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/////////////////////////////////////////////////////////////////////////////// |
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// BSD 3-Clause License |
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// |
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// Copyright (C) 2018-2019, LAAS-CNRS |
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// Copyright note valid unless otherwise stated in individual files. |
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// All rights reserved. |
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/////////////////////////////////////////////////////////////////////////////// |
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#include <quadruped-walkgen/quadruped_nl.hpp> |
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#include "crocoddyl/core/actions/unicycle.hpp" |
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#include "crocoddyl/core/solvers/ddp.hpp" |
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#include "crocoddyl/core/utils/callbacks.hpp" |
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#include "crocoddyl/core/utils/timer.hpp" |
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void updateModel( |
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std::vector< |
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boost::shared_ptr<quadruped_walkgen::ActionModelQuadrupedNonLinear> > |
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running_models_2, |
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boost::shared_ptr<quadruped_walkgen::ActionModelQuadrupedNonLinear> |
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terminal_model_2, |
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Eigen::Matrix<double, 6, 5> gait, Eigen::Matrix<double, 12, 17> xref, |
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Eigen::Matrix<double, 6, 13> fsteps, int N) { |
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// Iterate over all the phases of the gait matrix |
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// The first column of xref correspond to the current state = x0 |
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// Tmp is needed to use .data(), transformation of a column into a vector |
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Eigen::Array<double, 1, 12> tmp = Eigen::Array<double, 1, 12>::Zero(); |
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int max_index = int(gait.block(0, 0, 6, 1).array().min(1.).matrix().sum()); |
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int k_cum = 0; |
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for (int j = 0; j < max_index; j++) { |
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for (int k = k_cum; k < k_cum + int(gait(j, 0)); ++k) { |
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if (k < int(N)) { |
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// Update model : |
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tmp = fsteps.block(j, 1, 1, 12).array(); |
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running_models_2[k]->update_model( |
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Eigen::Map<Eigen::Matrix<double, 3, 4> >(tmp.data(), 3, 4), |
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Eigen::Map<Eigen::Matrix<double, 12, 1> >( |
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xref.block(0, k + 1, 12, 1).data(), 12, 1), |
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Eigen::Map<Eigen::Matrix<double, 4, 1> >( |
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gait.block(j, 1, 1, 4).data(), 4, 1)); |
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} |
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} |
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k_cum += int(gait(j, 0)); |
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} |
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tmp = fsteps.block(max_index - 1, 1, 1, 12).array(); |
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Eigen::Array<double, 1, 4> gait_tmp = Eigen::Array<double, 1, 4>::Zero(); |
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gait_tmp = gait.block(max_index - 1, 1, 1, 4).array(); |
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terminal_model_2->update_model( |
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Eigen::Map<Eigen::Matrix<double, 3, 4> >(tmp.data(), 3, 4), |
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Eigen::Map<Eigen::Matrix<double, 12, 1> >(xref.block(0, 16, 12, 1).data(), |
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12, 1), |
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Eigen::Map<Eigen::Matrix<double, 4, 1> >(gait_tmp.data(), 4, 1)); |
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terminal_model_2->set_force_weights(Eigen::Matrix<double, 12, 1>::Zero()); |
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terminal_model_2->set_friction_weight(0); |
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} |
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int main(int argc, char* argv[]) { |
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// The time of the cycle contol is 0.02s, and last 0.32s --> 16nodes |
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// Control cycle during one gait period |
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unsigned int N = 16; // number of nodes |
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unsigned int T = 1000; // number of trials |
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unsigned int MAXITER = 1; |
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if (argc > 1) { |
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T = atoi(argv[1]); |
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MAXITER = atoi(argv[2]); |
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; |
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} |
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// Creating the initial state vector (size x12) |
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// [x,y,z,Roll,Pitch,Yaw,Vx,Vy,Vz,Wroll,Wpitch,Wyaw] Perturbation of Vx = |
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// 0.2m.s-1 |
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Eigen::Matrix<double, 12, 1> x0; |
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x0 << 0, 0, 0.25, 0.15, 0.1, 0, 0.2, 0, 0, 0, 0, 0; |
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Eigen::Matrix<double, 4, 1> S; |
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S << 1, 0, 0, 1; |
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// Creating the reference state vector (size 12x16) to follow during the |
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// control cycle Nullifying the Vx speed. |
