edge_obstacle.h

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 *
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 *  TU Dortmund - Institute of Control Theory and Systems Engineering.
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 *     from this software without specific prior written permission.
 *
 *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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 * Notes:
 * The following class is derived from a class defined by the
 * g2o-framework. g2o is licensed under the terms of the BSD License.
 * Refer to the base class source for detailed licensing information.
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 * Author: Christoph Rösmann
 *********************************************************************/
#ifndef EDGE_OBSTACLE_H_
#define EDGE_OBSTACLE_H_

#include <teb_local_planner/obstacles.h>
#include <teb_local_planner/robot_footprint_model.h>
#include <teb_local_planner/g2o_types/vertex_pose.h>
#include <teb_local_planner/g2o_types/base_teb_edges.h>
#include <teb_local_planner/g2o_types/penalties.h>
#include <teb_local_planner/teb_config.h>



namespace teb_local_planner
{

class EdgeObstacle : public BaseTebUnaryEdge<1, const Obstacle*, VertexPose>
{
public:
    
  EdgeObstacle() 
  {
    _measurement = NULL;
  }
 
  void computeError()
  {
    ROS_ASSERT_MSG(cfg_ && _measurement && robot_model_, "You must call setTebConfig(), setObstacle() and setRobotModel() on EdgeObstacle()");
    const VertexPose* bandpt = static_cast<const VertexPose*>(_vertices[0]);

    double dist = robot_model_->calculateDistance(bandpt->pose(), _measurement);

    // Original obstacle cost.
    _error[0] = penaltyBoundFromBelow(dist, cfg_->obstacles.min_obstacle_dist, cfg_->optim.penalty_epsilon);

    if (cfg_->optim.obstacle_cost_exponent != 1.0 && cfg_->obstacles.min_obstacle_dist > 0.0)
    {
      // Optional non-linear cost. Note the max cost (before weighting) is
      // the same as the straight line version and that all other costs are
      // below the straight line (for positive exponent), so it may be
      // necessary to increase weight_obstacle and/or the inflation_weight
      // when using larger exponents.
      _error[0] = cfg_->obstacles.min_obstacle_dist * std::pow(_error[0] / cfg_->obstacles.min_obstacle_dist, cfg_->optim.obstacle_cost_exponent);
    }

    ROS_ASSERT_MSG(std::isfinite(_error[0]), "EdgeObstacle::computeError() _error[0]=%f\n",_error[0]);
  }

#ifdef USE_ANALYTIC_JACOBI
#if 0

  void linearizeOplus()
  {
    ROS_ASSERT_MSG(cfg_, "You must call setTebConfig on EdgePointObstacle()");
    const VertexPose* bandpt = static_cast<const VertexPose*>(_vertices[0]);
    
    Eigen::Vector2d deltaS = *_measurement - bandpt->position(); 
    double angdiff = atan2(deltaS[1],deltaS[0])-bandpt->theta();
    
    double dist_squared = deltaS.squaredNorm();
    double dist = sqrt(dist_squared);
    
    double aux0 = sin(angdiff);
    double dev_left_border = penaltyBoundFromBelowDerivative(dist*fabs(aux0),cfg_->obstacles.min_obstacle_dist,cfg_->optim.penalty_epsilon);

    if (dev_left_border==0)
    {
      _jacobianOplusXi( 0 , 0 ) = 0;
      _jacobianOplusXi( 0 , 1 ) = 0;
      _jacobianOplusXi( 0 , 2 ) = 0;
      return;
    }
    
    double aux1 = -fabs(aux0) / dist;
    double dev_norm_x = deltaS[0]*aux1;
    double dev_norm_y = deltaS[1]*aux1;
    
    double aux2 = cos(angdiff) * g2o::sign(aux0);
    double aux3 = aux2 / dist_squared;
    double dev_proj_x = aux3 * deltaS[1] * dist;
    double dev_proj_y = -aux3 * deltaS[0] * dist;
    double dev_proj_angle = -aux2;
    
    _jacobianOplusXi( 0 , 0 ) = dev_left_border * ( dev_norm_x + dev_proj_x );
    _jacobianOplusXi( 0 , 1 ) = dev_left_border * ( dev_norm_y + dev_proj_y );
    _jacobianOplusXi( 0 , 2 ) = dev_left_border * dev_proj_angle;
  }
#endif
#endif
  
  void setObstacle(const Obstacle* obstacle)
  {
    _measurement = obstacle;
  }
    
  void setRobotModel(const BaseRobotFootprintModel* robot_model)
  {
    robot_model_ = robot_model;
  }
    
  void setParameters(const TebConfig& cfg, const BaseRobotFootprintModel* robot_model, const Obstacle* obstacle)
  {
    cfg_ = &cfg;
    robot_model_ = robot_model;
    _measurement = obstacle;
  }
  
protected:

  const BaseRobotFootprintModel* robot_model_; 
  
public:         
  EIGEN_MAKE_ALIGNED_OPERATOR_NEW

};
  


class EdgeInflatedObstacle : public BaseTebUnaryEdge<2, const Obstacle*, VertexPose>
{
public:
    
  EdgeInflatedObstacle() 
  {
    _measurement = NULL;
  }
 
  void computeError()
  {
    ROS_ASSERT_MSG(cfg_ && _measurement && robot_model_, "You must call setTebConfig(), setObstacle() and setRobotModel() on EdgeInflatedObstacle()");
    const VertexPose* bandpt = static_cast<const VertexPose*>(_vertices[0]);

    double dist = robot_model_->calculateDistance(bandpt->pose(), _measurement);

    // Original "straight line" obstacle cost. The max possible value
    // before weighting is min_obstacle_dist
    _error[0] = penaltyBoundFromBelow(dist, cfg_->obstacles.min_obstacle_dist, cfg_->optim.penalty_epsilon);

    if (cfg_->optim.obstacle_cost_exponent != 1.0 && cfg_->obstacles.min_obstacle_dist > 0.0)
    {
      // Optional non-linear cost. Note the max cost (before weighting) is
      // the same as the straight line version and that all other costs are
      // below the straight line (for positive exponent), so it may be
      // necessary to increase weight_obstacle and/or the inflation_weight
      // when using larger exponents.
      _error[0] = cfg_->obstacles.min_obstacle_dist * std::pow(_error[0] / cfg_->obstacles.min_obstacle_dist, cfg_->optim.obstacle_cost_exponent);
    }

    // Additional linear inflation cost
    _error[1] = penaltyBoundFromBelow(dist, cfg_->obstacles.inflation_dist, 0.0);


    ROS_ASSERT_MSG(std::isfinite(_error[0]) && std::isfinite(_error[1]), "EdgeInflatedObstacle::computeError() _error[0]=%f, _error[1]=%f\n",_error[0], _error[1]);
  }

  void setObstacle(const Obstacle* obstacle)
  {
    _measurement = obstacle;
  }
    
  void setRobotModel(const BaseRobotFootprintModel* robot_model)
  {
    robot_model_ = robot_model;
  }
    
  void setParameters(const TebConfig& cfg, const BaseRobotFootprintModel* robot_model, const Obstacle* obstacle)
  {
    cfg_ = &cfg;
    robot_model_ = robot_model;
    _measurement = obstacle;
  }
  
protected:

  const BaseRobotFootprintModel* robot_model_; 
  
public:         
  EIGEN_MAKE_ALIGNED_OPERATOR_NEW

};
    

} // end namespace

#endif