irsim.lib.path_planners.rrt_star
================================

.. py:module:: irsim.lib.path_planners.rrt_star

.. autoapi-nested-parse::

   RRT* (RRT-Star) 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.

   Reference:
       S. Karaman and E. Frazzoli, "Sampling-based Algorithms for Optimal
       Motion Planning," Int. J. Robotics Research, 2011.

   Implementation reference:
       ZJU-FAST-Lab/sampling-based-path-finding
       https://github.com/ZJU-FAST-Lab/sampling-based-path-finding
       (C++ implementation with kd-tree nearest-neighbour and range queries,
       optimised parent selection, and BFS cost propagation.)

   Adapted for ir-sim.



Attributes
----------

.. autoapisummary::

   irsim.lib.path_planners.rrt_star.logger


Classes
-------

.. autoapisummary::

   irsim.lib.path_planners.rrt_star.RRTStar


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

.. py:data:: logger

.. py:class:: RRTStar(env_map: irsim.world.map.EnvGridMap, robot: Any, expand_dis: float = 1.5, path_resolution: float = 0.25, goal_sample_rate: int = 5, max_iter: int = 500, connect_circle_dist: float = 0.5, search_until_max_iter: bool = False)

   Bases: :py:obj:`irsim.lib.path_planners.rrt.RRT`


   RRT* path planner with asymptotic optimality.

   Extends :class:`RRT` with:

   * **Neighbour search** — after steering, all nodes within a shrinking
     radius are considered as potential parents (``_choose_parent``).
   * **Rewiring** — after inserting a new node, nearby nodes are checked
     to see whether their cost can be reduced by re-parenting through
     the new node (``_rewire``).
   * **Efficient cost propagation** — ``_change_node_parent`` uses BFS
     through the ``children`` list for O(subtree) updates.

   Initialise the RRT* planner.


   .. py:attribute:: Node


   .. py:attribute:: connect_circle_dist
      :value: 0.5



   .. py:attribute:: search_until_max_iter
      :value: False



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

      RRT* path planning.

      :param start_pose: Start position ``[x, y]``.
      :param goal_pose: Goal position ``[x, y]``.
      :param show_animation: Render the tree during planning.

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