RRT* with an airplane model (as a combined dubins car double integrator). This file provides the code for running the RRT* algorithm for a simple airplane model that consists of a combined dubins car (on the plane) and a double integrator (for the altitute), which are decoupled. This model involves a 5 dimensional state space with non-holonomic differential constraints with under-actuation.
// Standard header files #include<iostream> using namespace std; // SMP HEADER FILES ------ #include <smp/components/extenders/dubins_double_integrator.hpp> #include <smp/components/samplers/uniform.hpp> #include <smp/components/collision_checkers/standard.hpp> #include <smp/components/distance_evaluators/kdtree.hpp> #include <smp/components/multipurpose/minimum_time_reachability.hpp> #include <smp/planners/rrtstar.hpp> #include <smp/planner_utils/trajectory.hpp> // SMP TYPE DEFINITIONS ------- using namespace smp; // State, input, vertex_data, and edge_data definitions typedef state_dubins_double_integrator state_t; typedef input_dubins_double_integrator input_t; typedef minimum_time_reachability_vertex_data vertex_data_t; typedef minimum_time_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,5> sampler_t; typedef distance_evaluator_kdtree<typeparams,5> distance_evaluator_t; typedef extender_dubins_double_integrator<typeparams> extender_t; typedef collision_checker_standard<typeparams,3> collision_checker_t; typedef minimum_time_reachability<typeparams,5> min_time_reachability_t; // Define all algorithm types typedef rrtstar<typeparams> rrtstar_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; min_time_reachability_t min_time_reachability; // 1.b Create the planner algorithm -- Note that the min_time_reachability variable acts both // as a model checker and a cost evaluator. rrtstar_t planner (sampler, distance_evaluator, extender, collision_checker, min_time_reachability, min_time_reachability); 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 (25.0); // This parameter should be set to a high enough value. In practice, // one can use smaller values of this parameter to get a good // solution quickly, while preserving the asymptotic optimality. // 2. INITALIZE PLANNING OBJECTS // 2.a Initialize the sampler region<5> sampler_support; sampler_support.center[0] = 0.0; sampler_support.center[1] = 0.0; sampler_support.center[2] = 5.0; sampler_support.center[3] = 0.0; sampler_support.center[4] = 0.0; sampler_support.size[0] = 20.0; sampler_support.size[1] = 20.0; sampler_support.size[2] = 10.0; sampler_support.size[3] = 2.0*M_PI; sampler_support.size[4] = 2.0; 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<3> obstacle_new; obstacle_new.center[0] = 5.0; obstacle_new.center[1] = 5.0; obstacle_new.center[2] = 5.0; obstacle_new.size[0] = 5.0; obstacle_new.size[1] = 5.0; obstacle_new.size[2] = 10.0; collision_checker.add_obstacle (obstacle_new); // 2.e Initialize the model checker and the cost evaluator (with minimum_time_reachability). region<5> region_goal; region_goal.center[0] = 8.0; region_goal.center[1] = 8.0; region_goal.center[2] = 5.0; region_goal.center[3] = 0.0; region_goal.center[4] = 0.0; region_goal.size[0] = 2.0; region_goal.size[1] = 2.0; region_goal.size[2] = 10.0; region_goal.size[3] = 10.0; region_goal.size[4] = 2.0; min_time_reachability.set_goal_region (region_goal); // 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. GET THE RESULTS trajectory_t trajectory_final; min_time_reachability.get_solution (trajectory_final); return 1; }