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() {}
33 void SolverBoxDDP::allocateData() {
34 SolverDDP::allocateData();
36 std::size_t nu_max = 0;
37 const std::size_t& T = problem_->get_T();
39 for (std::size_t t = 0; t < T; ++t) {
40 const boost::shared_ptr<ActionModelAbstract>& model = problem_->get_runningModels()[t];
41 const std::size_t& nu = model->get_nu();
44 if (nu > nu_max) nu_max = nu;
46 Quu_inv_[t] = Eigen::MatrixXd::Zero(nu, nu);
49 du_lb_.resize(nu_max);
50 du_ub_.resize(nu_max);
53 void SolverBoxDDP::computeGains(
const std::size_t& t) {
54 if (problem_->get_runningModels()[t]->get_nu() > 0) {
55 if (!problem_->get_runningModels()[t]->get_has_control_limits() || !is_feasible_) {
57 SolverDDP::computeGains(t);
61 du_lb_ = problem_->get_runningModels()[t]->get_u_lb() - us_[t];
62 du_ub_ = problem_->get_runningModels()[t]->get_u_ub() - us_[t];
64 const BoxQPSolution& boxqp_sol = qp_.solve(Quu_[t], Qu_[t], du_lb_, du_ub_, k_[t]);
67 Quu_inv_[t].setZero();
68 for (std::size_t i = 0; i < boxqp_sol.free_idx.size(); ++i) {
69 for (std::size_t j = 0; j < boxqp_sol.free_idx.size(); ++j) {
70 Quu_inv_[t](boxqp_sol.free_idx[i], boxqp_sol.free_idx[j]) = boxqp_sol.Hff_inv(i, j);
73 K_[t].noalias() = Quu_inv_[t] * Qxu_[t].transpose();
74 k_[t].noalias() = -boxqp_sol.x;
78 for (std::size_t i = 0; i < boxqp_sol.clamped_idx.size(); ++i) {
79 Qu_[t](boxqp_sol.clamped_idx[i]) = 0.;
84 void SolverBoxDDP::forwardPass(
const double& steplength) {
85 if (steplength > 1. || steplength < 0.) {
86 throw_pretty(
"Invalid argument: " 87 <<
"invalid step length, value is between 0. to 1.");
90 xnext_ = problem_->get_x0();
91 const std::size_t& T = problem_->get_T();
92 for (std::size_t t = 0; t < T; ++t) {
93 const boost::shared_ptr<ActionModelAbstract>& m = problem_->get_runningModels()[t];
94 const boost::shared_ptr<ActionDataAbstract>& d = problem_->get_runningDatas()[t];
96 m->get_state()->diff(xs_[t], xs_try_[t], dx_[t]);
97 us_try_[t].noalias() = us_[t] - k_[t] * steplength - K_[t] * dx_[t];
98 if (m->get_has_control_limits()) {
99 us_try_[t] = us_try_[t].cwiseMax(m->get_u_lb()).cwiseMin(m->get_u_ub());
101 m->calc(d, xs_try_[t], us_try_[t]);
103 cost_try_ += d->cost;
105 if (raiseIfNaN(cost_try_)) {
106 throw_pretty(
"forward_error");
108 if (raiseIfNaN(xnext_.lpNorm<Eigen::Infinity>())) {
109 throw_pretty(
"forward_error");
113 const boost::shared_ptr<ActionModelAbstract>& m = problem_->get_terminalModel();
114 const boost::shared_ptr<ActionDataAbstract>& d = problem_->get_terminalData();
116 if ((is_feasible_) || (steplength == 1)) {
117 xs_try_.back() = xnext_;
119 m->get_state()->integrate(xnext_, fs_.back() * (steplength - 1), xs_try_.back());
121 m->calc(d, xs_try_.back());
122 cost_try_ += d->cost;
124 if (raiseIfNaN(cost_try_)) {
125 throw_pretty(
"forward_error");
129 const std::vector<Eigen::MatrixXd>& SolverBoxDDP::get_Quu_inv()
const {
return Quu_inv_; }