RoboFlux Whitepaper
  • RoboFlux AI: Comprehensive Technical Whitepaper
  • 1. Introduction
  • 2. System Overview
  • 3. Modular Architecture
  • 4. Data Ingestion and Preprocessing Layer
    • 4.1 Data Stream Typologies
    • 4.2 Data Pipeline Orchestration
    • 4.3 Data Normalization & Feature Engineering
  • 5. Anomaly Detection Subsystem
    • 5.1 Tensor-Based Anomaly Detection
    • 5.2 Online Learning Adaptation
  • 6. Quantum-Inspired Path and Task Optimization
  • 7. Secure Webhook Integration Framework
  • 8. AI Knowledge Hub Implementation
  • 9. Report Generation and Distribution Mechanism
  • 10. Deployment Modalities
  • 11. Cybersecurity and Compliance Protocols
  • 12. Future Roadmap and Extensibility
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  1. 4. Data Ingestion and Preprocessing Layer

4.1 Data Stream Typologies

RoboFlux AI accommodates a variety of telemetry modalities including but not limited to:

  • Time-Series Sensor Logs: Continuous metrics from accelerometers, gyroscopes, LIDAR, temperature sensors, and power monitors.

  • Event-Based Data: Discrete operational triggers such as emergency stop events, overload alerts, and task initiation logs.

  • Spatial Grid Maps: CSV-encoded matrices representing operational terrains for robotic pathfinding.

  • Task Allocation Matrices: Tabulated dispatch and duty cycle assignments for autonomous agent swarms.

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Last updated 12 days ago