Grid¶

`src.python_motion_planning.common.env.map.grid.Grid` ¶

Bases: BaseMap

Class for Grid Map. The shape of each dimension of the grid map is determined by the base world and resolution. For each dimension, the conversion equation is: shape_grid = shape_world * resolution + 1 For example, if the base world is (30, 40) and the resolution is 0.5, the grid map will be (30 * 0.5 + 1, 40 * 0.5 + 1) = (61, 81).

Parameters:

Name	Type	Description	Default
`bounds`	`Iterable`	The size of map in the world (shape: (n, 2) (n>=2)). bounds[i, 0] means the lower bound of the world in the i-th dimension. bounds[i, 1] means the upper bound of the world in the i-th dimension.	`[[0, 30], [0, 40]]`
`resolution`	`float`	resolution of the grid map	`1.0`
`type_map`	`Union[GridTypeMap, ndarray]`	initial type map of the grid map (its shape must be the same as the converted grid map shape, and its dtype must be int)	`None`
`dtype`	`dtype`	data type of coordinates (must be int)	`int32`
`inflation_radius`	`float`	radius of the inflation	`0.0`

Examples:

Python Console Session

>>> grid_map = Grid(bounds=[[0, 51], [0, 31]], resolution=0.5)
>>> grid_map
Grid(bounds=[[ 0. 51.]
 [ 0. 31.]], resolution=0.5)

Python Console Session

>>> grid_map.bounds    # bounds of the base world
array([[ 0., 51.],
       [ 0., 31.]])

Python Console Session

>>> grid_map.dim
2

Python Console Session

>>> grid_map.resolution
0.5

Python Console Session

>>> grid_map.shape   # shape of the grid map
(102, 62)

Python Console Session

>>> grid_map.dtype
<class 'numpy.int32'>

Python Console Session

>>> grid_map.type_map
GridTypeMap(array(
[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]
), shape=(102, 62), dtype=int8)

Python Console Session

>>> grid_map.map_to_world((1, 2))
(0.75, 1.25)

Python Console Session

>>> grid_map.world_to_map((0.5, 1.0))
(0, 2)

Python Console Session

>>> grid_map.get_neighbors(Node((1, 2)))
[Node((0, 1), (1, 2), 0, 0), Node((0, 2), (1, 2), 0, 0), Node((0, 3), (1, 2), 0, 0), Node((1, 1), (1, 2), 0, 0), Node((1, 3), (1, 2), 0, 0), Node((2, 1), (1, 2), 0, 0), Node((2, 2), (1, 2), 0, 0), Node((2, 3), (1, 2), 0, 0)]

Python Console Session

>>> grid_map.get_neighbors(Node((1, 2)), diagonal=False)
[Node((2, 2), (1, 2), 0, 0), Node((0, 2), (1, 2), 0, 0), Node((1, 3), (1, 2), 0, 0), Node((1, 1), (1, 2), 0, 0)]

Python Console Session

>>> grid_map.type_map[1, 0] = TYPES.OBSTACLE     # place an obstacle
>>> grid_map.get_neighbors(Node((0, 0)))    # limited within the bounds
[Node((0, 1), (0, 0), 0, 0), Node((1, 0), (0, 0), 0, 0), Node((1, 1), (0, 0), 0, 0)]

Python Console Session

>>> grid_map.get_neighbors(Node((grid_map.shape[0] - 1, grid_map.shape[1] - 1)), diagonal=False)  # limited within the boundss
[Node((100, 61), (101, 61), 0, 0), Node((101, 60), (101, 61), 0, 0)]

Python Console Session

>>> grid_map.line_of_sight((1, 2), (3, 6))
[(1, 2), (1, 3), (2, 4), (2, 5), (3, 6)]

Python Console Session

>>> grid_map.line_of_sight((1, 2), (1, 2))
[(1, 2)]

Python Console Session

>>> grid_map.in_collision((1, 2), (3, 6))
False

Python Console Session

>>> grid_map.type_map[1, 3] = TYPES.OBSTACLE
>>> grid_map.update_esdf()
>>> grid_map.in_collision((1, 2), (3, 6))
True

Source code in src\python_motion_planning\common\env\map\grid.py

Python

class Grid(BaseMap):
    """
    Class for Grid Map.
    The shape of each dimension of the grid map is determined by the base world and resolution.
    For each dimension, the conversion equation is: shape_grid = shape_world * resolution + 1
    For example, if the base world is (30, 40) and the resolution is 0.5, the grid map will be (30 * 0.5 + 1, 40 * 0.5 + 1) = (61, 81).

