Heuristic Weights

2. HeuristicWeights

• What it is: A class that defines the weights used in the A* heuristic function. These weights determine how the algorithm prioritizes different factors when searching for a path.

• Why it's important: It allows you to fine-tune the characteristics of the paths found. Do you want the shortest path? The smoothest path? A path that avoids height changes? HeuristicWeights controls this.

• How to use it:

  • Use the create() method to create a new HeuristicWeights instance with your desired weights.
  • Use the predefined constants (NATURAL_PATH_WEIGHTS, DIRECT_PATH_WEIGHTS) as starting points or for common scenarios.

  // For natural-looking paths:
  HeuristicWeights naturalWeights = HeuristicWeights.NATURAL_PATH_WEIGHTS;
  
  // For the most direct path:
  HeuristicWeights directWeights = HeuristicWeights.DIRECT_PATH_WEIGHTS;
  
  // For custom weights:
  HeuristicWeights customWeights = HeuristicWeights.create(
      0.4,  // manhattanWeight (direct movement)
      0.2,  // octileWeight (diagonal movement)
      0.3,  // perpendicularWeight (smoothness)
      0.1   // heightWeight (elevation changes)
      0.5   // directionalPenatlyWeight (stay on track)
  );

  Understanding the Weights:

  • manhattanWeight: Higher values favor grid-like, axis-aligned movement.
  • octileWeight: Higher values encourage diagonal movement.
  • perpendicularWeight: Higher values result in smoother paths by penalizing deviations from a straight line to the target.
  • heightWeight: Higher values make the algorithm more sensitive to changes in elevation (useful for terrain).