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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 "crocoddyl/core/codegen/action-base.hpp" |
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#include <quadruped-walkgen/quadruped_augmented.hpp> |
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#include <quadruped-walkgen/quadruped_step.hpp> |
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#include <quadruped-walkgen/quadruped_step_time.hpp> |
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#include <quadruped-walkgen/quadruped_time.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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#define STDDEV(vec) \ |
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std::sqrt(((vec - vec.mean())).square().sum() / (double(vec.size()) - 1.)) |
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#define AVG(vec) (vec.mean()) |
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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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boost::shared_ptr<crocoddyl::ActionModelAbstract> model_test; |
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model_test = |
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boost::make_shared<quadruped_walkgen::ActionModelQuadrupedStepTime>(); |
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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, 20, 1> x0; |
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x0 << 0., 0., 0.2, 0., 0., 0., 0.2, 0., 0., 0., 0., 0., 0.1946, 0.15005, |
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0.204, -0.137, -0.184, 0.14, -0.1946, -0.1505; |
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Eigen::Matrix<double, 4, 1> S; |
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S << 0, 1, 1, 0; |
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Eigen::Matrix<double, 3, 4> l_feet; // computed by previous gait cycle |
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l_feet << 0.1946, 0.21, -0.18, -0.19, 0.15, -0.16, 0.145, -0.135, 0.0, 0.0, |
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0.0, 0.0; |
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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) << 0., 0., 0.2, 0., 0., 0., 0.1, 0., 0., 0., 0., |
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0.; // 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 << 7, 0, 1, 1, 0, 1, 1, 1, 1, 1, 7, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, |
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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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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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if (int(gait.block(j, 1, 1, 4).sum()) == 4) { |
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boost::shared_ptr<crocoddyl::ActionModelAbstract> model = |
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boost::make_shared<quadruped_walkgen::ActionModelQuadrupedStep>(); |
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running_models.push_back(model); |
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} |
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boost::shared_ptr<crocoddyl::ActionModelAbstract> model = |
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boost::make_shared< |
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quadruped_walkgen::ActionModelQuadrupedAugmented>(); |
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running_models.push_back(model); |
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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<crocoddyl::ActionModelAbstract> terminal_model; |
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terminal_model = |
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boost::make_shared<quadruped_walkgen::ActionModelQuadrupedAugmented>(); |
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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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k_cum = 0; |
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Eigen::Matrix<double, 12, 1> u0; |
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u0 << 1, 0.2, 8, 1, 1, 8, -1, 1, 8, -1, -1, 8; |
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std::vector<Eigen::VectorXd> us; |
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Eigen::Matrix<double, 4, 1> u0_step; |
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u0_step << 0.05, 0.01, 0.02, 0.06; |
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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, 3, 4> tmp = Eigen::Array<double, 3, 4>::Zero(); |
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Eigen::Matrix<double, 1, 4> S_tmp; |
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S_tmp.setZero(); |
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int gap = 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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if (int(gait.block(j, 1, 1, 4).sum()) == 4) { |
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boost::shared_ptr<quadruped_walkgen::ActionModelQuadrupedStep> |
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model3 = boost::dynamic_pointer_cast< |
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quadruped_walkgen::ActionModelQuadrupedStep>( |
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running_models[k + gap]); |
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tmp = l_feet.array(); |
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S_tmp = gait.block(j, 1, 1, 4) - gait.block(j - 1, 1, 1, 4); |
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model3->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, 12, 1).data(), 12, 1), |
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Eigen::Map<Eigen::Matrix<double, 4, 1> >(S_tmp.data(), 4, 1), |
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Eigen::Matrix<double, 3, 4>::Zero(), |
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Eigen::Matrix<double, 3, 4>::Zero(), |
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Eigen::Matrix<double, 3, 4>::Zero(), |
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Eigen::Matrix<double, 3, 4>::Zero(), |
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Eigen::Matrix<double, 3, 3>::Zero(), |
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Eigen::Matrix<double, 3, 1>::Zero(), 0.16); |
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gap = gap + 1; |
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us.push_back(u0_step); |
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} |
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// std::cout << "indice :" << gap << std::endl ; |
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boost::shared_ptr<quadruped_walkgen::ActionModelQuadrupedAugmented> |
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model2 = boost::dynamic_pointer_cast< |
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quadruped_walkgen::ActionModelQuadrupedAugmented>( |
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running_models[k + gap]); |
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// Update model : |
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tmp = l_feet.array(); |
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if (int(gait.block(j, 1, 1, 4).sum()) == 4 and gap == 1) { |
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model2->set_stop_weights(Eigen::Matrix<double, 8, 1>::Constant(1)); |
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} else { |
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model2->set_stop_weights(Eigen::Matrix<double, 8, 1>::Zero()); |
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} |
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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, 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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us.push_back(u0); |
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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::ActionModelQuadrupedAugmented> |
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terminal_model_2 = boost::dynamic_pointer_cast< |
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quadruped_walkgen::ActionModelQuadrupedAugmented>(terminal_model); |
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tmp = l_feet.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, 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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terminal_model_2->set_stop_weights(Eigen::Matrix<double, 8, 1>::Zero()); |
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//////////////////////////////////// |
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// Code gen |
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// //////////////////////////////////// |
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// typedef CppAD::AD<CppAD::cg::CG<double> > ADScalar; |
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// // Code generation of the running an terminal models |
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// boost::shared_ptr<crocoddyl::ActionModelAbstractTpl<ADScalar> > |
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// ad_runningModel, ad_terminalModel; |
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// crocoddyl::benchmark::build_arm_action_models(ad_runningModel, |
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// ad_terminalModel); boost::shared_ptr<crocoddyl::ActionModelAbstract> |
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// cg_runningModel = |
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// boost::make_shared<crocoddyl::ActionModelCodeGen>(ad_runningModel, |
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// runningModel, "arm_manipulation_running_cg"); |
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// boost::shared_ptr<crocoddyl::ActionModelAbstract> cg_terminalModel = |
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// boost::make_shared<crocoddyl::ActionModelCodeGen>(ad_terminalModel, |
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// terminalModel, |
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// "arm_manipulation_terminal_cg"); |
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// for (int k = 0 ; k < running_models.size() ; j++ ) { |
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// } |
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std::cout << running_models.size() << std::endl; |
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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(running_models.size() + 1, x0); |
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// Eigen::Matrix<double,12,1> u0 ; |
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// u0 << 1,0.2,8, 1,1,8, -1,1,8, -1,-1,8; |
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// std::vector<Eigen::VectorXd> us(int(N), u0); |
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Eigen::ArrayXd duration(T); |
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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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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 << " 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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