    Args:
        bounds: The size of map in the world (shape: (n, 2) (n>=2)). bounds[i, 0] means the lower bound of the world in the i-th dimension. bounds[i, 1] means the upper bound of the world in the i-th dimension.
        resolution: resolution of the grid map
        type_map: initial type map of the grid map (its shape must be the same as the converted grid map shape, and its dtype must be int)
        dtype: data type of coordinates (must be int)
        inflation_radius: radius of the inflation

    Examples:
        >>> grid_map = Grid(bounds=[[0, 51], [0, 31]], resolution=0.5)
        >>> grid_map
        Grid(bounds=[[ 0. 51.]
         [ 0. 31.]], resolution=0.5)

        >>> grid_map.bounds    # bounds of the base world
        array([[ 0., 51.],
               [ 0., 31.]])

        >>> grid_map.dim
        2

        >>> grid_map.resolution
        0.5

        >>> grid_map.shape   # shape of the grid map
        (102, 62)

        >>> grid_map.dtype
        <class 'numpy.int32'>

        >>> grid_map.type_map
        GridTypeMap(array(
        [[0 0 0 ... 0 0 0]
         [0 0 0 ... 0 0 0]
         [0 0 0 ... 0 0 0]
         ...
         [0 0 0 ... 0 0 0]
         [0 0 0 ... 0 0 0]
         [0 0 0 ... 0 0 0]]
        ), shape=(102, 62), dtype=int8)

        >>> grid_map.map_to_world((1, 2))
        (0.75, 1.25)

        >>> grid_map.world_to_map((0.5, 1.0))
        (0, 2)

        >>> grid_map.get_neighbors(Node((1, 2)))
        [Node((0, 1), (1, 2), 0, 0), Node((0, 2), (1, 2), 0, 0), Node((0, 3), (1, 2), 0, 0), Node((1, 1), (1, 2), 0, 0), Node((1, 3), (1, 2), 0, 0), Node((2, 1), (1, 2), 0, 0), Node((2, 2), (1, 2), 0, 0), Node((2, 3), (1, 2), 0, 0)]

        >>> grid_map.get_neighbors(Node((1, 2)), diagonal=False)
        [Node((2, 2), (1, 2), 0, 0), Node((0, 2), (1, 2), 0, 0), Node((1, 3), (1, 2), 0, 0), Node((1, 1), (1, 2), 0, 0)]

        >>> grid_map.type_map[1, 0] = TYPES.OBSTACLE     # place an obstacle
        >>> grid_map.get_neighbors(Node((0, 0)))    # limited within the bounds
        [Node((0, 1), (0, 0), 0, 0), Node((1, 0), (0, 0), 0, 0), Node((1, 1), (0, 0), 0, 0)]

        >>> grid_map.get_neighbors(Node((grid_map.shape[0] - 1, grid_map.shape[1] - 1)), diagonal=False)  # limited within the boundss
        [Node((100, 61), (101, 61), 0, 0), Node((101, 60), (101, 61), 0, 0)]

        >>> grid_map.line_of_sight((1, 2), (3, 6))
        [(1, 2), (1, 3), (2, 4), (2, 5), (3, 6)]

        >>> grid_map.line_of_sight((1, 2), (1, 2))
        [(1, 2)]

        >>> grid_map.in_collision((1, 2), (3, 6))
        False

        >>> grid_map.type_map[1, 3] = TYPES.OBSTACLE
        >>> grid_map.update_esdf()
        >>> grid_map.in_collision((1, 2), (3, 6))
        True
    """
    def __init__(self, 
                bounds: Iterable = [[0, 30], [0, 40]], 
                resolution: float = 1.0, 
                type_map: Union[GridTypeMap, np.ndarray] = None, 
                dtype: np.dtype = np.int32,
                inflation_radius: float = 0.0,
                ) -> None:
        super().__init__(bounds, dtype)

        self._dtype_options = [np.int8, np.int16, np.int32, np.int64]
        if self._dtype not in self._dtype_options:
            raise ValueError("Dtype must be one of {} instead of {}".format(self._dtype_options, self._dtype))

        self._resolution = resolution
        self._shape = tuple([int((self.bounds[i, 1] - self.bounds[i, 0]) / self.resolution) for i in range(self.dim)])

