Chicken Twin

Simulation Logic (Particle-Level Detail) The simulation runs on a high-frequency loop (60Hz) using a state-update-render cycle. Entity Mobility: Every bird is an object instance with independent vectors for position, velocity, and acceleration. The "Boids" Influence: Bird movement uses a simplified flocking algorithm containing three forces: Cohesion: Steering toward the average position of the flock. Separation: Steering to avoid crowding (collision avoidance). Wander: A Perlin-noise based "random walk" that simulates natural curiosity. Disease Vectors: Diseases are non-linear state machines. Once a bird is infected, its internalTemperature rises at 0.05°C per minute, and its mobilityFactor decays, leading to perceptible lethargy. Robotic Engineering (ROBO-SM) The robot uses a Telescoping Brother-Node Hierarchy for the arm. Physics Constraint: Unlike simple scaling, the segments are individually addressed meshes that maintain their diameter while extending, ensuring visual stability. Interpolation: Movement uses ease-in-out quintic interpolation to simulate the mass and momentum of industrial servos. Mission Sequence: SCAN → INTERCEPT → DESCEND → SECURE → RETRACT → DEPOSIT. Backend Juggernaut (monitor.py) The Python backend acts as the "Cognitive Core." WebSocket Telemetry: It broadcasts a serialized JSON stream containing every bird's coordinate and risk score. Persistent Memory: Saves session data to a local server-side database, allowing for high-speed replay with 100% frame fidelity. Safety Net: The Fallback AI (browser-side) takes over if the Python core loses heartbeat, ensuring 100% uptime for live demos.