# class

PathFinderLoads and/or builds a navigation mesh and then allows point sampling, path finding, collision, and island queries on that navmesh. See PathFinder C++ API docs for more details.

## Methods

- Returns an array of triangle index data for the triangulated NavMesh poly vertices returned by build_navmesh_vertices(). Optionally limit results to a specific island. Default (island_index==-1) queries all islands.
- Returns an array of vertex data for the triangulated NavMesh polys. Optionally limit results to a specific island. Default (island_index==-1) queries all islands.
- def closest_obstacle_surface_point(self, pt: numpy.ndarray[numpy.float32[3, 1]], max_search_radius: float = 2.0) -> HitRecord
- Returns the hit_pos <HitRecord.hit_pos>, hit_normal <HitRecord.hit_normal>, and hit_dist <HitRecord.hit_dist> of the surface point on the closest obstacle.
- def distance_to_closest_obstacle(self, pt: numpy.ndarray[numpy.float32[3, 1]], max_search_radius: float = 2.0) -> float
- Returns the distance to the closest obstacle
- def find_path(self, path: ShortestPath) -> bool
- Finds the shortest path between two points on the navigation mesh using ShortestPath module. Path variable is filled if successful. Returns boolean success.
- def find_path(self, path: MultiGoalShortestPath) -> bool
- Finds the shortest path between a start point and the closest of a set of end points (in geodesic distance) on the navigation mesh using MultiGoalShortestPath module. Path variable is filled if successful. Returns boolean success.
- def get_bounds(self, /) -> typing.Tuple[numpy.ndarray[numpy.float32[3, 1]], numpy.ndarray[numpy.float32[3, 1]]]
- Get the axis aligned bounding box containing the navigation mesh.
- def get_island(self, point: magnum.Vector3) -> int
- Query the island closest to a point. Snaps the point to the NavMesh first, so check the snap distance also if unsure.
- def get_island(self, point: numpy.ndarray[numpy.float32[3, 1]]) -> int
- Query the island closest to a point. Snaps the point to the NavMesh first, so check the snap distance also if unsure.
- def get_random_navigable_point(self, max_tries: int = 10, island_index: int = -1) -> numpy.ndarray[numpy.float32[3, 1]]
- Samples a navigable point uniformly at random from the navmesh
- Returns a random navigable point within a specified radius about a given point. Optionally specify the island from which to sample the point. Default -1 queries the full navmesh.
- def get_topdown_view(self, meters_per_pixel: float, height: float) -> numpy.ndarray[bool[m, n]]
- Returns the topdown view of the PathFinder’s navmesh.
- def is_navigable(self, pt: numpy.ndarray[numpy.float32[3, 1]], max_y_delta: float = 0.5) -> bool
- Checks to see if the agent can stand at the specified point
- def island_area(self, island_index: int = -1) -> float
- The total area of all NavMesh polygons within the specified island.
- def island_radius(self, pt: numpy.ndarray[numpy.float32[3, 1]]) -> float
- Given a point, snaps to an islan and gets a heuristic of island size: the radius of a circle containing all NavMesh polygons within the specified island.
- def island_radius(self, island_index: int) -> float
- Given an island index, gets a heuristic of island size: the radius of a circle containing all NavMesh polygons within the specified island.
- Load a .navmesh file overriding this PathFinder instance.
- Serialize this PathFinder instance and current NavMesh settings to a .navmesh file.
- def seed(self, arg0: int, /) -> None
- Seed the pathfinder. Useful for get_random_navigable_point(). Seeds the global c rand function.
- def snap_point(self, point: magnum.Vector3, island_index: int = -1) -> magnum.Vector3
- Snaps a point to the closet navigable location
- def snap_point(self, point: numpy.ndarray[numpy.float32[3, 1]], island_index: int = -1) -> numpy.ndarray[numpy.float32[3, 1]]
- Snaps a point to the closet navigable location
- def try_step(self, start: magnum.Vector3, end: magnum.Vector3) -> magnum.Vector3
- Find a valid location for the agent to actually step to when it attempts to step between start and end
- def try_step(self, start: numpy.ndarray[numpy.float32[3, 1]], end: numpy.ndarray[numpy.float32[3, 1]]) -> numpy.ndarray[numpy.float32[3, 1]]
- Find a valid location for the agent to actually step to when it attempts to step between start and end
- def try_step_no_sliding(self, start: magnum.Vector3, end: magnum.Vector3) -> magnum.Vector3
- def try_step_no_sliding(self, start: numpy.ndarray[numpy.float32[3, 1]], end: numpy.ndarray[numpy.float32[3, 1]]) -> numpy.ndarray[numpy.float32[3, 1]]