        if type_map is None:
            self.type_map = GridTypeMap(np.zeros(self._shape, dtype=np.int8))
        else:
            if type_map.shape != self._shape:
                raise ValueError("Shape must be {} instead of {}".format(self._shape, type_map.shape))
            if type_map.dtype not in self._dtype_options:
                raise ValueError("Dtype must be one of {} instead of {}".format(self._dtype_options, type_map.dtype))

            if isinstance(type_map, GridTypeMap):
                self.type_map = type_map
            elif isinstance(type_map, np.ndarray):
                self.type_map = GridTypeMap(type_map)        
            else:
                raise ValueError("Type map must be GridTypeMap or numpy.ndarray instead of {}".format(type(type_map)))

        self._precompute_offsets()

        self._esdf = np.zeros(self._shape, dtype=np.float32)
        # self.update_esdf()    # updated in self.inflate_obstacles()

        self.inflation_radius = inflation_radius
        if self.inflation_radius >= 1:
            self.inflate_obstacles(self.inflation_radius)

    def __str__(self) -> str:
        return "Grid(bounds={}, resolution={})".format(self.bounds, self.resolution)

    def __repr__(self) -> str:
        return self.__str__()

    @property
    def resolution(self) -> float:
        return self._resolution

    @property
    def shape(self) -> tuple:
        return self._shape

    @property
    def esdf(self) -> np.ndarray:
        return self._esdf

    def map_to_world(self, point: Tuple[int, ...]) -> tuple:
        """
        Convert map coordinates to world coordinates.

        Args:
            point: Point in map coordinates.

        Returns:
            point: Point in world coordinates.
        """
        if len(point) != self.dim:
            raise ValueError("Point dimension does not match map dimension.")

        return tuple((x + 0.5) * self.resolution + float(self.bounds[i, 0]) for i, x in enumerate(point))

    def world_to_map(self, point: Tuple[float, ...]) -> tuple:
        """
        Convert world coordinates to map coordinates.

        Args:
            point: Point in world coordinates.

        Returns:
            point: Point in map coordinates.
        """
        if len(point) != self.dim:
            raise ValueError("Point dimension does not match map dimension.")

        return tuple(round((x - float(self.bounds[i, 0])) * (1.0 / self.resolution) - 0.5) for i, x in enumerate(point))

    def get_distance(self, p1: Tuple[int, int], p2: Tuple[int, int]) -> float:
        """
        Get the distance between two points.

        Args:
            p1: Start point.
            p2: Goal point.

        Returns:
            dist: Distance between two points.
        """
        return Geometry.dist(p1, p2, type='Euclidean')

    def within_bounds(self, point: Tuple[int, ...]) -> bool:
        """
        Check if a point is within the bounds of the grid map.

        Args:
            point: Point to check.

        Returns:
            bool: True if the point is within the bounds of the map, False otherwise.
        """
        # if point.dim != self.dim:
        #     raise ValueError("Point dimension does not match map dimension.")

        # return all(0 <= point[i] < self.shape[i] for i in range(self.dim))
        dim = self.dim
        shape = self.shape

        for i in range(dim):
            if not (0 <= point[i] < shape[i]):
                return False
        return True

    def is_expandable(self, point: Tuple[int, ...], src_point: Tuple[int, ...] = None) -> bool:
        """
        Check if a point is expandable.

        Args:
            point: Point to check.
            src_point: Source point.

        Returns:
            expandable: True if the point is expandable, False otherwise.
        """
        if not self.within_bounds(point):
            return False
        if src_point is not None:
            if self._esdf[point] >= self._esdf[src_point]:
                return True

        return not self.type_map[point] == TYPES.OBSTACLE and not self.type_map[point] == TYPES.INFLATION

    def get_neighbors(self, 
                    node: Node, 
                    diagonal: bool = True
                    ) -> list:
        """
        Get neighbor nodes of a given node.

        Args:
            node: Node to get neighbor nodes.
            diagonal: Whether to include diagonal neighbors.

        Returns:
            nodes: List of neighbor nodes.
        """
        if node.dim != self.dim:
            raise ValueError("Node dimension does not match map dimension.")

