static_arm.py

"""
This is a basic example on how to use biorbd model driven by muscle to perform an optimal reaching task.
The arms must reach a marker placed upward in front while minimizing the muscles activity

Please note that using show_meshes=True in the animator may be long due to the creation of a huge CasADi graph of the
mesh points.
"""

import platform

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


def prepare_ocp(
    biorbd_model_path: str,
    final_time: float,
    n_shooting: int,
    weight: float,
    ode_solver: OdeSolverBase = OdeSolver.RK4(),
    phase_dynamics: PhaseDynamics = PhaseDynamics.SHARED_DURING_THE_PHASE,
    expand_dynamics: bool = True,
    control_type: ControlType = ControlType.CONSTANT,
    n_threads: int = 8,
) -> OptimalControlProgram:
    """
    Prepare the ocp

    Parameters
    ----------
    biorbd_model_path: str
        The path to the bioMod
    final_time: float
        The time at the final node
    n_shooting: int
        The number of shooting points
    weight: float
        The weight applied to the SUPERIMPOSE_MARKERS final objective function. The bigger this number is, the greater
        the model will try to reach the marker. This is in relation with the other objective functions
    ode_solver: OdeSolverBase
        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)
    control_type: ControlType
        The type of control to use (CONSTANT, LINEAR_CONTROL, POLYNOMIAL_CONTROL)
    n_threads: int
        The number of threads to use in casadi (default: number of cores of your machine)

    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")
    objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="muscles")
    objective_functions.add(
        ObjectiveFcn.Mayer.SUPERIMPOSE_MARKERS, first_marker="target", second_marker="COM_hand", weight=weight
    )

    # Dynamics
    dynamics = DynamicsList()
    dynamics.add(
        DynamicsFcn.MUSCLE_DRIVEN,
        with_residual_torque=True,
        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"][:, 0] = (0.07, 1.4)
    x_bounds["qdot"] = bio_model.bounds_from_ranges("qdot")
    x_bounds["qdot"][:, 0] = 0

    # Initial guess
    x_init = InitialGuessList()
    x_init["q"] = [1.57] * bio_model.nb_q

    # Define control path constraint
    muscle_min, muscle_max, muscle_init = 0.0, 1.0, 0.5
    tau_min, tau_max, tau_init = -1.0, 1.0, 0.0
    u_bounds = BoundsList()
    u_bounds["tau"] = [tau_min] * bio_model.nb_tau, [tau_max] * bio_model.nb_tau
    u_bounds["muscles"] = [muscle_min] * bio_model.nb_muscles, [muscle_max] * bio_model.nb_muscles

    u_init = InitialGuessList()
    u_init["muscles"] = [muscle_init] * bio_model.nb_muscles
    # ------------- #

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


def main():
    """
    Prepare and solve and animate a reaching task ocp
    """

    ocp = prepare_ocp(biorbd_model_path="models/arm26.bioMod", final_time=0.5, n_shooting=50, weight=1000)

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

    # --- Show results --- #
    sol.animate(show_meshes=True)


if __name__ == "__main__":
    main()