## Special methods

## Properties

- is_loaded: bool get
- Whether a valid navigation mesh is currently loaded or not.
- The settings for the current nav mesh
- The total area of all NavMesh polygons.
- num_islands: int get
- The number of connected components making up the navmesh.

## Method documentation

###
def habitat_sim.nav.PathFinder. closest_obstacle_surface_point(self,
pt: numpy.ndarray[numpy.float32[3, 1]],
max_search_radius: float = 2.0) -> HitRecord

Returns the hit_pos <HitRecord.hit_pos>, hit_normal <HitRecord.hit_normal>, and hit_dist <HitRecord.hit_dist> of the surface point on the closest obstacle.

If the returned hit_dist <HitRecord.hit_dist> is equal to
`max_search_radius`

, no obstacle was found.

###
def habitat_sim.nav.PathFinder. distance_to_closest_obstacle(self,
pt: numpy.ndarray[numpy.float32[3, 1]],
max_search_radius: float = 2.0) -> float

Returns the distance to the closest obstacle

If this distance is greater than `max_search_radius`

,
`max_search_radius`

is returned instead.

###
def habitat_sim.nav.PathFinder. snap_point(self,
point: magnum.Vector3,
island_index: int = -1) -> magnum.Vector3

Snaps a point to the closet navigable location

Will only search within a 4x8x4 cube centerred around the point. If there is no navigable location within that cube, no navigable point will be found.

- param point:
- The starting location of the agent
- param island_index:
- Optionally specify the island from which to sample the point. Default -1 queries the full navmesh.
- return:
- The navigable point, if one exists, or
`{NAN, NAN, NAN}`

###
def habitat_sim.nav.PathFinder. snap_point(self,
point: numpy.ndarray[numpy.float32[3, 1]],
island_index: int = -1) -> numpy.ndarray[numpy.float32[3, 1]]

Snaps a point to the closet navigable location

Will only search within a 4x8x4 cube centerred around the point. If there is no navigable location within that cube, no navigable point will be found.

- param point:
- The starting location of the agent
- param island_index:
- Optionally specify the island from which to sample the point. Default -1 queries the full navmesh.
- return:
- The navigable point, if one exists, or
`{NAN, NAN, NAN}`

###
def habitat_sim.nav.PathFinder. try_step(self,
start: magnum.Vector3,
end: magnum.Vector3) -> magnum.Vector3

Find a valid location for the agent to actually step to when it attempts to step between start and end

Attempts to move from start to end and returns the navigable point closest to end that is feasibly reachable from start.

- param start:
- The starting location of the agent
- param end:
- The desired end location
- return:
- The actual ending location, if such a location exists, or
`{NAN, NAN, NAN}`

###
def habitat_sim.nav.PathFinder. try_step(self,
start: numpy.ndarray[numpy.float32[3, 1]],
end: numpy.ndarray[numpy.float32[3, 1]]) -> numpy.ndarray[numpy.float32[3, 1]]

Attempts to move from start to end and returns the navigable point closest to end that is feasibly reachable from start.

- param start:
- The starting location of the agent
- param end:
- The desired end location
- return:
- The actual ending location, if such a location exists, or
`{NAN, NAN, NAN}`