        # current_point = node.current.astype(self.dtype)
        # current_pos = current_point.numpy()
        # neighbors = []

        offsets = self._diagonal_offsets if diagonal else self._orthogonal_offsets

        # Generate all neighbor positions
        # neighbor_positions = current_pos + offsets
        neighbors = [node + offset for offset in offsets]
        filtered_neighbors = []

        # print(neighbors)

        # Filter out positions outside map bounds
        # for pos in neighbor_positions:
        #     point = (pos, dtype=self.dtype)
        #     if self.within_bounds(point):
        #         if self.type_map[tuple(point)] != TYPES.OBSTACLE:
        #             neighbor_node = Node(point, parent=current_point)
        #             neighbors.append(neighbor_node)
        for neighbor in neighbors:
            if self.is_expandable(neighbor.current, node.current):
                filtered_neighbors.append(neighbor)

        # print(filtered_neighbors)

        return filtered_neighbors

    def line_of_sight(self, p1: Tuple[int, ...], p2: Tuple[int, ...]) -> List[Tuple[int, ...]]:
        """
        N-dimensional line of sight (Bresenham's line algorithm)

        Args:
            p1: Start point of the line.
            p2: End point of the line.

        Returns:
            points: List of point on the line of sight.
        """
        p1 = np.array(p1)
        p2 = np.array(p2)

        dim = len(p1)
        delta = p2 - p1
        abs_delta = np.abs(delta)

        # Determine the main direction axis (the dimension with the greatest change)
        primary_axis = np.argmax(abs_delta)
        primary_step = 1 if delta[primary_axis] > 0 else -1

        # Initialize the error variable
        error = np.zeros(dim, dtype=self.dtype)
        delta2 = 2 * abs_delta

        # Calculate the number of steps and initialize the current point
        steps = abs_delta[primary_axis]
        current = p1

        # Allocate the result array
        result = []
        result.append(tuple(int(x) for x in current))

        for i in range(1, steps + 1):
            current[primary_axis] += primary_step

            # Update the error for the primary dimension
            for d in range(dim):
                if d == primary_axis:
                    continue

                error[d] += delta2[d]
                if error[d] > abs_delta[primary_axis]:
                    current[d] += 1 if delta[d] > 0 else -1
                    error[d] -= delta2[primary_axis]

            result.append(tuple(int(x) for x in current))

        return result

    def in_collision(self, p1: Tuple[int, ...], p2: Tuple[int, ...]) -> bool:
        """
        Check if the line of sight between two points is in collision.

        Args:
            p1: Start point of the line.
            p2: End point of the line.

        Returns:
            in_collision: True if the line of sight is in collision, False otherwise.
        """
        if not self.is_expandable(p1) or not self.is_expandable(p2, p1):
            return True

        # Corner Case: Start and end points are the same
        if p1 == p2:
            return False

        p1 = np.array(p1)
        p2 = np.array(p2)

        # Calculate delta and absolute delta
        delta = p2 - p1
        abs_delta = np.abs(delta)

        # Determine the primary axis (the dimension with the greatest change)
        primary_axis = np.argmax(abs_delta)
        primary_step = 1 if delta[primary_axis] > 0 else -1

        # Initialize the error variable
        error = np.zeros_like(delta, dtype=np.int32)
        delta2 = 2 * abs_delta

        # calculate the number of steps and initialize the current point
        steps = abs_delta[primary_axis]
        current = p1

        for _ in range(steps):
            last_point = current.copy()
            current[primary_axis] += primary_step

            # Update the error for the primary dimension
            for d in range(len(delta)):
                if d == primary_axis:
                    continue

                error[d] += delta2[d]
                if error[d] > abs_delta[primary_axis]:
                    current[d] += 1 if delta[d] > 0 else -1
                    error[d] -= delta2[primary_axis]

            # Check the current point
            if not self.is_expandable(tuple(current), tuple(last_point)):
                return True

        return False

    def fill_boundary_with_obstacles(self) -> None:
        """
        Fill the boundary of the map with obstacles.
        """
        for d in range(self.dim):
            # Create a tuple of slice objects to select boundary elements in current dimension
            # First boundary (start index)
            slices_start = [slice(None)] * self.dim
            slices_start[d] = 0
            self.type_map[tuple(slices_start)] = TYPES.OBSTACLE

            # Last boundary (end index)
            slices_end = [slice(None)] * self.dim
            slices_end[d] = -1
            self.type_map[tuple(slices_end)] = TYPES.OBSTACLE

    def inflate_obstacles(self, radius: float = 1.0) -> None:
        """
        Inflate the obstacles in the map.

