AStar

src.python_motion_planning.path_planner.graph_search.a_star.AStar

Bases: Dijkstra

Class for A* path planner.

Parameters:

Name	Type	Description	Default
*args		see the parent class.	()
*kwargs		see the parent class.	{}

References

[1] A Formal Basis for the heuristic Determination of Minimum Cost Paths

Examples:

Python Console Session

>>> map_ = Grid(bounds=[[0, 15], [0, 15]])
>>> planner = AStar(map_=map_, start=(5, 5), goal=(10, 10))
>>> path, path_info = planner.plan()
>>> print(path_info['success'])
True

Python Console Session

>>> planner.map_.type_map[3:10, 6] = TYPES.OBSTACLE
>>> path, path_info = planner.plan()
>>> print(path_info['success'])
True

Source code in src\python_motion_planning\path_planner\graph_search\a_star.py

Python

class AStar(Dijkstra):
    """
    Class for A* path planner.

    Args:
        *args: see the parent class.
        *kwargs: see the parent class.

    References:
        [1] A Formal Basis for the heuristic Determination of Minimum Cost Paths

    Examples:
        >>> map_ = Grid(bounds=[[0, 15], [0, 15]])
        >>> planner = AStar(map_=map_, start=(5, 5), goal=(10, 10))
        >>> path, path_info = planner.plan()
        >>> print(path_info['success'])
        True

        >>> planner.map_.type_map[3:10, 6] = TYPES.OBSTACLE
        >>> path, path_info = planner.plan()
        >>> print(path_info['success'])
        True
    """
    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)

    def __str__(self) -> str:
        return "A*"

    def plan(self) -> Union[List[Tuple[float, ...]], Dict[str, Any]]:
        """
        Interface for planning.

        Returns:
            path: A list containing the path waypoints
            path_info: A dictionary containing the path information
        """
        # OPEN list (priority queue) and CLOSED list (hash table)
        OPEN = []
        start_node = Node(self.start, None, 0, self.get_heuristic(self.start))
        heapq.heappush(OPEN, start_node)
        CLOSED = dict()

        while OPEN:
            node = heapq.heappop(OPEN)

            # exists in CLOSED list
            if node.current in CLOSED:
                continue

            # goal found
            if node.current == self.goal:
                CLOSED[node.current] = node
                path, length, cost = self.extract_path(CLOSED)
                return path, {
                    "success": True, 
                    "start": self.start, 
                    "goal": self.goal, 
                    "length": length, 
                    "cost": cost, 
                    "expand": CLOSED
                }

            for node_n in self.map_.get_neighbors(node, diagonal=self.diagonal): 
                # exists in CLOSED list
                if node_n.current in CLOSED:
                    continue

                node_n.g = node.g + self.get_cost(node.current, node_n.current)
                node_n.h = self.get_heuristic(node_n.current)

                # goal found
                if node_n.current == self.goal:
                    heapq.heappush(OPEN, node_n)
                    break

                # update OPEN list
                heapq.heappush(OPEN, node_n)

            CLOSED[node.current] = node

        self.failed_info[1]["expand"] = CLOSED
        return self.failed_info

plan()

Interface for planning.

Returns:

Name	Type	Description
path	Union[List[Tuple[float, ...]], Dict[str, Any]]	A list containing the path waypoints
path_info	Union[List[Tuple[float, ...]], Dict[str, Any]]	A dictionary containing the path information

Source code in src\python_motion_planning\path_planner\graph_search\a_star.py

Python

def plan(self) -> Union[List[Tuple[float, ...]], Dict[str, Any]]:
    """
    Interface for planning.

    Returns:
        path: A list containing the path waypoints
        path_info: A dictionary containing the path information
    """
    # OPEN list (priority queue) and CLOSED list (hash table)
    OPEN = []
    start_node = Node(self.start, None, 0, self.get_heuristic(self.start))
    heapq.heappush(OPEN, start_node)
    CLOSED = dict()

    while OPEN:
        node = heapq.heappop(OPEN)

        # exists in CLOSED list
        if node.current in CLOSED:
            continue

        # goal found
        if node.current == self.goal:
            CLOSED[node.current] = node
            path, length, cost = self.extract_path(CLOSED)
            return path, {
                "success": True, 
                "start": self.start, 
                "goal": self.goal, 
                "length": length, 
                "cost": cost, 
                "expand": CLOSED
            }

        for node_n in self.map_.get_neighbors(node, diagonal=self.diagonal): 
            # exists in CLOSED list
            if node_n.current in CLOSED:
                continue

            node_n.g = node.g + self.get_cost(node.current, node_n.current)
            node_n.h = self.get_heuristic(node_n.current)

            # goal found
            if node_n.current == self.goal:
                heapq.heappush(OPEN, node_n)
                break

            # update OPEN list
            heapq.heappush(OPEN, node_n)

        CLOSED[node.current] = node

    self.failed_info[1]["expand"] = CLOSED
    return self.failed_info