| Line |
Branch |
Exec |
Source |
| 1 |
|
|
/////////////////////////////////////////////////////////////////////////////// |
| 2 |
|
|
// BSD 3-Clause License |
| 3 |
|
|
// |
| 4 |
|
|
// Copyright (C) 2019-2023, LAAS-CNRS, University of Edinburgh |
| 5 |
|
|
// Heriot-Watt University |
| 6 |
|
|
// Copyright note valid unless otherwise stated in individual files. |
| 7 |
|
|
// All rights reserved. |
| 8 |
|
|
/////////////////////////////////////////////////////////////////////////////// |
| 9 |
|
|
|
| 10 |
|
|
#include "crocoddyl/core/solvers/fddp.hpp" |
| 11 |
|
|
#include "crocoddyl/core/utils/callbacks.hpp" |
| 12 |
|
|
#include "crocoddyl/core/utils/timer.hpp" |
| 13 |
|
|
#include "factory/arm.hpp" |
| 14 |
|
|
|
| 15 |
|
✗ |
int main(int argc, char* argv[]) { |
| 16 |
|
✗ |
bool CALLBACKS = false; |
| 17 |
|
✗ |
unsigned int N = 100; // number of nodes |
| 18 |
|
✗ |
unsigned int T = 5e3; // number of trials |
| 19 |
|
✗ |
unsigned int MAXITER = 1; |
| 20 |
|
✗ |
if (argc > 1) { |
| 21 |
|
✗ |
T = atoi(argv[1]); |
| 22 |
|
|
} |
| 23 |
|
|
|
| 24 |
|
|
// Building the running and terminal models |
| 25 |
|
✗ |
boost::shared_ptr<crocoddyl::ActionModelAbstract> runningModel, terminalModel; |
| 26 |
|
✗ |
crocoddyl::benchmark::build_arm_action_models(runningModel, terminalModel); |
| 27 |
|
|
|
| 28 |
|
|
// Get the initial state |
| 29 |
|
|
boost::shared_ptr<crocoddyl::StateMultibody> state = |
| 30 |
|
|
boost::static_pointer_cast<crocoddyl::StateMultibody>( |
| 31 |
|
✗ |
runningModel->get_state()); |
| 32 |
|
✗ |
std::cout << "NQ: " << state->get_nq() << std::endl; |
| 33 |
|
✗ |
std::cout << "Number of nodes: " << N << std::endl; |
| 34 |
|
|
Eigen::VectorXd q0 = |
| 35 |
|
✗ |
state->get_pinocchio()->referenceConfigurations["arm_up"]; |
| 36 |
|
✗ |
Eigen::VectorXd x0(state->get_nx()); |
| 37 |
|
✗ |
x0 << q0, Eigen::VectorXd::Random(state->get_nv()); |
| 38 |
|
|
|
| 39 |
|
|
// For this optimal control problem, we define 100 knots (or running action |
| 40 |
|
|
// models) plus a terminal knot |
| 41 |
|
|
std::vector<boost::shared_ptr<crocoddyl::ActionModelAbstract> > runningModels( |
| 42 |
|
✗ |
N, runningModel); |
| 43 |
|
|
boost::shared_ptr<crocoddyl::ShootingProblem> problem = |
| 44 |
|
|
boost::make_shared<crocoddyl::ShootingProblem>(x0, runningModels, |
| 45 |
|
✗ |
terminalModel); |
| 46 |
|
✗ |
std::vector<Eigen::VectorXd> xs(N + 1, x0); |
| 47 |
|
|
std::vector<Eigen::VectorXd> us( |
| 48 |
|
✗ |
N, Eigen::VectorXd::Zero(runningModel->get_nu())); |
| 49 |
|
✗ |
