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// Copyright (c) 2017, Joseph Mirabel
// Authors: Joseph Mirabel (joseph.mirabel@laas.fr)
//
// This file is part of hpp-manipulation.
// hpp-manipulation is free software: you can redistribute it
// and/or modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation, either version
// 3 of the License, or (at your option) any later version.
//
// hpp-manipulation is distributed in the hope that it will be
// useful, but WITHOUT ANY WARRANTY; without even the implied warranty
// of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
// General Lesser Public License for more details. You should have
// received a copy of the GNU Lesser General Public License along with
// hpp-manipulation. If not, see <http://www.gnu.org/licenses/>.
#ifndef HPP_MANIPULATION_STEERING_METHOD_CROSS_STATE_OPTIMIZATION_HH
# define HPP_MANIPULATION_STEERING_METHOD_CROSS_STATE_OPTIMIZATION_HH
# include <hpp/core/steering-method.hh>
# include <hpp/manipulation/config.hh>
# include <hpp/manipulation/fwd.hh>
# include <hpp/manipulation/problem.hh>
# include <hpp/manipulation/steering-method/fwd.hh>
namespace hpp {
namespace manipulation {
namespace steeringMethod {
/// \addtogroup steering_method
/// \{
/// Optimization-based steering method.
///
/// #### Methodology
///
/// Given two configuration \f$ (q_1,q_2) \f$, this class formulates and
/// solves the problem as follows.
/// - Compute the corresponding states \f$ (s_1, s_2) \f$.
/// - For a each path \f$ (e_0, ... e_n) \f$ between \f$ (s_1, s_2) \f$
/// in the constraint graph, do:
/// - define \f$ n-1 \f$ intermediate configuration \f$ p_i \f$,
/// - initialize the optimization problem, as explained below,
/// - solve the optimization problem, which gives \f$ p^*_i \f$,
/// - in case of failure, continue the loop.
/// - call the Edge::build of each \f$ e_j \f$ for each consecutive
/// \f$ (p^*_i, p^*_{i+1}) \f$.
///
/// #### Problem formulation
/// Find \f$ (p_i) \f$ such that:
/// - \f$ p_0 = q_1 \f$,
/// - \f$ p_{n+1} = q_2 \f$,
/// - \f$ p_i \f$ is in state between \f$ (e_{i-1}, e_i) \f$, (\ref StateFunction)
/// - \f$ (p_i, p_{i+1}) \f$ are reachable with transition \f$ e_i \f$ (\ref EdgeFunction).
class HPP_MANIPULATION_DLLAPI CrossStateOptimization :
public SteeringMethod
{
public:
static CrossStateOptimizationPtr_t create (const Problem& problem);
/// \warning core::Problem will be casted to Problem
static CrossStateOptimizationPtr_t create
(const core::Problem& problem);
template <typename T>
static CrossStateOptimizationPtr_t create
(const core::Problem& problem);
core::SteeringMethodPtr_t copy () const;
protected:
CrossStateOptimization (const Problem& problem) :
SteeringMethod (problem)
{}
CrossStateOptimization (const CrossStateOptimization& other) :
SteeringMethod (other),
weak_ ()
{}
core::PathPtr_t impl_compute (ConfigurationIn_t q1, ConfigurationIn_t q2) const;
void init (CrossStateOptimizationWkPtr_t weak)
{
SteeringMethod::init (weak);
weak_ = weak;
}
private:
struct GraphSearchData;
struct OptimizationData;
/// Step 1 of the algorithm
/// \return whether the max depth was reached.
bool findTransitions (GraphSearchData& data) const;
/// Step 2 of the algorithm
graph::Edges_t getTransitionList (GraphSearchData& data, const std::size_t& i) const;
/// Step 3 of the algorithm
void buildOptimizationProblem (OptimizationData& d, const graph::Edges_t& edges) const;
core::PathVectorPtr_t buildPath (OptimizationData& d, const graph::Edges_t& edges) const;
/// Weak pointer to itself
CrossStateOptimizationWkPtr_t weak_;
}; // class CrossStateOptimization
template <typename T>
CrossStateOptimizationPtr_t CrossStateOptimization::create
(const core::Problem& problem)
{
CrossStateOptimizationPtr_t gsm = CrossStateOptimization::create (problem);
gsm->innerSteeringMethod (T::create (problem));
return gsm;
}
} // namespace steeringMethod
} // namespace manipulation
} // namespace hpp
#endif // HPP_MANIPULATION_STEERING_METHOD_CROSS_STATE_OPTIMIZATION_HH