track_segment_on_rt.py

"""
This example is a trivial example where a stick must keep its coordinate system of axes aligned with the one
from a box during the whole duration of the movement. The initial and final position of the box are dictated,
the rest is fully optimized. It is designed to show how one can use the tracking RT function to track
any RT (for instance Inertial Measurement Unit [IMU]) with a body segment
"""

import platform

from bioptim import (
    BiorbdModel,
    Node,
    OptimalControlProgram,
    DynamicsList,
    DynamicsFcn,
    ObjectiveList,
    ObjectiveFcn,
    ConstraintList,
    ConstraintFcn,
    BoundsList,
    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=100)

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

    # Constraints
    constraints = ConstraintList()
    constraints.add(ConstraintFcn.TRACK_SEGMENT_WITH_CUSTOM_RT, node=Node.ALL, segment="seg_rt", rt=0)

    # 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")
    x_bounds["qdot"][:, [0, -1]] = 0

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

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

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

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

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


if __name__ == "__main__":
    main()