        Args:
            radius: Radius of the inflation.
        """
        self.update_esdf()
        mask = (self.esdf <= radius) & (self.type_map.array == TYPES.FREE)
        self.type_map[mask] = TYPES.INFLATION
        self.inflation_radius = radius

    def fill_expands(self, expands: Dict[Tuple[int, int], Node]) -> None:
        """
        Fill the expands in the map.

        Args:
            expands: List of expands.
        """
        for expand in expands.keys():
            if self.type_map[expand] != TYPES.FREE:
                continue
            self.type_map[expand] = TYPES.EXPAND

    def update_esdf(self) -> None:
        """
        Update the ESDF (signed Euclidean Distance Field) based on the obstacles in the map.
        - Obstacle grid ESDF = 0
        - Free grid ESDF > 0. The value is the distance to the nearest obstacle
        """
        obstacle_mask = (self.type_map.array == TYPES.OBSTACLE)
        free_mask = ~obstacle_mask

        # distance to obstacles
        dist_outside = ndimage.distance_transform_edt(free_mask, sampling=self.resolution)
        # distance to free space (internal distance of obstacles)
        dist_inside = ndimage.distance_transform_edt(obstacle_mask, sampling=self.resolution)

        self._esdf = dist_outside.astype(np.float32)
        self._esdf[obstacle_mask] = -dist_inside[obstacle_mask]

    def path_map_to_world(self, path: List[Tuple[int, int]]) -> List[Tuple[float, float]]:
        """
        Convert path from map coordinates to world coordinates

        Args:
            path: a list of map coordinates

        Returns:
            path: a list of world coordinates
        """
        return [self.map_to_world(p) for p in path]

    def path_world_to_map(self, path: List[Tuple[float, float]]) -> List[Tuple[int, int]]:
        """
        Convert path from world coordinates to map coordinates

        Args:
            path: a list of world coordinates

        Returns:
            path: a list of map coordinates
        """
        return [self.world_to_map(p) for p in path]

    def _precompute_offsets(self):
        # Generate all possible offsets (-1, 0, +1) in each dimension
        self._diagonal_offsets = np.array(np.meshgrid(*[[-1, 0, 1]]*self.dim), dtype=self.dtype).T.reshape(-1, self.dim)
        # Remove the zero offset (current node itself)
        self._diagonal_offsets = self._diagonal_offsets[np.any(self._diagonal_offsets != 0, axis=1)]
        # self._diagonal_offsets = [Node((offset.tolist(), dtype=self.dtype)) for offset in self._diagonal_offsets]
        self._diagonal_offsets = [Node(tuple(offset.tolist())) for offset in self._diagonal_offsets]

        # Generate only orthogonal offsets (one dimension changes by ±1)
        self._orthogonal_offsets = np.zeros((2*self.dim, self.dim), dtype=self.dtype)
        for d in range(self.dim):
            self._orthogonal_offsets[2*d, d] = 1
            self._orthogonal_offsets[2*d+1, d] = -1
        # self._orthogonal_offsets = [Node((offset.tolist(), dtype=self.dtype)) for offset in self._orthogonal_offsets]
        self._orthogonal_offsets = [Node(tuple(offset.tolist())) for offset in self._orthogonal_offsets]

`fill_boundary_with_obstacles()` ¶

Fill the boundary of the map with obstacles.

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def fill_boundary_with_obstacles(self) -> None:
    """
    Fill the boundary of the map with obstacles.
    """
    for d in range(self.dim):
        # Create a tuple of slice objects to select boundary elements in current dimension
        # First boundary (start index)
        slices_start = [slice(None)] * self.dim
        slices_start[d] = 0
        self.type_map[tuple(slices_start)] = TYPES.OBSTACLE

        # Last boundary (end index)
        slices_end = [slice(None)] * self.dim
        slices_end[d] = -1
        self.type_map[tuple(slices_end)] = TYPES.OBSTACLE

`fill_expands(expands)` ¶

Fill the expands in the map.

Parameters:

Name	Type	Description	Default
`expands`	`Dict[Tuple[int, int], Node]`	List of expands.	required

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def fill_expands(self, expands: Dict[Tuple[int, int], Node]) -> None:
    """
    Fill the expands in the map.

    Args:
        expands: List of expands.
    """
    for expand in expands.keys():
        if self.type_map[expand] != TYPES.FREE:
            continue
        self.type_map[expand] = TYPES.EXPAND

`get_distance(p1, p2)` ¶

Get the distance between two points.

