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
Powered by GitBook
On this page

2. System Overview

RoboFlux AI operates as a decentralized, modular orchestration platform facilitating seamless integration between AI-driven anomaly detection engines, quantum-inspired task optimization algorithms, and distributed robotic ecosystems. Its operational topography is partitioned into three primary strata:

  • Cognitive Analysis Layer (CAL): Executes tensor-based anomaly detection using convolutional recurrent networks optimized for multivariate time series sensor streams.

  • Optimization and Pathfinding Engine (OPE): Deploys a quantum-inspired Grover heuristic search protocol and genetic algorithm hybrids for task and path optimization across spatially distributed robotic agents.

  • Secure Integration Gateway (SIG): Manages encrypted bidirectional communications via webhook mechanisms, ensuring robust and scalable interoperability with physical robotic infrastructures.

These layers are synergistically interconnected through asynchronous microservices and event-driven data buses, delivering high-availability, fault-tolerant robotic orchestration services.

Previous1. IntroductionNext3. Modular Architecture

Last updated 12 days ago