10 #include "crocoddyl/core/utils/exception.hpp" 11 #include "crocoddyl/core/solvers/box-ddp.hpp" 15 SolverBoxDDP::SolverBoxDDP(boost::shared_ptr<ShootingProblem> problem)
16 : SolverDDP(problem), qp_(problem->get_runningModels()[0]->get_nu(), 100, 0.1, 1e-5, 0.) {
19 const std::size_t& n_alphas = 10;
20 alphas_.resize(n_alphas);
21 for (std::size_t n = 0; n < n_alphas; ++n) {
22 alphas_[n] = 1. / pow(2., static_cast<double>(n));
31 SolverBoxDDP::~SolverBoxDDP() {}
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 const std::size_t& nu =
problem_->get_runningModels()[t]->get_nu();
55 du_lb_.head(nu) =
problem_->get_runningModels()[t]->get_u_lb() -
us_[t].head(nu);
56 du_ub_.head(nu) =
problem_->get_runningModels()[t]->get_u_ub() -
us_[t].head(nu);
59 qp_.solve(Quu_[t].topLeftCorner(nu, nu), Qu_[t].head(nu), du_lb_.head(nu), du_ub_.head(nu), k_[t].head(nu));
62 Quu_inv_[t].topLeftCorner(nu, nu).setZero();
63 for (std::size_t i = 0; i < boxqp_sol.
free_idx.size(); ++i) {
64 for (std::size_t j = 0; j < boxqp_sol.
free_idx.size(); ++j) {
68 K_[t].topRows(nu).noalias() = Quu_inv_[t].topLeftCorner(nu, nu) * Qxu_[t].leftCols(nu).transpose();
69 k_[t].topRows(nu).noalias() = -boxqp_sol.
x;
73 for (std::size_t i = 0; i < boxqp_sol.
clamped_idx.size(); ++i) {
80 if (steplength > 1. || steplength < 0.) {
81 throw_pretty(
"Invalid argument: " 82 <<
"invalid step length, value is between 0. to 1.");
86 const std::size_t& T =
problem_->get_T();
87 const std::vector<boost::shared_ptr<ActionModelAbstract> >& models =
problem_->get_runningModels();
88 const std::vector<boost::shared_ptr<ActionDataAbstract> >& datas =
problem_->get_runningDatas();
89 for (std::size_t t = 0; t < T; ++t) {
90 const boost::shared_ptr<ActionModelAbstract>& m = models[t];
91 const boost::shared_ptr<ActionDataAbstract>& d = datas[t];
92 const std::size_t& nu = m->get_nu();
95 m->get_state()->diff(
xs_[t], xs_try_[t], dx_[t]);
97 us_try_[t].head(nu).noalias() =
us_[t].head(nu) - k_[t].head(nu) * steplength - K_[t].topRows(nu) * dx_[t];
98 if (m->get_has_control_limits()) {
99 us_try_[t].head(nu) = us_try_[t].head(nu).cwiseMax(m->get_u_lb()).cwiseMin(m->get_u_ub());
101 m->calc(d, xs_try_[t], us_try_[t].head(nu));
103 m->calc(d, xs_try_[t]);
106 cost_try_ += d->cost;
108 if (raiseIfNaN(cost_try_)) {
109 throw_pretty(
"forward_error");
111 if (raiseIfNaN(xnext_.lpNorm<Eigen::Infinity>())) {
112 throw_pretty(
"forward_error");
116 const boost::shared_ptr<ActionModelAbstract>& m =
problem_->get_terminalModel();
117 const boost::shared_ptr<ActionDataAbstract>& d =
problem_->get_terminalData();
119 xs_try_.back() = xnext_;
121 m->get_state()->integrate(xnext_, fs_.back() * (steplength - 1), xs_try_.back());
123 m->calc(d, xs_try_.back());
124 cost_try_ += d->cost;
126 if (raiseIfNaN(cost_try_)) {
127 throw_pretty(
"forward_error");
131 const std::vector<Eigen::MatrixXd>& SolverBoxDDP::get_Quu_inv()
const {
return Quu_inv_; }
virtual void allocateData()
Allocate all the internal data needed for the solver.
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 > us_
Control trajectory.
Eigen::MatrixXd Hff_inv
Inverse of the free space Hessian.
boost::shared_ptr< ShootingProblem > problem_
optimal control problem
Eigen::VectorXd x
Decision vector.
std::vector< Eigen::VectorXd > xs_
State trajectory.
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.
virtual void forwardPass(const double &steplength)
Run the forward pass or rollout.
std::vector< size_t > free_idx
Free space indexes.
virtual void computeGains(const std::size_t &t)
Compute the feedforward and feedback terms using a Cholesky decomposition.