Parameters:

Name	Type	Description	Default
`p1`	`Tuple[int, int]`	Start point.	required
`p2`	`Tuple[int, int]`	Goal point.	required

Returns:

Name	Type	Description
`dist`	`float`	Distance between two points.

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def get_distance(self, p1: Tuple[int, int], p2: Tuple[int, int]) -> float:
    """
    Get the distance between two points.

    Args:
        p1: Start point.
        p2: Goal point.

    Returns:
        dist: Distance between two points.
    """
    return Geometry.dist(p1, p2, type='Euclidean')

`get_neighbors(node, diagonal=True)` ¶

Get neighbor nodes of a given node.

Parameters:

Name	Type	Description	Default
`node`	`Node`	Node to get neighbor nodes.	required
`diagonal`	`bool`	Whether to include diagonal neighbors.	`True`

Returns:

Name	Type	Description
`nodes`	`list`	List of neighbor nodes.

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def get_neighbors(self, 
                node: Node, 
                diagonal: bool = True
                ) -> list:
    """
    Get neighbor nodes of a given node.

    Args:
        node: Node to get neighbor nodes.
        diagonal: Whether to include diagonal neighbors.

    Returns:
        nodes: List of neighbor nodes.
    """
    if node.dim != self.dim:
        raise ValueError("Node dimension does not match map dimension.")

    # current_point = node.current.astype(self.dtype)
    # current_pos = current_point.numpy()
    # neighbors = []

    offsets = self._diagonal_offsets if diagonal else self._orthogonal_offsets

    # Generate all neighbor positions
    # neighbor_positions = current_pos + offsets
    neighbors = [node + offset for offset in offsets]
    filtered_neighbors = []

    # print(neighbors)

    # Filter out positions outside map bounds
    # for pos in neighbor_positions:
    #     point = (pos, dtype=self.dtype)
    #     if self.within_bounds(point):
    #         if self.type_map[tuple(point)] != TYPES.OBSTACLE:
    #             neighbor_node = Node(point, parent=current_point)
    #             neighbors.append(neighbor_node)
    for neighbor in neighbors:
        if self.is_expandable(neighbor.current, node.current):
            filtered_neighbors.append(neighbor)

    # print(filtered_neighbors)

    return filtered_neighbors

`in_collision(p1, p2)` ¶

Check if the line of sight between two points is in collision.

Parameters:

Name	Type	Description	Default
`p1`	`Tuple[int, ...]`	Start point of the line.	required
`p2`	`Tuple[int, ...]`	End point of the line.	required

Returns:

Name	Type	Description
`in_collision`	`bool`	True if the line of sight is in collision, False otherwise.

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def in_collision(self, p1: Tuple[int, ...], p2: Tuple[int, ...]) -> bool:
    """
    Check if the line of sight between two points is in collision.

    Args:
        p1: Start point of the line.
        p2: End point of the line.

    Returns:
        in_collision: True if the line of sight is in collision, False otherwise.
    """
    if not self.is_expandable(p1) or not self.is_expandable(p2, p1):
        return True

    # Corner Case: Start and end points are the same
    if p1 == p2:
        return False

    p1 = np.array(p1)
    p2 = np.array(p2)

    # Calculate delta and absolute delta
    delta = p2 - p1
    abs_delta = np.abs(delta)

    # Determine the primary axis (the dimension with the greatest change)
    primary_axis = np.argmax(abs_delta)
    primary_step = 1 if delta[primary_axis] > 0 else -1

    # Initialize the error variable
    error = np.zeros_like(delta, dtype=np.int32)
    delta2 = 2 * abs_delta

    # calculate the number of steps and initialize the current point
    steps = abs_delta[primary_axis]
    current = p1

    for _ in range(steps):
        last_point = current.copy()
        current[primary_axis] += primary_step

        # Update the error for the primary dimension
        for d in range(len(delta)):
            if d == primary_axis:
                continue

            error[d] += delta2[d]
            if error[d] > abs_delta[primary_axis]:
                current[d] += 1 if delta[d] > 0 else -1
                error[d] -= delta2[primary_axis]

        # Check the current point
        if not self.is_expandable(tuple(current), tuple(last_point)):
            return True

    return False

`inflate_obstacles(radius=1.0)` ¶

Inflate the obstacles in the map.

Parameters:

Name	Type	Description	Default
`radius`	`float`	Radius of the inflation.	`1.0`

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def inflate_obstacles(self, radius: float = 1.0) -> None:
    """
    Inflate the obstacles in the map.