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Eigen::Matrix<double, 12, 1> xref_vector; |
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xref_vector << 0, 0, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0; |
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Eigen::Matrix<double, 12, 17> xref; |
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xref.block(0, 0, 12, 1) = x0; // first vector is the initial state |
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xref.block(0, 1, 12, 16) = xref_vector.replicate<1, 16>(); |
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// Creating the gait matrix : The number at the beginning represents the |
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// number of node spent in that position 1 -> foot in contact with the ground |
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// : 0-> foot in the air |
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Eigen::Matrix<double, 6, 5> gait; |
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gait << 1, 1, 1, 1, 1, 7, 1, 0, 0, 1, 1, 1, 1, 1, 1, 7, 0, 1, 1, 0, 0, 0, 0, |
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0, 0, 0, 0, 0, 0, 0; |
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// Creating the fsteps matrix that represents the position of the feet during |
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// the whole control cycle (0.32s). [nb , x1,y1,z1, x2,y2,z2 ...] in local |
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// frame The number at the beginning represents the number of node spent in |
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// that position Here, the robot starts with 4 feet on the ground at the first |
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// node, then during 7 nodes (0.02s * 7) The leg right front leg and left back |
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// leg are in the air ...etc |
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Eigen::Matrix<double, 6, 13> fsteps; |
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fsteps << 1, 0.19, 0.15, 0.0, 0.19, -0.15, 0.0, -0.19, 0.15, 0.0, -0.19, |
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-0.15, 0.0, 7, 0.19, 0.15, 0.0, 0, 0, 0, 0, 0, 0, -0.19, -0.15, 0.0, 1, |
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0.19, 0.15, 0.0, 0.19, -0.15, 0.0, -0.19, 0.15, 0.0, -0.19, -0.15, 0.0, 7, |
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0, 0, 0, 0.19, -0.15, 0.0, -0.19, 0.15, 0.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; |
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// Creating the Shoting problem that needs |
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// boost::shared_ptr<crocoddyl::ActionModelAbstract> |
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// Cannot use 1 model for the whole control cycle, because each model depends |
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// on the position of the feet And the inertia matrix depends on the reference |
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// state (approximation ) |
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std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > |
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running_models; |
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for (int i = 0; i < int(N); ++i) { // 16 nodes |
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boost::shared_ptr<crocoddyl::ActionModelAbstract> model = |
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boost::make_shared<quadruped_walkgen::ActionModelQuadrupedNonLinear>(); |
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running_models.push_back(model); |
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} |
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boost::shared_ptr<crocoddyl::ActionModelAbstract> terminal_model; |
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terminal_model = |
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boost::make_shared<quadruped_walkgen::ActionModelQuadrupedNonLinear>(); |
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// Update each model and set to 0 the weight ont the command for the terminal |
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// node For that, the internal method of |
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// quadruped_walkgen::ActionModelQuadruped needs to be accessed |
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// -> Creation of a 2nd list using dynamic_cast |
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int k_cum = 0; |
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std::vector< |
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boost::shared_ptr<quadruped_walkgen::ActionModelQuadrupedNonLinear> > |
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running_models_2; |
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// Iterate over all the phases of the gait matrix |
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// The first column of xref correspond to the current state = x0 |
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// Tmp is needed to use .data(), transformation of a column into a vector |
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Eigen::Array<double, 1, 12> tmp = Eigen::Array<double, 1, 12>::Zero(); |
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int max_index = int(gait.block(0, 0, 6, 1).array().min(1.).matrix().sum()); |
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for (int j = 0; j < max_index; j++) { |
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for (int k = k_cum; k < k_cum + int(gait(j, 0)); ++k) { |
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if (k < int(N)) { |
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boost::shared_ptr<quadruped_walkgen::ActionModelQuadrupedNonLinear> |
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model2 = boost::dynamic_pointer_cast< |
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quadruped_walkgen::ActionModelQuadrupedNonLinear>( |
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running_models[k]); |
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running_models_2.push_back(model2); |
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// Update model : |
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tmp = fsteps.block(j, 1, 1, 12).array(); |
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model2->update_model( |
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Eigen::Map<Eigen::Matrix<double, 3, 4> >(tmp.data(), 3, 4), |
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Eigen::Map<Eigen::Matrix<double, 12, 1> >( |
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xref.block(0, k + 1, 12, 1).data(), 12, 1), |
