unconstrained_time_indexed_problem.cpp

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#include <exotica_core/problems/unconstrained_time_indexed_problem.h>
#include <exotica_core/setup.h>
 
REGISTER_PROBLEM_TYPE("UnconstrainedTimeIndexedProblem", exotica::UnconstrainedTimeIndexedProblem)
 
namespace exotica
{
void UnconstrainedTimeIndexedProblem::Instantiate(const UnconstrainedTimeIndexedProblemInitializer& init)
{
    this->parameters_ = init;
 
    N = scene_->GetKinematicTree().GetNumControlledJoints();
 
    w_scale_ = this->parameters_.Wrate;
    W = Eigen::MatrixXd::Identity(N, N) * w_scale_;
    if (this->parameters_.W.rows() > 0)
    {
        if (this->parameters_.W.rows() == N)
        {
            W.diagonal() = this->parameters_.W * w_scale_;
        }
        else
        {
            ThrowNamed("W dimension mismatch! Expected " << N << ", got " << this->parameters_.W.rows());
        }
    }
 
    cost.Initialize(this->parameters_.Cost, shared_from_this(), cost_Phi);
 
    T_ = this->parameters_.T;
    tau_ = this->parameters_.tau;
    ApplyStartState(false);
    ReinitializeVariables();
}
 
void UnconstrainedTimeIndexedProblem::ReinitializeVariables()
{
    if (debug_) HIGHLIGHT_NAMED("UnconstrainedTimeIndexedProblem", "Initialize problem with T=" << T_);
 
    num_tasks = tasks_.size();
    length_Phi = 0;
    length_jacobian = 0;
    TaskSpaceVector y_ref_;
    for (int i = 0; i < num_tasks; ++i)
    {
        AppendVector(y_ref_.map, tasks_[i]->GetLieGroupIndices());
        length_Phi += tasks_[i]->length;
        length_jacobian += tasks_[i]->length_jacobian;
    }
 
    y_ref_.SetZero(length_Phi);
    Phi.assign(T_, y_ref_);
 
    x.assign(T_, Eigen::VectorXd::Zero(N));
    xdiff.assign(T_, Eigen::VectorXd::Zero(N));
    if (flags_ & KIN_J) jacobian.assign(T_, Eigen::MatrixXd(length_jacobian, N));
    if (flags_ & KIN_H)
    {
        Hessian Htmp;
        Htmp.setConstant(length_jacobian, Eigen::MatrixXd::Zero(N, N));
        hessian.assign(T_, Htmp);
    }
 
    // Set initial trajectory with current state
    initial_trajectory_.resize(T_, scene_->GetControlledState());
 
    cost.ReinitializeVariables(T_, shared_from_this(), cost_Phi);
 
    // Updates related to tau
    ct = 1.0 / tau_ / T_;
    xdiff_max_ = q_dot_max_ * tau_;
 
    PreUpdate();
}
 
void UnconstrainedTimeIndexedProblem::PreUpdate()
{
    PlanningProblem::PreUpdate();
    for (int i = 0; i < tasks_.size(); ++i) tasks_[i]->is_used = false;
    cost.UpdateS();
 
    // Create a new set of kinematic solutions with the size of the trajectory
    // based on the latest KinematicResponse in order to reflect model state
    // updates etc.
    kinematic_solutions_.clear();
    kinematic_solutions_.resize(T_);
    for (int i = 0; i < T_; ++i) kinematic_solutions_[i] = std::make_shared<KinematicResponse>(*scene_->GetKinematicTree().GetKinematicResponse());
}
 
void UnconstrainedTimeIndexedProblem::Update(Eigen::VectorXdRefConst x_in, int t)
{
    ValidateTimeIndex(t);
 
    x[t] = x_in;
 
    // Set the corresponding KinematicResponse for KinematicTree in order to
    // have Kinematics elements updated based in x_in.
    scene_->GetKinematicTree().SetKinematicResponse(kinematic_solutions_[t]);
 
    // Pass the corresponding number of relevant task kinematics to the TaskMaps
    // via the PlanningProblem::UpdateMultipleTaskKinematics method. For now we
    // support passing _two_ timesteps - this can be easily changed later on.
    std::vector<std::shared_ptr<KinematicResponse>> kinematics_solutions{kinematic_solutions_[t]};
 
    // If the current timestep is 0, pass the 0th timestep's response twice.
    // Otherwise pass the (t-1)th response.
    kinematics_solutions.emplace_back((t == 0) ? kinematic_solutions_[t] : kinematic_solutions_[t - 1]);
 
    // Actually update the tasks' kinematics mappings.
    PlanningProblem::UpdateMultipleTaskKinematics(kinematics_solutions);
 
    scene_->Update(x_in, t_start + static_cast<double>(t) * tau_);
 
    Phi[t].SetZero(length_Phi);
    if (flags_ & KIN_J) jacobian[t].setZero();
    if (flags_ & KIN_H)
        for (int i = 0; i < length_jacobian; ++i) hessian[t](i).setZero();
    for (int i = 0; i < num_tasks; ++i)
    {
        // Only update TaskMap if rho is not 0
        if (tasks_[i]->is_used)
        {
            if (flags_ & KIN_H)
            {
                tasks_[i]->Update(x[t],
                                  Phi[t].data.segment(tasks_[i]->start, tasks_[i]->length),
                                  jacobian[t].middleRows(tasks_[i]->start_jacobian, tasks_[i]->length_jacobian),
                                  hessian[t].segment(tasks_[i]->start_jacobian, tasks_[i]->length_jacobian));
            }
            else if (flags_ & KIN_J)
            {
                tasks_[i]->Update(x[t],
                                  Phi[t].data.segment(tasks_[i]->start, tasks_[i]->length),
                                  Eigen::MatrixXdRef(jacobian[t].middleRows(tasks_[i]->start_jacobian, tasks_[i]->length_jacobian))  // Adding MatrixXdRef(...) is a work-around for issue #737 when using Eigen 3.3.9
                );
            }
            else
            {
                tasks_[i]->Update(x[t], Phi[t].data.segment(tasks_[i]->start, tasks_[i]->length));
            }
        }
    }
    if (flags_ & KIN_H)
    {
        cost.Update(Phi[t], jacobian[t], hessian[t], t);
    }
    else if (flags_ & KIN_J)
    {
        cost.Update(Phi[t], jacobian[t], t);
    }
    else
    {
        cost.Update(Phi[t], t);
    }
 
    if (t > 0) xdiff[t] = x[t] - x[t - 1];
 
    ++number_of_problem_updates_;
}
 
bool UnconstrainedTimeIndexedProblem::IsValid()
{
    return true;
}
}  // namespace exotica