    Args:
        radius: Radius of the inflation.
    """
    self.update_esdf()
    mask = (self.esdf <= radius) & (self.type_map.array == TYPES.FREE)
    self.type_map[mask] = TYPES.INFLATION
    self.inflation_radius = radius

`is_expandable(point, src_point=None)` ¶

Check if a point is expandable.

Parameters:

Name	Type	Description	Default
`point`	`Tuple[int, ...]`	Point to check.	required
`src_point`	`Tuple[int, ...]`	Source point.	`None`

Returns:

Name	Type	Description
`expandable`	`bool`	True if the point is expandable, False otherwise.

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def is_expandable(self, point: Tuple[int, ...], src_point: Tuple[int, ...] = None) -> bool:
    """
    Check if a point is expandable.

    Args:
        point: Point to check.
        src_point: Source point.

    Returns:
        expandable: True if the point is expandable, False otherwise.
    """
    if not self.within_bounds(point):
        return False
    if src_point is not None:
        if self._esdf[point] >= self._esdf[src_point]:
            return True

    return not self.type_map[point] == TYPES.OBSTACLE and not self.type_map[point] == TYPES.INFLATION

`line_of_sight(p1, p2)` ¶

N-dimensional line of sight (Bresenham's line algorithm)

Parameters:

Name	Type	Description	Default
`p1`	`Tuple[int, ...]`	Start point of the line.	required
`p2`	`Tuple[int, ...]`	End point of the line.	required

Returns:

Name	Type	Description
`points`	`List[Tuple[int, ...]]`	List of point on the line of sight.

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def line_of_sight(self, p1: Tuple[int, ...], p2: Tuple[int, ...]) -> List[Tuple[int, ...]]:
    """
    N-dimensional line of sight (Bresenham's line algorithm)

    Args:
        p1: Start point of the line.
        p2: End point of the line.

    Returns:
        points: List of point on the line of sight.
    """
    p1 = np.array(p1)
    p2 = np.array(p2)

    dim = len(p1)
    delta = p2 - p1
    abs_delta = np.abs(delta)

    # Determine the main direction axis (the dimension with the greatest change)
    primary_axis = np.argmax(abs_delta)
    primary_step = 1 if delta[primary_axis] > 0 else -1

    # Initialize the error variable
    error = np.zeros(dim, dtype=self.dtype)
    delta2 = 2 * abs_delta

    # Calculate the number of steps and initialize the current point
    steps = abs_delta[primary_axis]
    current = p1

    # Allocate the result array
    result = []
    result.append(tuple(int(x) for x in current))

    for i in range(1, steps + 1):
        current[primary_axis] += primary_step

        # Update the error for the primary dimension
        for d in range(dim):
            if d == primary_axis:
                continue

            error[d] += delta2[d]
            if error[d] > abs_delta[primary_axis]:
                current[d] += 1 if delta[d] > 0 else -1
                error[d] -= delta2[primary_axis]

        result.append(tuple(int(x) for x in current))

    return result

`map_to_world(point)` ¶

Convert map coordinates to world coordinates.

Parameters:

Name	Type	Description	Default
`point`	`Tuple[int, ...]`	Point in map coordinates.	required

Returns:

Name	Type	Description
`point`	`tuple`	Point in world coordinates.

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def map_to_world(self, point: Tuple[int, ...]) -> tuple:
    """
    Convert map coordinates to world coordinates.

    Args:
        point: Point in map coordinates.

    Returns:
        point: Point in world coordinates.
    """
    if len(point) != self.dim:
        raise ValueError("Point dimension does not match map dimension.")

    return tuple((x + 0.5) * self.resolution + float(self.bounds[i, 0]) for i, x in enumerate(point))

`path_map_to_world(path)` ¶

Convert path from map coordinates to world coordinates

Parameters:

Name	Type	Description	Default
`path`	`List[Tuple[int, int]]`	a list of map coordinates	required

Returns:

Name	Type	Description
`path`	`List[Tuple[float, float]]`	a list of world coordinates

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def path_map_to_world(self, path: List[Tuple[int, int]]) -> List[Tuple[float, float]]:
    """
    Convert path from map coordinates to world coordinates

    Args:
        path: a list of map coordinates

    Returns:
        path: a list of world coordinates
    """
    return [self.map_to_world(p) for p in path]

`path_world_to_map(path)` ¶

Convert path from world coordinates to map coordinates

Parameters:

