🎯 Argus - Vision-Guided Missile Simulation

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Argus is an advanced simulation that combines machine learning and real-time 3D physics to create a self-guided missile system. The project demonstrates how computer vision can be integrated into game engines for autonomous target tracking and interception.

Named after the Argus Panoptes from Greek mythology—a giant with a hundred eyes—this project embodies the concept of constant visual surveillance and intelligent tracking.

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Features

🤖 Computer Vision & AI

• YOLOv8 Object Detection running in real-time on GPU
• Unity Sentis Integration for neural network inference
• Single-class aircraft detection optimized for accuracy
• Real-time bounding box visualization with confidence scores

🚁 Flight Physics

• Realistic aerodynamic model with lift/drag calculations
• Angle of Attack (AoA) based flight dynamics
• Proportional Navigation guidance system
• Manual plane controls with throttle, pitch, and roll

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🎯 How It Works

The Complete Pipeline

[Camera Feed] → [640×640 Tensor] → [YOLOv8 Sentis] → [Detections]
     ↑                                                      ↓
     └──[Missile Rotation]←[Proportional Navigation]←[Offset Calculation]

How Guidance Works:

1. Camera captures view → 640×640 render texture
2. Sentis runs YOLOv8 inference → detections [x, y, w, h, confidence]
3. Target center computed in screen space
4. Offset calculated: (targetX - screenCenterX, targetY - screenCenterY)
5. Missile rotates toward target: yaw ∝ offsetX, pitch ∝ offsetY

Proportional Navigation Explained

The missile uses a classic guidance law:

1. Detection: YOLOv8 finds target bounding box [x, y, w, h]
2. Center Calculation: targetCenter = (x + w/2, y + h/2)
3. Screen Offset: offset = targetCenter - screenCenter
4. Normalize: offsetNormalized = offset / screenSize × 2 → range [-1, 1]
5. Apply Rotation:

   yaw += offsetX × rotationSpeed × deltaTime
   pitch += offsetY × rotationSpeed × deltaTime

This creates a smooth pursuit trajectory that leads the target!

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🤖 ArgusModel - ML Training & Export

Data

• Uses the Kaggle Military Aircraft Detection Dataset
• All aircraft types merged into a single "Plane" class for maximum accuracy
• Trained on Kaggle GPU (NVIDIA P100) in ~10 minutes

Model Training

• Architecture: YOLOv8n (nano variant for real-time performance)
• Input Size: 640×640 pixels
• Output: Bounding boxes with confidence scores

ONNX Export for Unity

# main.py - Optimized export for Unity Sentis
model.export(
    format='onnx',
    imgsz=640,
    simplify=True,
    opset=15,        # Sentis compatibility
    batch=1,         # Fixed batch size
    dynamic=False    # Static shapes for performance
)

Model Performance

Metric	Value
Input Size	640×640 RGB
Inference Time	~16-30ms (GPU)
FPS	30-60
Confidence Threshold	0.9
Architecture	YOLOv8n

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🎮 ArgusSimulation - Unity 3D Simulation

Engine: Unity 2023.2.20f1

📊 Technical Specifications

Physics Parameters

Parameter	Default Value
Air Density	1.225 kg/m³
Wing Area	16 m²
Max Thrust	190 N
Lift Slope (ClAlpha)	5.5
Induced Drag (k)	0.04

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🚀 Future Improvements

• Hybrid Tracking: Combine detection with Kalman filtering for smoother tracking
• Multi-Target: Track and prioritize multiple aircraft
• Better Physics: Add wind resistance, thrust vectoring, fuel consumption
• Advanced UI: HUD with radar, lock indicators, target info
• Multiplayer: Network-based dogfighting simulation

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References & Credits

• Unity Flight Physics - b3agz
• Unity Flight Physics - Vazgriz