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Eigen::Map<Eigen::Matrix<double, 4, 1> >( |
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gait.block(j, 1, 1, 4).data(), 4, 1)); |
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} |
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} |
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k_cum += int(gait(j, 0)); |
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} |
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boost::shared_ptr<quadruped_walkgen::ActionModelQuadrupedNonLinear> |
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terminal_model_2 = boost::dynamic_pointer_cast< |
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quadruped_walkgen::ActionModelQuadrupedNonLinear>(terminal_model); |
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tmp = fsteps.block(max_index - 1, 1, 1, 12).array(); |
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Eigen::Array<double, 1, 4> gait_tmp = Eigen::Array<double, 1, 4>::Zero(); |
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gait_tmp = gait.block(max_index - 1, 1, 1, 4).array(); |
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terminal_model_2->update_model( |
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Eigen::Map<Eigen::Matrix<double, 3, 4> >(tmp.data(), 3, 4), |
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Eigen::Map<Eigen::Matrix<double, 12, 1> >(xref.block(0, 16, 12, 1).data(), |
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12, 1), |
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Eigen::Map<Eigen::Matrix<double, 4, 1> >(gait_tmp.data(), 4, 1)); |
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terminal_model_2->set_force_weights(Eigen::Matrix<double, 12, 1>::Zero()); |
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terminal_model_2->set_friction_weight(0); |
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boost::shared_ptr<crocoddyl::ShootingProblem> problem = |
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boost::make_shared<crocoddyl::ShootingProblem>(x0, running_models, |
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terminal_model); |
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crocoddyl::SolverDDP ddp(problem); |
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std::vector<Eigen::VectorXd> xs(int(N) + 1, x0); |
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Eigen::Matrix<double, 12, 1> u0; |
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u0 << 1, 0.2, 0.5, 1, 1, -0.2, -1, 1, 0.5, -1, -1, -0.5; |
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std::vector<Eigen::VectorXd> us(int(N), u0); |
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Eigen::ArrayXd duration(T); |
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// Updating the problem |
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for (unsigned int i = 0; i < T; ++i) { |
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crocoddyl::Timer timer; |
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updateModel(running_models_2, terminal_model_2, gait, xref, fsteps, N); |
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duration[i] = timer.get_duration(); |
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} |
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double avrg_duration = duration.sum() / T; |
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double min_duration = duration.minCoeff(); |
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double max_duration = duration.maxCoeff(); |
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std::cout << " UpdateModel [ms]: " << avrg_duration << " (" << min_duration |
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<< "-" << max_duration << ")" << std::endl; |
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// Solving the optimal control problem |
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for (unsigned int i = 0; i < T; ++i) { |
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crocoddyl::Timer timer; |
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ddp.solve(xs, us, MAXITER); |
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duration[i] = timer.get_duration(); |
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} |
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avrg_duration = duration.sum() / T; |
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min_duration = duration.minCoeff(); |
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max_duration = duration.maxCoeff(); |
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std::cout << " DDP.solve [ms]: " << avrg_duration << " (" << min_duration |
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<< "-" << max_duration << ")" << std::endl; |
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// Running calc |
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for (unsigned int i = 0; i < T; ++i) { |
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crocoddyl::Timer timer; |
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problem->calc(xs, us); |
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duration[i] = timer.get_duration(); |
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} |
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avrg_duration = duration.sum() / T; |
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min_duration = duration.minCoeff(); |
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max_duration = duration.maxCoeff(); |
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std::cout << " ShootingProblem.calc [ms]: " << avrg_duration << " (" |
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<< min_duration << "-" << max_duration << ")" << std::endl; |
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// Running calcDiff |
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for (unsigned int i = 0; i < T; ++i) { |
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crocoddyl::Timer timer; |
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problem->calcDiff(xs, us); |
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duration[i] = timer.get_duration(); |
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} |
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avrg_duration = duration.sum() / T; |
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min_duration = duration.minCoeff(); |
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max_duration = duration.maxCoeff(); |
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std::cout << " ShootingProblem.calcDiff [ms]: " << avrg_duration << " (" |
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<< min_duration << "-" << max_duration << ")" << std::endl; |
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} |
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