irsim.lib.path_planners.informed_rrt_star
=========================================

.. py:module:: irsim.lib.path_planners.informed_rrt_star

.. autoapi-nested-parse::

   Informed RRT* path planner.

   Collision precedence (inherited from :class:`RRT`):
     1. Grid lookup (O(1) per cell) when ``env_map.grid`` is not ``None``.
     2. Shapely geometry intersection when the grid is unavailable.

   Informed RRT* improves upon RRT* by constraining the sampling region to an
   ellipsoidal subset of the planning space once an initial solution has been
   found.  The ellipse is defined by the start and goal positions as foci, and
   the current best path cost determines its size.  As the cost decreases, the
   ellipse shrinks, focusing exploration on the region that can actually produce
   shorter paths.

   Reference:
       J. D. Gammell, S. S. Srinivasa, and T. D. Barfoot,
       "Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct
       Sampling of an Admissible Ellipsoidal Heuristic," in Proc. IEEE/RSJ
       Int. Conf. Intelligent Robots and Systems (IROS), 2014.

   Implementation reference:
       ZJU-FAST-Lab/sampling-based-path-finding
       https://github.com/ZJU-FAST-Lab/sampling-based-path-finding
       (C++ implementation of RRT* with informed sampling and ellipsoidal
       heuristic.)

   Adapted for ir-sim.



Attributes
----------

.. autoapisummary::

   irsim.lib.path_planners.informed_rrt_star.logger


Classes
-------

.. autoapisummary::

   irsim.lib.path_planners.informed_rrt_star.InformedRRTStar


Module Contents
---------------

.. py:data:: logger

.. py:class:: InformedRRTStar(env_map: irsim.world.map.EnvGridMap, robot: Any, expand_dis: float = 1.5, path_resolution: float = 0.25, goal_sample_rate: int = 10, max_iter: int = 500, connect_circle_dist: float = 50.0, search_until_max_iter: bool = True)

   Bases: :py:obj:`irsim.lib.path_planners.rrt_star.RRTStar`


   Informed RRT* path planner.

   After finding an initial feasible path the sampler switches from uniform
   random sampling to sampling inside an informed ellipsoidal set whose
   foci are the start and goal.  This dramatically speeds up convergence
   towards the optimal path.

   Initialise the Informed RRT* planner.


   .. py:attribute:: Node


   .. py:method:: planning(start_pose: list[float], goal_pose: list[float], show_animation: bool = True) -> numpy.ndarray | None

      Informed RRT* path planning.

      :param start_pose: Start position ``[x, y]``.
      :param goal_pose: Goal position ``[x, y]``.
      :param show_animation: Render tree, ellipse and best path during
                             planning.

      :returns: ``(2, N)`` waypoint array or *None*.