for (unsigned int i = 0; i < N; ++i) { |
| 50 |
|
|
const boost::shared_ptr<crocoddyl::ActionModelAbstract>& model = |
| 51 |
|
✗ |
problem->get_runningModels()[i]; |
| 52 |
|
|
const boost::shared_ptr<crocoddyl::ActionDataAbstract>& data = |
| 53 |
|
✗ |
problem->get_runningDatas()[i]; |
| 54 |
|
✗ |
model->quasiStatic(data, us[i], x0); |
| 55 |
|
|
} |
| 56 |
|
|
|
| 57 |
|
|
// Formulating the optimal control problem |
| 58 |
|
✗ |
crocoddyl::SolverFDDP solver(problem); |
| 59 |
|
✗ |
if (CALLBACKS) { |
| 60 |
|
✗ |
std::vector<boost::shared_ptr<crocoddyl::CallbackAbstract> > cbs; |
| 61 |
|
✗ |
cbs.push_back(boost::make_shared<crocoddyl::CallbackVerbose>()); |
| 62 |
|
✗ |
solver.setCallbacks(cbs); |
| 63 |
|
|
} |
| 64 |
|
|
|
| 65 |
|
|
// Solving the optimal control problem |
| 66 |
|
✗ |
Eigen::ArrayXd duration(T); |
| 67 |
|
✗ |
for (unsigned int i = 0; i < T; ++i) { |
| 68 |
|
✗ |
crocoddyl::Timer timer; |
| 69 |
|
✗ |
solver.solve(xs, us, MAXITER, false, 0.1); |
| 70 |
|
✗ |
duration[i] = timer.get_duration(); |
| 71 |
|
|
} |
| 72 |
|
|
|
| 73 |
|
✗ |
double avrg_duration = duration.sum() / T; |
| 74 |
|
✗ |
double min_duration = duration.minCoeff(); |
| 75 |
|
✗ |
double max_duration = duration.maxCoeff(); |
| 76 |
|
✗ |
std::cout << " FDDP.solve [ms]: " << avrg_duration << " (" << min_duration |
| 77 |
|
✗ |
<< "-" << max_duration << ")" << std::endl; |
| 78 |
|
|
|
| 79 |
|
|
// Running calc |
| 80 |
|
✗ |
for (unsigned int i = 0; i < T; ++i) { |
| 81 |
|
✗ |
crocoddyl::Timer timer; |
| 82 |
|
✗ |
problem->calc(xs, us); |
| 83 |
|
✗ |
duration[i] = timer.get_duration(); |
| 84 |
|
|
} |
| 85 |
|
|
|
| 86 |
|
✗ |
avrg_duration = duration.sum() / T; |
| 87 |
|
✗ |
min_duration = duration.minCoeff(); |
| 88 |
|
✗ |
max_duration = duration.maxCoeff(); |
| 89 |
|
✗ |
std::cout << " ShootingProblem.calc [ms]: " << avrg_duration << " (" |
| 90 |
|
✗ |
<< min_duration << "-" << max_duration << ")" << std::endl; |
| 91 |
|
|
|
| 92 |
|
|
// Running calcDiff |
| 93 |
|
✗ |
for (unsigned int i = 0; i < T; ++i) { |
| 94 |
|
✗ |
crocoddyl::Timer timer; |
| 95 |
|
✗ |
problem->calcDiff(xs, us); |
| 96 |
|
✗ |
duration[i] = timer.get_duration(); |
| 97 |
|
|
} |
| 98 |
|
|
|
| 99 |
|
✗ |
avrg_duration = duration.sum() / T; |
| 100 |
|
✗ |
min_duration = duration.minCoeff(); |
| 101 |
|
✗ |
max_duration = duration.maxCoeff(); |
| 102 |
|
✗ |
std::cout << " ShootingProblem.calcDiff [ms]: " << avrg_duration << " (" |
| 103 |
|
✗ |
<< min_duration << "-" << max_duration << ")" << std::endl; |
| 104 |
|
|
} |
| 105 |
|
|
|