Name	Type	Description	Default
`path`	`List[Tuple[float, float]]`	a list of world coordinates	required

Returns:

Name	Type	Description
`path`	`List[Tuple[int, int]]`	a list of map coordinates

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def path_world_to_map(self, path: List[Tuple[float, float]]) -> List[Tuple[int, int]]:
    """
    Convert path from world coordinates to map coordinates

    Args:
        path: a list of world coordinates

    Returns:
        path: a list of map coordinates
    """
    return [self.world_to_map(p) for p in path]

`update_esdf()` ¶

Update the ESDF (signed Euclidean Distance Field) based on the obstacles in the map. - Obstacle grid ESDF = 0 - Free grid ESDF > 0. The value is the distance to the nearest obstacle

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def update_esdf(self) -> None:
    """
    Update the ESDF (signed Euclidean Distance Field) based on the obstacles in the map.
    - Obstacle grid ESDF = 0
    - Free grid ESDF > 0. The value is the distance to the nearest obstacle
    """
    obstacle_mask = (self.type_map.array == TYPES.OBSTACLE)
    free_mask = ~obstacle_mask

    # distance to obstacles
    dist_outside = ndimage.distance_transform_edt(free_mask, sampling=self.resolution)
    # distance to free space (internal distance of obstacles)
    dist_inside = ndimage.distance_transform_edt(obstacle_mask, sampling=self.resolution)

    self._esdf = dist_outside.astype(np.float32)
    self._esdf[obstacle_mask] = -dist_inside[obstacle_mask]

`within_bounds(point)` ¶

Check if a point is within the bounds of the grid map.

Parameters:

Name	Type	Description	Default
`point`	`Tuple[int, ...]`	Point to check.	required

Returns:

Name	Type	Description
`bool`	`bool`	True if the point is within the bounds of the map, False otherwise.

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def within_bounds(self, point: Tuple[int, ...]) -> bool:
    """
    Check if a point is within the bounds of the grid map.

    Args:
        point: Point to check.

    Returns:
        bool: True if the point is within the bounds of the map, False otherwise.
    """
    # if point.dim != self.dim:
    #     raise ValueError("Point dimension does not match map dimension.")

    # return all(0 <= point[i] < self.shape[i] for i in range(self.dim))
    dim = self.dim
    shape = self.shape

    for i in range(dim):
        if not (0 <= point[i] < shape[i]):
            return False
    return True

`world_to_map(point)` ¶

Convert world coordinates to map coordinates.

Parameters:

Name	Type	Description	Default
`point`	`Tuple[float, ...]`	Point in world coordinates.	required

Returns:

Name	Type	Description
`point`	`tuple`	Point in map coordinates.

Source code in src\python_motion_planning\common\env\map\grid.py

Python

def world_to_map(self, point: Tuple[float, ...]) -> tuple:
    """
    Convert world coordinates to map coordinates.

    Args:
        point: Point in world coordinates.

    Returns:
        point: Point in map coordinates.
    """
    if len(point) != self.dim:
        raise ValueError("Point dimension does not match map dimension.")

    return tuple(round((x - float(self.bounds[i, 0])) * (1.0 / self.resolution) - 0.5) for i, x in enumerate(point))

Grid¶

src.python_motion_planning.common.env.map.grid.Grid ¶

fill_boundary_with_obstacles() ¶

fill_expands(expands) ¶

get_distance(p1, p2) ¶

get_neighbors(node, diagonal=True) ¶

in_collision(p1, p2) ¶

inflate_obstacles(radius=1.0) ¶

is_expandable(point, src_point=None) ¶

line_of_sight(p1, p2) ¶

map_to_world(point) ¶

path_map_to_world(path) ¶

path_world_to_map(path) ¶

update_esdf() ¶

within_bounds(point) ¶

world_to_map(point) ¶

`src.python_motion_planning.common.env.map.grid.Grid` ¶

`fill_boundary_with_obstacles()` ¶

`fill_expands(expands)` ¶

`get_distance(p1, p2)` ¶

`get_neighbors(node, diagonal=True)` ¶

`in_collision(p1, p2)` ¶

`inflate_obstacles(radius=1.0)` ¶

`is_expandable(point, src_point=None)` ¶

`line_of_sight(p1, p2)` ¶

`map_to_world(point)` ¶

`path_map_to_world(path)` ¶

`path_world_to_map(path)` ¶

`update_esdf()` ¶

`within_bounds(point)` ¶

`world_to_map(point)` ¶