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AI selection architecture document

AI Selection Architecture Document: 3D One AI

Version: 1.0
Date: 2023-10-05

1. Introduction

Project Overview:
3D One AI is an educational platform for K-12 students, integrating virtual electronics, robotics programming, physics-based simulations, and AI-driven behavioral modeling. It aims to align with national STEM curricula, enabling users to design, program, and simulate interactive 3D robotics systems via GUI or code.

Document Purpose:
This document defines the AI/ML architecture for realistic physics simulations, behavioral AI, and educational personalization, ensuring scalability, security, and performance.

2. Key Requirements

  • Functional:
    • Real-time rigid-body physics simulation.
    • AI-driven agent behavior (e.g., pathfinding, decision-making).
    • Virtual hardware emulation (Arduino/Raspberry Pi).
    • 3D animation rendering & export (e.g., GLB, FBX).
    • Block-based (Blockly) and Python scripting interfaces.
  • Non-Functional:
    • Performance: 60 FPS on standard school hardware (Intel i5/8GB RAM).
    • Security: GDPR/COPPA compliance, sandboxed code execution.
    • Scalability: Support 50+ concurrent users per server instance.
    • Extensibility: Modular AI/ML pipeline for future integrations.

3. AI Functional Areas & Technology Selection

Component Technology Version Rationale
Physics Simulation NVIDIA PhysX 5.1 GPU-accelerated rigid-body dynamics; industry standard for real-time accuracy.
Behavioral AI Unity ML-Agents 3.0 Reinforcement Learning (RL) for adaptive robot behaviors; Unity-native integration.
Pathfinding A* Algorithm (Recast & Detour) 1.6 Open-source, efficient for 3D navigation meshes.
3D Data Processing Unity Engine (URP) 2022.3 LTS Optimized rendering pipeline; cross-platform (Windows/macOS/WebGL).
AI Tutoring System Scikit-learn 1.3.0 Lightweight ML for student progress analysis (clustering & recommendation).
Code Execution Pyodide (WebAssembly) 0.24.0 Secure in-browser Python runtime; sandboxed for student safety.

4. Architecture Overview

3D One AI Architecture Diagram
(Diagram Description: Client-Server model with Unity frontend, REST APIs for AI services, and isolated execution environments.)

  • Client Layer:
    • Unity-based GUI (Windows/macOS/WebGL).
    • Blockly (v10.0) for visual programming.
  • AI Service Layer:
    • Physics Engine: PhysX handling collisions, gravity, and joint dynamics.
    • Behavior Engine: ML-Agents RL policies for autonomous agents.
    • Pathfinding Service: Recast/Detour for dynamic obstacle avoidance.
  • Data Layer:
    • 3D Asset Database: GLTF 2.0 models stored in MongoDB (v6.0).
    • Student Analytics: PostgreSQL (v15) for progress tracking (anonymized).

5. Implementation Steps

Phase 1: Core Physics & Rendering (Sprint 1-2)

  1. Integrate PhysX 5.1 into Unity URP.
  2. Develop virtual hardware emulators (Arduino/C++ bindings via DLLs).
  3. Implement GLB export using Unity Recorder (v3.0).

Phase 2: Behavioral AI & Pathfinding (Sprint 3-4)

  1. Train RL policies in ML-Agents (Unity) for tasks:
    • Object avoidance (PPO algorithm).
    • Line-following robots (Imitation Learning).
  2. Integrate Recast Navigation for dynamic 3D path grids.

Phase 3: Educational Personalization (Sprint 5-6)

  1. Deploy Scikit-learn-based recommender:
    • Cluster students by skill level (k-means).
    • Suggest challenges via cosine similarity.
  2. Secure Pyodide for browser Python execution (XSS protection).

Phase 4: Optimization & Security (Sprint 7)

  1. Profile GPU usage (NVIDIA Nsight) to maintain 60 FPS.
  2. Apply sandboxing:
    • Blockly/Python code → WebWorker threads.
    • Hardware emulators → Docker containers.
  3. Encrypt student data at rest (AES-256).

6. Key Considerations

  • Scalability:
    • Use Kubernetes for containerized AI services (auto-scaling groups).
    • Level-of-Detail (LOD) for physics simulations at scale.
  • Security:
    • Role-based access (teacher/student) via OAuth 2.0.
    • Static code analysis (Bandit) for Python scripts.
  • Performance:
    • GPU batching for 3D rendering.
    • Async I/O for animation exports.
  • Extensibility:
    • gRPC APIs for future hardware (e.g., ROS integration).
    • Modular ML pipelines (support TensorFlow Lite post-v1.0).

7. Conclusion

This architecture leverages industry-standard tools (Unity, PhysX, Pyodide) to deliver a secure, high-performance platform for AI-driven robotics education. By prioritizing GPU acceleration, sandboxed execution, and modular AI services, 3D One AI ensures adaptability to evolving curriculum needs while minimizing technical barriers for K-12 adoption.

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