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Constraint.cpp
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136 lines (118 loc) · 4.79 KB
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/*********************************************************************
* Software License Agreement (BSD License)
*
* Copyright (c) 2014, Rice University
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Rice University nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*********************************************************************/
/* Author: Zachary Kingston */
#include "ompl/base/Constraint.h"
#include "ompl/base/spaces/constraint/ConstrainedStateSpace.h"
void ompl::base::Constraint::function(const State *state, Eigen::Ref<Eigen::VectorXd> out) const
{
function(*state->as<ConstrainedStateSpace::StateType>(), out);
}
void ompl::base::Constraint::jacobian(const State *state, Eigen::Ref<Eigen::MatrixXd> out) const
{
jacobian(*state->as<ConstrainedStateSpace::StateType>(), out);
}
void ompl::base::Constraint::jacobian(const Eigen::Ref<const Eigen::VectorXd> &x, Eigen::Ref<Eigen::MatrixXd> out) const
{
Eigen::VectorXd y1 = x;
Eigen::VectorXd y2 = x;
Eigen::VectorXd t1(getCoDimension());
Eigen::VectorXd t2(getCoDimension());
// Use a 7-point central difference stencil on each column.
for (std::size_t j = 0; j < n_; j++)
{
const double ax = std::fabs(x[j]);
// Make step size as small as possible while still giving usable accuracy.
const double h = std::sqrt(std::numeric_limits<double>::epsilon()) * (ax >= 1 ? ax : 1);
// Can't assume y1[j]-y2[j] == 2*h because of precision errors.
y1[j] += h;
y2[j] -= h;
function(y1, t1);
function(y2, t2);
const Eigen::VectorXd m1 = (t1 - t2) / (y1[j] - y2[j]);
y1[j] += h;
y2[j] -= h;
function(y1, t1);
function(y2, t2);
const Eigen::VectorXd m2 = (t1 - t2) / (y1[j] - y2[j]);
y1[j] += h;
y2[j] -= h;
function(y1, t1);
function(y2, t2);
const Eigen::VectorXd m3 = (t1 - t2) / (y1[j] - y2[j]);
out.col(j) = 1.5 * m1 - 0.6 * m2 + 0.1 * m3;
// Reset for next iteration.
y1[j] = y2[j] = x[j];
}
}
bool ompl::base::Constraint::project(State *state) const
{
return project(*state->as<ConstrainedStateSpace::StateType>());
}
bool ompl::base::Constraint::project(Eigen::Ref<Eigen::VectorXd> x) const
{
// Newton's method
unsigned int iter = 0;
double norm = 0;
Eigen::VectorXd f(getCoDimension());
Eigen::MatrixXd j(getCoDimension(), n_);
const double squaredTolerance = tolerance_ * tolerance_;
function(x, f);
while ((norm = f.squaredNorm()) > squaredTolerance && iter++ < maxIterations_)
{
jacobian(x, j);
x -= j.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV).solve(f);
function(x, f);
}
return norm < squaredTolerance;
}
double ompl::base::Constraint::distance(const State *state) const
{
return distance(*state->as<ConstrainedStateSpace::StateType>());
}
double ompl::base::Constraint::distance(const Eigen::Ref<const Eigen::VectorXd> &x) const
{
Eigen::VectorXd f(getCoDimension());
function(x, f);
return f.norm();
}
bool ompl::base::Constraint::isSatisfied(const State *state) const
{
return isSatisfied(*state->as<ConstrainedStateSpace::StateType>());
}
bool ompl::base::Constraint::isSatisfied(const Eigen::Ref<const Eigen::VectorXd> &x) const
{
Eigen::VectorXd f(getCoDimension());
function(x, f);
return f.allFinite() && f.squaredNorm() <= tolerance_ * tolerance_;
}