pathfinding3d.finder.ida_star module

class pathfinding3d.finder.ida_star.IDAStarFinder(heuristic=None, weight=1, diagonal_movement=DiagonalMovement.never, time_limit=TIME_LIMIT, max_runs=MAX_RUNS, track_recursion=True)[source]

Bases: Finder

Iterative Deeping A Star (IDA*) path-finder.

Recursion based on: http://www.apl.jhu.edu/~hall/AI-Programming/IDA-Star.html

Path retracing based on: V. Nageshwara Rao, Vipin Kumar and K. Ramesh “A Parallel Implementation of Iterative-Deeping-A*”, January 1987. ftp://ftp.cs.utexas.edu/.snapshot/hourly.1/pub/AI-Lab/tech-reports/ UT-AI-TR-87-46.pdf

based on the JavaScript implementation by Gerard Meier (www.gerardmeier.com)

Parameters:

  • heuristic (Callable | None) –

  • weight (int) –

  • diagonal_movement (int) –

  • time_limit (float) –

  • max_runs (int | float) –

  • track_recursion (bool) –

__init__(heuristic=None, weight=1, diagonal_movement=DiagonalMovement.never, time_limit=TIME_LIMIT, max_runs=MAX_RUNS, track_recursion=True)[source]

Find shortest path using IDA* algorithm

Parameters:

  • heuristic (Callable) – heuristic used to calculate distance of 2 points

  • weight (int) – weight for the edges

  • diagonal_movement (int) – if diagonal movement is allowed (see enum in diagonal_movement)

  • time_limit (float) – max. runtime in seconds

  • max_runs (int) – max. amount of tries until we abort the search (optional, only if we enter huge grids and have time constrains) <=0 means there are no constrains and the code might run on any large map.

  • track_recursion (bool) – if we should track recursion

search(node, g, cutoff, path, depth, end, grid)[source]

Recursive IDA* search implementation

Parameters:

  • node (GridNode) – current node

  • g (float) – cost from start to current node

  • cutoff (float) – cutoff cost

  • path (List[GridNode]) – path

  • depth (int) – current depth

  • end (GridNode) – end node

  • grid (Grid) – grid that stores all possible steps/tiles as 3D-list

Returns:

cutoff cost or end node

Return type:

Union[float, GridNode]

apply_heuristic(node_a, node_b, heuristic=None)

Helper function to apply heuristic

Parameters:

  • node_a (GridNode) – first node

  • node_b (GridNode) – second node

  • heuristic (Callable) – heuristic used to calculate distance of 2 points

Returns:

heuristic value

Return type:

float

check_neighbors(start, end, grid, open_list, open_value=1, backtrace_by=None)

find next path segment based on given node (or return path if we found the end)

Parameters:

  • start (GridNode) – start node

  • end (GridNode) – end node

  • grid (Grid) – grid that stores all possible steps/tiles as 3D-list

  • open_list (List) – stores nodes that will be processed next

  • open_value (int) –

Returns:

path

Return type:

Optional[List[GridNode]]

find_neighbors(grid, node, diagonal_movement=None)

Find neighbor, same for Djikstra, A*, Bi-A*, IDA*

Parameters:

  • grid (Grid) – grid that stores all possible steps/tiles as 3D-list

  • node (GridNode) – node to find neighbors for

  • diagonal_movement (int) – if diagonal movement is allowed (see enum in diagonal_movement)

Returns:

list of neighbors

Return type:

List[GridNode]

find_path(start, end, grid)[source]

Find a path from start to end node on grid using the IDA* algorithm

Parameters:

  • start (GridNode) – start node

  • end (GridNode) – end node

  • grid (Grid) – grid that stores all possible steps/tiles as 3D-list

Returns:

path, number of iterations

Return type:

Tuple[List, int]

keep_running()

Check, if we run into time or iteration constrains.

Raises:
process_node(grid, node, parent, end, open_list, open_value=1)

We check if the given node is part of the path by calculating its cost and add or remove it from our path

Parameters:

  • grid (Grid) – grid that stores all possible steps/tiles as 3D-list

  • node (GridNode) – the node we like to test (the neighbor in A* or jump-node in JumpPointSearch)

  • parent (GridNode) – the parent node (of the current node we like to test)

  • end (GridNode) – the end point to calculate the cost of the path

  • open_list (List) – the list that keeps track of our current path

  • open_value (bool) – needed if we like to set the open list to something else than True (used for bi-directional algorithms)