Smac State Lattice Planner

Paths generated by the Smac State Lattice

<name> is the corresponding planner plugin ID selected for this type.

Note: State Lattice does not have the costmap downsampler due to the minimum control sets being tied with map resolutions on generation. The minimum turning radius is also not a parameter in State Lattice since this was specified at the minimum control set pre-computation phase. See the Smac Planner package to generate custom control sets for your vehicle or use one of our pre-generated examples.

The image above you can see the reverse expansion enabled, such that the robot can back into a tight requested spot close to an obstacle.

Parameters

<name>.allow_unknown

Type

Default

bool

True

Description

Whether to allow traversing/search in unknown space.

<name>.tolerance

Type

Default

double

0.25

Description

If an exact path cannot be found, the tolerance (as measured by the heuristic cost-to-goal) that would be acceptable to diverge from the requested pose in distance-to-goal.

<name>.max_iterations

Type

Default

int

1000000

Description

Maximum number of search iterations before failing to limit compute time, disabled by -1.

<name>.max_on_approach_iterations

Type

Default

int

1000

Description

Maximum number of iterations once a visited node is within the goal tolerances to continue to try to find an exact match before returning the best path solution within tolerances.

<name>.max_planning_time

Type

Default

double

5.0

Description

Maximum planning time in seconds.

<name>.analytic_expansion_ratio

Type

Default

double

3.5

Description

SE2 node will attempt to complete an analytic expansion with frequency proportional to this value and the minimum heuristic. Negative values convert to infinite.

<name>.analytic_expansion_max_length

Type

Default

double

3.0

Description

If the length is too far, reject this expansion. This prevents shortcutting of search with its penalty functions far out from the goal itself (e.g. so we don’t reverse half-way across open maps or cut through high cost zones). This should never be smaller than 4-5x the minimum turning radius being used, or planning times will begin to spike.

<name>.reverse_penalty

Type

Default

double

2.0

Description

Heuristic penalty to apply to SE2 node if searching in reverse direction. Only used in allow_reverse_expansion = true.

<name>.change_penalty

Type

Default

double

0.05

Description

Heuristic penalty to apply to SE2 node if changing direction (e.g. left to right) in search.

<name>.non_straight_penalty

Type

Default

double

1.05

Description

Heuristic penalty to apply to SE2 node if searching in non-straight direction.

<name>.cost_penalty

Type

Default

double

2.0

Description

Heuristic penalty to apply to SE2 node for cost at pose. Allows State Lattice to be cost aware.

<name>.rotation_penalty

Type

Default

double

5.0

Description

Penalty to apply for rotations in place, if minimum control set contains in-place rotations. This should always be set sufficiently high to weight against in-place rotations unless strictly necessary for obstacle avoidance or there may be frequent discontinuities in the plan where the plan requests the robot to rotate in place to short-cut an otherwise smooth forward-moving path for marginal path distance savings.

<name>.retrospective_penalty

Type

Default

double

0.015

Description

Heuristic penalty to apply to SE2 node penalty. Causes State Lattice to prefer later maneuvers before earlier ones along the path. Saves search time since earlier (shorter) branches are not expanded until it is necessary. Must be >= 0.0 and <= 1.0. Must be 0.0 to be fully admissible.

<name>.lattice_filepath

Type

Default

string

“”

Description

The filepath to the state lattice minimum control set graph, this will default to a 16 bin, 0.4m turning radius control set located in test/ for basic testing and evaluation.

<name>.lookup_table_size

Type

Default

double

20.0

Description

Size of the dubin/reeds-sheep distance window to cache, in meters.

<name>.cache_obstacle_heuristic

Type

Default

bool

false

Description

Cache the obstacle map dynamic programming distance expansion heuristic between subsiquent replannings of the same goal location. Dramatically speeds up replanning performance (40x) if costmap is largely static.

<name>.allow_reverse_expansion

Type

Default

bool

false

Description

If true, allows the robot to use the primitives to expand in the mirrored opposite direction of the current robot’s orientation (to reverse).

<name>.smooth_path

Type

Default

bool

true

Description

If true, does simple and fast smoothing post-processing to the path from search

<name>.smoother.max_iterations

Type

Default

int

1000

Description

The maximum number of iterations the smoother has to smooth the path, to bound potential computation.

<name>.smoother.w_smooth

Type

Default

double

0.3

Description

Weight for smoother to apply to smooth out the data points

<name>.smoother.w_data

Type

Default

double

0.2

Description

Weight for smoother to apply to retain original data information

<name>.smoother.tolerance

Type

Default

double

1e-10

Description

Parameter tolerance change amount to terminate smoothing session

<name>.smoother.do_refinement

Type

Default

bool

true

Description

Performs extra refinement smoothing runs. Essentially, this recursively calls the smoother using the output from the last smoothing cycle to further smooth the path for macro-trends. This typically improves quality especially in the Hybrid-A* planner but can be helpful on the state lattice planner to reduce the “blocky” movements in State Lattice caused by the limited number of headings.

Example

planner_server:
  ros__parameters:
    planner_plugins: ["GridBased"]
    use_sim_time: True

    GridBased:
      plugin: "nav2_smac_planner/SmacPlannerLattice"
      allow_unknown: true                 # Allow traveling in unknown space
      tolerance: 0.25                     # dist-to-goal heuristic cost (distance) for valid tolerance endpoints if exact goal cannot be found.
      max_iterations: 1000000             # Maximum total iterations to search for before failing (in case unreachable), set to -1 to disable
      max_on_approach_iterations: 1000    # Maximum number of iterations after within tolerances to continue to try to find exact solution
      max_planning_time: 5.0              # Max time in s for planner to plan, smooth
      analytic_expansion_ratio: 3.5       # The ratio to attempt analytic expansions during search for final approach.
      analytic_expansion_max_length: 3.0  # For Hybrid/Lattice nodes: The maximum length of the analytic expansion to be considered valid to prevent unsafe shortcutting
      reverse_penalty: 2.0                # Penalty to apply if motion is reversing, must be => 1
      change_penalty: 0.05                # Penalty to apply if motion is changing directions (L to R), must be >= 0
      non_straight_penalty: 1.05          # Penalty to apply if motion is non-straight, must be => 1
      cost_penalty: 2.0                   # Penalty to apply to higher cost areas when adding into the obstacle map dynamic programming distance expansion heuristic. This drives the robot more towards the center of passages. A value between 1.3 - 3.5 is reasonable.
      rotation_penalty: 5.0               # Penalty to apply to in-place rotations, if minimum control set contains them
      retrospective_penalty: 0.015
      lattice_filepath: ""                # The filepath to the state lattice graph
      lookup_table_size: 20.0             # Size of the dubin/reeds-sheep distance window to cache, in meters.
      cache_obstacle_heuristic: false     # Cache the obstacle map dynamic programming distance expansion heuristic between subsiquent replannings of the same goal location. Dramatically speeds up replanning performance (40x) if costmap is largely static.
      allow_reverse_expansion: false      # If true, allows the robot to use the primitives to expand in the mirrored opposite direction of the current robot's orientation (to reverse).
      smooth_path: True                   # If true, does a simple and quick smoothing post-processing to the path
      smoother:
        max_iterations: 1000
        w_smooth: 0.3
        w_data: 0.2
        tolerance: 1.0e-10
        do_refinement: true