standalone_rrg_dubins_car.cpp

RRG with a Dubins car model. This file provides the code for running the RRG algorithm for a Dubins car model, i.e., a robot with constraints on minimum turning radius. This robot model is usually adequate for describing many car-like robotic systems. The model involves a system with kinematic constraints and under-actuation. The dimensionality of the state space is three.

// Standard header files
#include<iostream>
using namespace std;


// SMP HEADER FILES ------
#include <smp/components/samplers/uniform.hpp>
#include <smp/components/distance_evaluators/kdtree.hpp>
#include <smp/components/extenders/dubins.hpp>
#include <smp/components/collision_checkers/standard.hpp>
#include <smp/components/model_checkers/reachability.hpp>

#include <smp/planners/rrg.hpp>

#include <smp/planner_utils/trajectory.hpp>


// SMP TYPE DEFINITIONS -------
using namespace smp;

// State, input, vertex_data, and edge_data definitions
typedef state_dubins state_t;
typedef input_dubins input_t;
typedef model_checker_reachability_vertex_data vertex_data_t;
typedef model_checker_reachability_edge_data edge_data_t;

// Create the typeparams structure
typedef struct _typeparams {
  typedef state_t state;
  typedef input_t input;
  typedef vertex_data_t vertex_data;
  typedef edge_data_t edge_data;
} typeparams; 

// Define the trajectory type
typedef trajectory<typeparams> trajectory_t;

// Define all planner component types
typedef sampler_uniform<typeparams,3> sampler_t;
typedef distance_evaluator_kdtree<typeparams,3> distance_evaluator_t;
typedef extender_dubins<typeparams> extender_t;
typedef collision_checker_standard<typeparams,2> collision_checker_t;
typedef model_checker_reachability<typeparams,2> model_checker_t;

// Define all algorithm types
typedef rrg<typeparams>  rrg_t;






int
main () {
  
  



  // 1. CREATE PLANNING OBJECTS

  // 1.a Create the components
  sampler_t sampler;
  distance_evaluator_t distance_evaluator;
  extender_t extender;
  collision_checker_t collision_checker;
  model_checker_t model_checker;

  // 1.b Create the planner algorithm
  rrg_t planner (sampler, distance_evaluator, extender, collision_checker, model_checker);

  planner.parameters.set_phase (2);   // The phase parameter can be used to run the algorithm as an RRT, 
                                      // See the documentation of the RRG algorithm for more information.

  planner.parameters.set_gamma (35.0);    // Set this parameter should be set at least to the side length of
                                          //   the (bounded) state space. E.g., if the state space is a box
                                          //   with side length L, then this parameter should be set to at 
                                          //   least L for rapid and efficient convergence in trajectory space.
  planner.parameters.set_dimension (3);
  planner.parameters.set_max_radius (5.0);  // This parameter is expecially capped to a certain to limit
                                            //   the amount of data that is published through the libbot interface.




  // 2. INITALIZE PLANNING OBJECTS
  
  // 2.a Initialize the sampler
  region<3> sampler_support;
  sampler_support.center[0] = 0.0;
  sampler_support.center[1] = 0.0;
  sampler_support.center[2] = 0.0;
  sampler_support.size[0] = 20.0;
  sampler_support.size[1] = 20.0;
  sampler_support.size[2] = 2.0*M_PI;
  sampler.set_support (sampler_support);

  
  // 2.b Initialize the distance evaluator
  //     Nothing to initialize. One could change the kdtree weights.


  // 2.c Initialize the extender

 
  // 2.d Initialize the collision checker
  region<2> obstacle_new;
  for (int i = 0; i < 2; i++) {
    obstacle_new.center[i] = 5.0;
    obstacle_new.size[i] = 5.0;
  }
  collision_checker.add_obstacle (obstacle_new);

  
  
  // 2.e Initialize the model checker
  region<2> region_goal;
  region_goal.center[0] = 8.0;
  region_goal.center[1] = 8.0;  
  region_goal.size[0] = 2.0;
  region_goal.size[1] = 2.0;
  model_checker.set_goal_region (region_goal);
  planner.init_model_checker (model_checker);
    
  
  // 2.f Initialize the planner
  state_t *state_initial = new state_t;
  for (int i = 0; i < 3; i++) {
    state_initial->state_vars[i] = 0.0;
  }
  planner.initialize (state_initial);

  





  // 3. RUN THE PLANNER 
  for (int i = 0; i < 2000; i++){

    planner.iteration ();
    
    if (i%100 == 0) {
      cout << "Iteration: " << i << endl;
    }

  }


  
  
  

  // 4. PUBLISH THE RESULTS THROUGH THE LIBBOT INTERFACE

  
  
  
  return 1;
  
}