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.MUSCLE_EXCITATIONS_AND_TORQUE_DRIVEN_WITH_CONTACT) # Constraints constraints = ConstraintList() constraints.add( ConstraintFcn.CONTACT_FORCE, min_bound=min_bound, max_bound=np.inf, node=Node.ALL, contact_force_idx=1, ) constraints.add( ConstraintFcn.CONTACT_FORCE, min_bound=min_bound, max_bound=np.inf, node=Node.ALL, contact_force_idx=2, ) # Path constraint nb_q = biorbd_model.nbQ() nb_qdot = nb_q nb_mus = biorbd_model.nbMuscleTotal() pose_at_first_node = [0, 0, -0.75, 0.75] # Initialize x_bounds x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0].concatenate( Bounds([activation_min] * nb_mus, [activation_max] * nb_mus)) x_bounds[0][:, 0] = pose_at_first_node + [0] * nb_qdot + [0.5] * nb_mus # Initial guess x_init = InitialGuessList() x_init.add(pose_at_first_node + [0] * nb_qdot + [0.5] * nb_mus) # Define control path constraint u_bounds = BoundsList() u_bounds.add( [torque_min] * tau_mapping.reduce.len + [activation_min] * biorbd_model.nbMuscleTotal(), [torque_max] * tau_mapping.reduce.len + [activation_max] * biorbd_model.nbMuscleTotal(), ) u_init = InitialGuessList() u_init.add([torque_init] * tau_mapping.reduce.len + [activation_init] * biorbd_model.nbMuscleTotal()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, number_shooting_points, phase_time, x_init, u_init, x_bounds, u_bounds, objective_functions=objective_functions, constraints=constraints, tau_mapping=tau_mapping, ode_solver=ode_solver, )
def prepare_ocp(biorbd_model_path: str, final_time: float, n_shooting: int, use_sx: bool = True) -> OptimalControlProgram: """ The initialization of an ocp Parameters ---------- biorbd_model_path: str The path to the biorbd model final_time: float The time in second required to perform the task n_shooting: int The number of shooting points to define int the direct multiple shooting program use_sx: bool If the ocp should be built with SX. Please note that ACADOS requires SX Returns ------- The OptimalControlProgram ready to be solved """ biorbd_model = biorbd.Model(biorbd_model_path) n_q = biorbd_model.nbQ() n_qdot = biorbd_model.nbQdot() data_to_track = np.zeros((n_shooting + 1, n_q + n_qdot)) data_to_track[:, 1] = 3.14 # Add objective functions objective_functions = ObjectiveList() objective_functions.add( ObjectiveFcn.Lagrange.MINIMIZE_TORQUE, weight=100.0, ) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, weight=1.0) objective_functions.add( ObjectiveFcn.Mayer.MINIMIZE_STATE, weight=50000.0, target=data_to_track[-1:, :].T, node=Node.END, ) # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.TORQUE_DRIVEN) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) x_bounds[0][:, 0] = 0 # Initial guess x_init = InitialGuessList() x_init.add([0] * (n_q + n_qdot)) # Define control path constraint n_tau = biorbd_model.nbGeneralizedTorque() torque_min, torque_max, torque_init = -100, 100, 0 u_bounds = BoundsList() u_bounds.add([torque_min] * n_tau, [torque_max] * n_tau) u_bounds[0][n_tau - 1, :] = 0 u_init = InitialGuessList() u_init.add([torque_init] * n_tau) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, use_sx=use_sx, )
def prepare_ocp( biorbd_model: biorbd.Model, final_time: float, n_shooting: int, markers_ref: np.ndarray, excitations_ref: np.ndarray, q_ref: np.ndarray, use_residual_torque: bool, kin_data_to_track: str = "markers", ode_solver: OdeSolver = OdeSolver.COLLOCATION(), ) -> OptimalControlProgram: """ Prepare the ocp to solve Parameters ---------- biorbd_model: biorbd.Model The loaded biorbd model final_time: float The time at final node n_shooting: int The number of shooting points markers_ref: np.ndarray The marker to track if 'markers' is chosen in kin_data_to_track excitations_ref: np.ndarray The muscle excitation (EMG) to track q_ref: np.ndarray The state to track if 'q' is chosen in kin_data_to_track kin_data_to_track: str The type of kin data to track ('markers' or 'q') use_residual_torque: bool If residual torque are present or not in the dynamics ode_solver: OdeSolver The ode solver to use Returns ------- The OptimalControlProgram ready to solve """ # Add objective functions objective_functions = ObjectiveList() objective_functions.add(ObjectiveFcn.Lagrange.TRACK_CONTROL, key="muscles", target=excitations_ref) if use_residual_torque: objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau") if kin_data_to_track == "markers": objective_functions.add(ObjectiveFcn.Lagrange.TRACK_MARKERS, node=Node.ALL, weight=100, target=markers_ref) elif kin_data_to_track == "q": objective_functions.add( ObjectiveFcn.Lagrange.TRACK_STATE, key="q", weight=100, node=Node.ALL, target=q_ref, index=range(biorbd_model.nbQ()), ) else: raise RuntimeError("Wrong choice of kin_data_to_track") # Dynamics dynamics = DynamicsList() dynamics.add(DynamicsFcn.MUSCLE_DRIVEN, with_excitations=True, with_residual_torque=use_residual_torque) # Path constraint x_bounds = BoundsList() x_bounds.add(bounds=QAndQDotBounds(biorbd_model)) # Due to unpredictable movement of the forward dynamics that generated the movement, the bound must be larger x_bounds[0].min[[0, 1], :] = -2 * np.pi x_bounds[0].max[[0, 1], :] = 2 * np.pi # Add muscle to the bounds activation_min, activation_max, activation_init = 0, 1, 0.5 x_bounds[0].concatenate( Bounds([activation_min] * biorbd_model.nbMuscles(), [activation_max] * biorbd_model.nbMuscles())) x_bounds[0][(biorbd_model.nbQ() + biorbd_model.nbQdot()):, 0] = excitations_ref[:, 0] # Initial guess x_init = InitialGuessList() x_init.add([0] * (biorbd_model.nbQ() + biorbd_model.nbQdot()) + [0] * biorbd_model.nbMuscles()) # Define control path constraint excitation_min, excitation_max, excitation_init = 0, 1, 0.5 u_bounds = BoundsList() u_init = InitialGuessList() if use_residual_torque: tau_min, tau_max, tau_init = -100, 100, 0 u_bounds.add( [tau_min] * biorbd_model.nbGeneralizedTorque() + [excitation_min] * biorbd_model.nbMuscles(), [tau_max] * biorbd_model.nbGeneralizedTorque() + [excitation_max] * biorbd_model.nbMuscles(), ) u_init.add([tau_init] * biorbd_model.nbGeneralizedTorque() + [excitation_init] * biorbd_model.nbMuscles()) else: u_bounds.add([excitation_min] * biorbd_model.nbMuscles(), [excitation_max] * biorbd_model.nbMuscles()) u_init.add([excitation_init] * biorbd_model.nbMuscles()) # ------------- # return OptimalControlProgram( biorbd_model, dynamics, n_shooting, final_time, x_init, u_init, x_bounds, u_bounds, objective_functions, ode_solver=ode_solver, )