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Lab Connect

Overview

This comprehensive documentation provides a complete guide to laboratory digitalization and the path toward self-driving laboratories (SDLs). It integrates real-world implementation experience from up-scaling experiments with unit operations and experimental measurement devices with state-of-the-art SDL concepts from recent research.

Key Aspects

1. Foundational Concepts

  • Self-Driving Laboratory Spectrum: 6 levels of autonomy (0-5)
  • Two Dimensions: Hardware automation + software autonomy
  • Pragmatic Philosophy: Incremental progression, not complete rebuild

2. Technical Architecture

  • Protocol Unification: OPC-UA gateways for heterogeneous equipment in combination with industrial SCADA systems
  • Data Infrastructure: MQTT broker + time-series based PostgreSQL database
  • SCADA System: Unified web-based interfaces
  • Kubernetes Deployment: Container orchestration for scalability

3. Advanced Capabilities

  • Closed-Loop Experimentation: Bayesian optimization integration
  • Digital Twins: Real-time hybrid models with incremental learning
  • Modular SDL Framework: Modular robotic lab automation using ROS2
  • Robotic Automation: UR5e (Universal Robots) and Meca500 (Mecademic) robot arms and MoveIt2 motion planning
  • Computer Vision: LabLiquidVision volume estimation, collision avoidance
  • Soft Sensors: On-edge estimation of unmeasured variables

4. Real-World Implementation

  • Single unified interface
  • Automatic data acquisition and structured storage
  • Digital twins deployment for research and/or production
  • Infrastructure can be used over several locations/units

Key Innovations

  1. Modular Design: New equipment added without schema changes
  2. MRLAS Framework: ROS2-based modular robotic lab automation system
  3. Computer Vision: LabLiquidVision for volume estimation, stereo vision for collision avoidance
  4. Open Standards: Vendor-neutral, replaceable components (OPC-UA, MQTT, ROS2)
  5. Security: Network segmentation, encrypted communication
  6. Scalability: From 1 to 100+ instruments with same architecture
  7. DevOps: CI/CD pipelines, version-controlled infrastructure

Primary References

  1. Huusom, Jones, Krager, Dam-Johansen, Dreyer; Building a scalable digital infrastructure for a (bio)chemical engineering pilot plant: A case study from DTU; Digital Chemical Engineering, 2025; DOI

  2. Mansour & Jalili; MRLAS - Modular Robotic Lab Automation System; M.Sc. Thesis, 2024, DTU Electrical and Photonics Engineering and collaboration with MQS and DTU DALSA

  3. Tom et al., 2024; Self-Driving Laboratories for Chemistry and Materials Science; DOI

Target Audiences

  • University Researchers: Modernizing laboratory infrastructure
  • Industrial R&D: Process automation and optimization
  • Laboratory Directors: Strategic planning for digital transformation
  • Educators: Teaching next-generation scientists
  • IT Departments: Understanding requirements and architecture

MQS Value Proposition

  • Proven Expertise: 14 units digitalized, multiple research applications
  • Pragmatic Approach: Start small, scale systematically
  • Technology Agnostic: Open protocols, no vendor lock-in
  • Knowledge Transfer: Training and documentation included
  • Long-term Support: Maintainable, extensible architectures

Next Steps for Readers

  1. Assess your current state and goals
  2. Review relevant sections based on your role
  3. Contact MQS for detailed consultation
  4. Plan phased implementation approach
  5. Execute starting with pilot project

Contact

Email: contact (at) mqs [dot] dk