10 #include "crocoddyl/core/utils/exception.hpp" 11 #include "crocoddyl/core/solvers/box-fddp.hpp" 15 SolverBoxFDDP::SolverBoxFDDP(boost::shared_ptr<ShootingProblem> problem)
16 : SolverFDDP(problem), qp_(problem->get_runningModels()[0]->get_nu(), 100, 0.1, 1e-5, 0.) {
19 const std::size_t& n_alphas = 10;
21 for (std::size_t n = 0; n < n_alphas; ++n) {
22 alphas_[n] = 1. / pow(2., static_cast<double>(n));
31 SolverBoxFDDP::~SolverBoxFDDP() {}
36 const std::size_t& T =
problem_->get_T();
38 const std::size_t& nu =
problem_->get_nu_max();
39 for (std::size_t t = 0; t < T; ++t) {
40 Quu_inv_[t] = Eigen::MatrixXd::Zero(nu, nu);
47 if (
problem_->get_runningModels()[t]->get_nu() > 0) {
54 du_lb_ =
problem_->get_runningModels()[t]->get_u_lb() -
us_[t];
55 du_ub_ =
problem_->get_runningModels()[t]->get_u_ub() -
us_[t];
60 Quu_inv_[t].setZero();
61 for (std::size_t i = 0; i < boxqp_sol.
free_idx.size(); ++i) {
62 for (std::size_t j = 0; j < boxqp_sol.
free_idx.size(); ++j) {
66 K_[t].noalias() = Quu_inv_[t] *
Qxu_[t].transpose();
67 k_[t].noalias() = -boxqp_sol.
x;
71 for (std::size_t i = 0; i < boxqp_sol.
clamped_idx.size(); ++i) {
78 if (steplength > 1. || steplength < 0.) {
79 throw_pretty(
"Invalid argument: " 80 <<
"invalid step length, value is between 0. to 1.");
84 const std::size_t& T =
problem_->get_T();
85 const std::vector<boost::shared_ptr<ActionModelAbstract> >& models =
problem_->get_runningModels();
86 const std::vector<boost::shared_ptr<ActionDataAbstract> >& datas =
problem_->get_runningDatas();
88 for (std::size_t t = 0; t < T; ++t) {
89 const boost::shared_ptr<ActionModelAbstract>& m = models[t];
90 const boost::shared_ptr<ActionDataAbstract>& d = datas[t];
93 m->get_state()->diff(
xs_[t],
xs_try_[t], dx_[t]);
94 if (m->get_nu() != 0) {
95 us_try_[t].noalias() =
us_[t] -
k_[t] * steplength -
K_[t] * dx_[t];
96 if (m->get_has_control_limits()) {
97 us_try_[t] =
us_try_[t].cwiseMax(m->get_u_lb()).cwiseMin(m->get_u_ub());
107 throw_pretty(
"forward_error");
109 if (raiseIfNaN(
xnext_.lpNorm<Eigen::Infinity>())) {
110 throw_pretty(
"forward_error");
114 const boost::shared_ptr<ActionModelAbstract>& m =
problem_->get_terminalModel();
115 const boost::shared_ptr<ActionDataAbstract>& d =
problem_->get_terminalData();
121 throw_pretty(
"forward_error");
124 for (std::size_t t = 0; t < T; ++t) {
125 const boost::shared_ptr<ActionModelAbstract>& m = models[t];
126 const boost::shared_ptr<ActionDataAbstract>& d = datas[t];
129 m->get_state()->diff(
xs_[t],
xs_try_[t], dx_[t]);
130 if (m->get_nu() != 0) {
131 us_try_[t].noalias() =
us_[t] -
k_[t] * steplength -
K_[t] * dx_[t];
132 if (m->get_has_control_limits()) {
133 us_try_[t] =
us_try_[t].cwiseMax(m->get_u_lb()).cwiseMin(m->get_u_ub());
143 throw_pretty(
"forward_error");
145 if (raiseIfNaN(
xnext_.lpNorm<Eigen::Infinity>())) {
146 throw_pretty(
"forward_error");
150 const boost::shared_ptr<ActionModelAbstract>& m =
problem_->get_terminalModel();
151 const boost::shared_ptr<ActionDataAbstract>& d =
problem_->get_terminalData();
152 m->get_state()->integrate(
xnext_,
fs_.back() * (steplength - 1),
xs_try_.back());
157 throw_pretty(
"forward_error");
162 const std::vector<Eigen::MatrixXd>& SolverBoxFDDP::get_Quu_inv()
const {
return Quu_inv_; }
virtual void allocateData()
Allocate all the internal data needed for the solver.
std::vector< Eigen::VectorXd > fs_
Gaps/defects between shooting nodes.
bool is_feasible_
Label that indicates is the iteration is feasible.
virtual void allocateData()
Allocate all the internal data needed for the solver.
std::vector< Eigen::VectorXd > xs_try_
State trajectory computed by line-search procedure.
const BoxQPSolution & solve(const Eigen::MatrixXd &H, const Eigen::VectorXd &q, const Eigen::VectorXd &lb, const Eigen::VectorXd &ub, const Eigen::VectorXd &xinit)
Compute the solution of bound-constrained QP based on Newton projection.
std::vector< Eigen::VectorXd > us_
Control trajectory.
virtual void allocateData()
Allocate all the internal data needed for the solver.
std::vector< Eigen::VectorXd > k_
Feed-forward terms.
Eigen::MatrixXd Hff_inv
Inverse of the free space Hessian.
Eigen::VectorXd xnext_
Next state.
boost::shared_ptr< ShootingProblem > problem_
optimal control problem
Eigen::VectorXd x
Decision vector.
std::vector< Eigen::MatrixXd > K_
Feedback gains.
std::vector< Eigen::VectorXd > xs_
State trajectory.
std::vector< double > alphas_
Set of step lengths using by the line-search procedure.
double th_stop_
Tolerance for stopping the algorithm.
virtual void computeGains(const std::size_t &t)
Compute the feedforward and feedback terms using a Cholesky decomposition.
std::vector< size_t > clamped_idx
Clamped space indexes.
std::vector< Eigen::MatrixXd > Qxu_
Hessian of the Hamiltonian.
virtual void forwardPass(const double &steplength)
Run the forward pass or rollout.
std::vector< size_t > free_idx
Free space indexes.
std::vector< Eigen::VectorXd > us_try_
Control trajectory computed by line-search procedure.
std::vector< Eigen::MatrixXd > Quu_
Hessian of the Hamiltonian.
virtual void computeGains(const std::size_t &t)
Compute the feedforward and feedback terms using a Cholesky decomposition.
std::vector< Eigen::VectorXd > Qu_
Gradient of the Hamiltonian.
double cost_try_
Total cost computed by line-search procedure.