Smac Planner Hybrid A Star

name: smac-planner-hybrid-a-star description: 'Configure Nav2 SMAC Hybrid-A* planner. Use when planning kinematically feasible paths with minimum turning radius constraints.'

SMAC Hybrid-A* Planner

Plugin

nav2_smac_planner::SmacPlannerHybridAstar

Algorithm

Hybrid-A* extends A* into the SE2 state space (x, y, theta), producing paths that respect non-holonomic motion constraints. The search graph discretizes heading into angular bins and expands nodes using Dubins or Reeds-Shepp motion primitives. An analytic expansion periodically attempts to connect the current node directly to the goal using a closed-form curve, dramatically reducing search time when a clear path exists.

The planner searches in a 3D space: (x_cell, y_cell, heading_bin). Each expansion applies a kinematically feasible motion primitive, ensuring the output path can be physically executed by the robot.

Critical: Minimum Turning Radius

minimum_turning_radius: 0.20  # meters - for differential drive robots

For differential drive robots, the true minimum turning radius is effectively zero (the robot can spin in place). However, setting it to exactly 0 produces degenerate motion primitives. Use a small value like 0.1–0.4 meters to produce smooth, natural-looking arcs while still allowing tight turns. Larger values (0.5+) force wider arcs and may cause failures in tight spaces.

For Ackermann (car-like) robots, set this to the actual mechanical minimum turning radius.

Motion Models

For differential drive robots, Reeds-Shepp is preferred since the robot can move backward. For Ackermann robots, choose based on whether reversing is allowed in your application.

Key Parameters

Analytic Expansion Parameters

The analytic expansion attempts to shortcut the search by computing a closed-form Dubins/Reeds-Shepp curve from the current node to the goal.

Penalty Parameters

These penalties shape path characteristics by adding cost to certain types of motion.

Penalty tuning strategy:

• Robot hugs walls → increase cost_penalty (try 3.0–5.0).
• Path has too many unnecessary turns → increase non_straight_penalty.
• Robot reverses when it shouldn't → increase reverse_penalty or disable allow_reverse_expansion.
• Robot oscillates forward/reverse → increase change_penalty.

Complete YAML Configuration

planner_server:
  ros__parameters:
    expected_planner_frequency: 20.0
    planner_plugins: ["GridBased"]
    GridBased:
      plugin: "nav2_smac_planner::SmacPlannerHybridAstar"
      tolerance: 0.25
      max_iterations: 1000000
      max_on_approach_iterations: 1000
      max_planning_time: 5.0
      minimum_turning_radius: 0.20
      motion_model_for_search: "REEDS_SHEPP"
      angle_quantization_bins: 72
      analytic_expansion_ratio: 3.5
      analytic_expansion_max_length: 3.0
      lookup_table_size: 20.0
      allow_unknown: true
      allow_reverse_expansion: false
      cost_travel_multiplier: 2.0
      change_penalty: 0.0
      non_straight_penalty: 1.20
      cost_penalty: 2.0
      reverse_penalty: 2.0
      downsample_costmap: false
      downsampling_factor: 1
      cost_penalty: 2.0
      smoother:
        max_iterations: 1000
        w_smooth: 0.3
        w_data: 0.2
        tolerance: 1.0e-10

When To Use

Good fit:

• You want kinematically feasible paths directly from the planner, reducing reliance on the controller to fix infeasible segments.
• Ackermann steering robots with a real minimum turning radius.
• Differential drive robots in constrained environments where smooth, arc-based paths reduce controller effort.
• Docking or parking maneuvers where approach angle matters.

Poor fit:

• Simple open environments where NavFn + controller smoothing suffices (Hybrid-A* is slower).
• Real-time replanning at high frequency (the 3D search is inherently more expensive).
• Holonomic robots that don't benefit from non-holonomic motion planning.

Performance Considerations

Hybrid-A* is significantly slower than 2D planners because the search space is O(X × Y × θ) instead of O(X × Y). Mitigation strategies:

• Downsample the costmap (downsample_costmap: true, downsampling_factor: 2).
• Reduce angle_quantization_bins to 36 (10° resolution) if fine heading control isn't needed.
• Lower max_planning_time and accept occasional planning failures that trigger replanning.
• Reduce lookup_table_size if your map is smaller.

Troubleshooting

• Planner times out in open space: The lookup table or analytic expansion may be misconfigured. Increase analytic_expansion_ratio to attempt shortcuts more often.
• Paths have unnecessary reversal segments: Set allow_reverse_expansion: false or increase reverse_penalty.
• Planner fails in narrow corridors: Decrease minimum_turning_radius. Check that costmap inflation isn't closing off the corridor.
• Path doesn't match robot's actual capability: Ensure minimum_turning_radius matches your robot. For diff-drive, 0.1–0.4m is typical.