Tier 4
Series 303
303F: The Poisson Disc
"Randomly placing trees usually results in clumps and overlaps. It looks digital and fake. Nature has personal space. A Navigator uses Poisson Disc Sampling to create organic, evenly spaced distributions."
The Concept: Poisson Disc Sampling
A specific algorithm that picks a point, then tries to pick another point that is *at least* "r" distance away. If it fails, it tries again.
* **Result:** A pleasing, natural distribution of objects without overlaps.
* **Use Case:** Forests, crowd placement, loot spawning.
* **Result:** A pleasing, natural distribution of objects without overlaps.
* **Use Case:** Forests, crowd placement, loot spawning.
Red Flag Detected
The AI Trap: "The Overlap Clump"
You ask the AI: "Scatter 50 coins on the floor."
// AI-Generated Code: Lazy Random
for(int i=0; i<50; i++) {
// Audit Fail: Coins will spawn inside each other.
Vector3 pos = new Vector3(Random.Range(0,10), 0, Random.Range(0,10));
Instantiate(coin, pos, rot);
}
This is "Collision Chaos." Physics objects spawning inside each other will explode apart or look glitchy.
Elite Telemetry
Research shows "Elite" teams achieve 15% faster lead times by keeping AI on a "very tight leash."
- Small Batches Solving one problem at a time prevents logic drift.
- Modular Design Localizing the "blast radius" of AI changes.
- Tight Loops Rapid iteration with constant code review.
The Navigator's Correction
Corrective Protocol
// Corrected: Organic Spacing
List<Vector2> points = PoissonDisc.Generate(radius: 1.5f);
foreach(var p in points) {
Instantiate(coin, new Vector3(p.x, 0, p.y), rot);
}
Your Pilot Command
> A skilled Navigator directs the AI to use Poisson logic.