normal_distribution_sampling.cpp

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#include <stomp_moveit/noise_generators/normal_distribution_sampling.h>
#include <stomp_moveit/utils/multivariate_gaussian.h>
#include <XmlRpcException.h>
#include <pluginlib/class_list_macros.h>
#include <ros/console.h>

PLUGINLIB_EXPORT_CLASS(stomp_moveit::noise_generators::NormalDistributionSampling,stomp_moveit::noise_generators::StompNoiseGenerator);

/*
 * These coefficients correspond to the five point stencil method
 */
static const std::vector<double> ACC_MATRIX_DIAGONAL_VALUES = {-1.0/12.0, 16.0/12.0, -30.0/12.0, 16.0/12.0, -1.0/12.0};
static const std::vector<int> ACC_MATRIX_DIAGONAL_INDICES = {-2, -1, 0 ,1, 2};

namespace stomp_moveit
{

namespace noise_generators
{

NormalDistributionSampling::NormalDistributionSampling():
    name_("NormalDistributionSampling")
{
  // TODO Auto-generated constructor stub

}

NormalDistributionSampling::~NormalDistributionSampling()

{
  // TODO Auto-generated destructor stub
}

bool NormalDistributionSampling::initialize(moveit::core::RobotModelConstPtr robot_model_ptr,
                        const std::string& group_name,const XmlRpc::XmlRpcValue& config)
{
  using namespace moveit::core;

  group_ = group_name;
  const JointModelGroup* joint_group = robot_model_ptr->getJointModelGroup(group_name);
  if(!joint_group)
  {
    ROS_ERROR("Invalid joint group %s",group_name.c_str());
    return false;
  }

  stddev_.resize(joint_group->getActiveJointModelNames().size());


  return configure(config);
}

bool NormalDistributionSampling::configure(const XmlRpc::XmlRpcValue& config)
{
  using namespace XmlRpc;

  try
  {
    XmlRpcValue c = config;
    XmlRpcValue stddev_param = c["stddev"];

    if(stddev_param.size() < stddev_.size())
    {
      ROS_ERROR("%s the 'stddev' parameter has fewer elements than the number of joints",getName().c_str());
      return false;
    }

    stddev_.resize(stddev_param.size());
    for(auto i = 0u; i < stddev_param.size(); i++)
    {
      stddev_[i] = static_cast<double>(stddev_param[i]);
    }
  }
  catch(XmlRpc::XmlRpcException& e)
  {
    ROS_ERROR("%s failed to load parameters",getName().c_str());
    return false;
  }

  return true;
}

bool NormalDistributionSampling::setMotionPlanRequest(const planning_scene::PlanningSceneConstPtr& planning_scene,
                 const moveit_msgs::MotionPlanRequest &req,
                 const stomp_core::StompConfiguration &config,
                 moveit_msgs::MoveItErrorCodes& error_code)
{
  using namespace Eigen;

  auto fill_diagonal = [](Eigen::MatrixXd& m,double coeff,int diag_index)
  {
    std::size_t size = m.rows() - std::abs(diag_index);
    m.diagonal(diag_index) = VectorXd::Constant(size,coeff);
  };

  // creating finite difference acceleration matrix
  std::size_t num_timesteps = config.num_timesteps;
  Eigen::MatrixXd A = MatrixXd::Zero(num_timesteps,num_timesteps);
  int num_elements = (int((ACC_MATRIX_DIAGONAL_INDICES.size() -1)/2.0) + 1)* num_timesteps ;
  for(auto i = 0u; i < ACC_MATRIX_DIAGONAL_INDICES.size() ; i++)
  {
    fill_diagonal(A,ACC_MATRIX_DIAGONAL_VALUES[i],ACC_MATRIX_DIAGONAL_INDICES[i]);
  }

  // create and scale covariance matrix
  Eigen::MatrixXd covariance = MatrixXd::Identity(num_timesteps,num_timesteps);
  covariance = A.transpose() * A;
  covariance = covariance.fullPivLu().inverse();
  double max_val = covariance.array().abs().matrix().maxCoeff();
  covariance /= max_val;

  // create random generators
  rand_generators_.resize(stddev_.size());
  for(auto& r: rand_generators_)
  {
    r.reset(new utils::MultivariateGaussian(VectorXd::Zero(num_timesteps),covariance));
  }

  // preallocating noise data
  raw_noise_.resize(config.num_timesteps);
  raw_noise_.setZero();

  return true;
}


bool NormalDistributionSampling::generateNoise(const Eigen::MatrixXd& parameters,
                                     std::size_t start_timestep,
                                     std::size_t num_timesteps,
                                     int iteration_number,
                                     int rollout_number,
                                     Eigen::MatrixXd& parameters_noise,
                                     Eigen::MatrixXd& noise)
{
  if(parameters.rows() != stddev_.size())
  {
    ROS_ERROR("Number of parameters %i differs from what was preallocated ",int(parameters.rows()));
    return false;
  }


  for(auto d = 0u; d < parameters.rows() ; d++)
  {
    rand_generators_[d]->sample(raw_noise_);
    noise.row(d).transpose() = stddev_[d] * raw_noise_;
    parameters_noise.row(d) = parameters.row(d) + noise.row(d);
  }

  return true;
}

} /* namespace noise_generators */
} /* namespace stomp_moveit */