track_vector_orientation.py

"""
This example is a trivial example where a stick must keep its axis aligned with the one
side of a box during the whole duration of the movement.
"""

import platform

from bioptim import (
    BiorbdModel,
    Node,
    OptimalControlProgram,
    DynamicsList,
    DynamicsFcn,
    ObjectiveList,
    ObjectiveFcn,
    BoundsList,
    InitialGuessList,
    OdeSolver,
    OdeSolverBase,
    Solver,
    PhaseDynamics,
)


def prepare_ocp(
    biorbd_model_path: str,
    final_time: float,
    n_shooting: int,
    ode_solver: OdeSolverBase = OdeSolver.RK4(),
    phase_dynamics: PhaseDynamics = PhaseDynamics.SHARED_DURING_THE_PHASE,
    expand_dynamics: bool = True,
) -> OptimalControlProgram:
    """
    Prepare the ocp

    Parameters
    ----------
    biorbd_model_path: str
        The path to the model
    final_time: float
        The time of the final node
    n_shooting: int
        The number of shooting points
    ode_solver:
        The ode solver to use
    phase_dynamics: PhaseDynamics
        If the dynamics equation within a phase is unique or changes at each node.
        PhaseDynamics.SHARED_DURING_THE_PHASE is much faster, but lacks the capability to have changing dynamics within
        a phase. A good example of when PhaseDynamics.ONE_PER_NODE should be used is when different external forces
        are applied at each node
    expand_dynamics: bool
        If the dynamics function should be expanded. Please note, this will solve the problem faster, but will slow down
        the declaration of the OCP, so it is a trade-off. Also depending on the solver, it may or may not work
        (for instance IRK is not compatible with expanded dynamics)

    Returns
    -------
    The OptimalControlProgram ready to be solved
    """

    bio_model = BiorbdModel(biorbd_model_path)

    # Add objective functions
    objective_functions = ObjectiveList()
    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1)
    objective_functions.add(
        ObjectiveFcn.Mayer.TRACK_VECTOR_ORIENTATIONS_FROM_MARKERS,
        node=Node.ALL,
        weight=100,
        vector_0_marker_0="m0",
        vector_0_marker_1="m3",
        vector_1_marker_0="origin",
        vector_1_marker_1="m6",
    )

    # Dynamics
    dynamics = DynamicsList()
    dynamics.add(DynamicsFcn.TORQUE_DRIVEN, expand_dynamics=expand_dynamics, phase_dynamics=phase_dynamics)

    # Path constraint
    x_bounds = BoundsList()
    x_bounds["q"] = bio_model.bounds_from_ranges("q")
    x_bounds["q"][2, [0, -1]] = [-1.57, 1.57]
    x_bounds["qdot"] = bio_model.bounds_from_ranges("qdot")

    # Define control path constraint
    tau_min, tau_max, tau_init = -100, 100, 2
    u_bounds = BoundsList()
    u_bounds["tau"] = [tau_min] * bio_model.nb_tau, [tau_max] * bio_model.nb_tau

    u_init = InitialGuessList()
    u_init["tau"] = [tau_init] * bio_model.nb_tau

    # ------------- #

    return OptimalControlProgram(
        bio_model,
        dynamics,
        n_shooting,
        final_time,
        x_bounds=x_bounds,
        u_bounds=u_bounds,
        u_init=u_init,
        objective_functions=objective_functions,
        ode_solver=ode_solver,
    )


def main():
    """
    Prepares, solves and animates the program
    """

    ocp = prepare_ocp(
        biorbd_model_path="models/cube_and_line.bioMod",
        n_shooting=30,
        final_time=1,
    )
    ocp.add_plot_penalty()

    # --- Solve the program --- #
    sol = ocp.solve(Solver.IPOPT(show_online_optim=platform.system() == "Linux"))

    # --- Show results --- #
    sol.animate()


if __name__ == "__main__":
    main()