ReFLEX Lab

"Cyber-physical systems, such as autonomous robots and self-driving cars, are becoming integral to modern society, with profound economic and societal implications. Our lab envisions leading the advancement of real-time scheduling and assurance techniques that enable safe, reliable, and adaptive robotics and autonomous systems."

Introduction

At Real-Time and Flexible Cyber-Physical Systems (ReFLEX) Lab, we focus on advancing real-time and embedded systems across topics like scheduling on many-core architectures, harnessing digital twins for performance gains, ensuring reliable timing in robotics and AI, verifying long-lived cyber-physical systems, and innovating hardware for real-time and IoT solutions. Typical target systems of the lab include autonomous driving, avionic and aerospace control systems, robotic warehouse, virtual production and transportation. By blending theory and practice, we aim to push the boundaries of computing and deliver safer, more efficient technologies that can serve both industry and society.

Our lab leverages digital twin technology to build real-time virtual replicas of embedded and cyber-physical systems, enabling runtime monitoring, scheduling optimisation, and verification of timing properties. We also develop novel scheduling algorithms and resource allocation strategies for safety-critical platforms, from multi-core processors and networks-on-chip to multi-robot systems and autonomous vehicles.

Our Research

➤ Mixed-criticality scheduling and timing assurance for avionics

We develop scheduling algorithms and timing assurance methods for mixed-criticality avionics, where safety-critical and lower-criticality tasks share the same hardware platform. Our work on the SCHEME project focuses on UK-native safety-critical microprocessors, addressing task isolation, predictable timing, and certification requirements for next-generation aerospace systems.

Related Projects:

• SCHEME: Safety Critical Harsh Environment Micro-processing Evolution

➤ Multi-core and network scheduling and allocation for autonomous driving

We investigate scheduling and resource allocation techniques for autonomous driving platforms, where sensor processing, decision-making, and vehicle-to-infrastructure communication must meet strict real-time deadlines. Our research addresses multi-core contention, cache interference, and network scheduling to provide timing guarantees for safety-critical autonomous functions.

Related Projects:

• Safety-Driven Timing-Predictable and Resource-Efficient Scheduling for Autonomous Systems

➤ Large-scale allocation and scheduling for cyber-physical systems

We develop scalable scheduling and allocation frameworks for complex cyber-physical systems, building on our work in the MOCHA and ATAS projects. Our approaches combine statistical modelling, adaptive policies, and digital-twin-based feedback to manage task dependencies and real-time constraints across many-core architectures, including 5G/6G base stations and industrial control systems.

Related Projects:

• MOCHA: Modelling and Optimising Complex Heterogenous Architectures
• ATAS: Adaptive Task Scheduling Framework for CPS

➤ Real-time digital twin and robotics for virtual production

We integrate real-time digital twin technology with robotic systems for virtual production in film, television, and live events. Through the RAVEN project and our XR Stories residency, we develop virtual replicas of physical sets and robotic camera platforms, enabling real-time previsualization and autonomous filming workflows.

Related Projects:

• RAVEN: Real-time Adaptive Virtual-Twin Environment for Next-Generation Robotics in Virtual Production

➤ Real-time Industrial Digital Twin

We investigate the real-time challenges of industrial digital twins, focusing on how to maintain temporal fidelity between physical and virtual entities under resource constraints. Through the DDTwins framework, we develop scheduling strategies and adaptive computation techniques to ensure digital twins meet real-time deadlines in domains such as smart manufacturing and energy systems.

Related Projects:

• DDTwins: An Industrial Digital Twin Framework

➤ Multi-robot safety-aware scheduling for Industry 5.0

We develop multi-robot scheduling frameworks that integrate safety awareness into task allocation for collaborative human-robot environments. Our approach combines real-time sensor data with criticality-aware scheduling to dynamically adapt to operational uncertainties while maintaining safety guarantees in manufacturing and warehouse settings.

Related Projects:

• DEIS: Dependability Engineering Innovation for CPS

Our Research Team

Lab Lead

• Dr. Steven Xiaotian Dai, University of York, UK

Research Associate/Fellow

• Dr. Nan Chen (2023-2028), Adaptive Task and Resource Scheduling in Avionics (SCHEME project)
• Dr. Felix Ulrich-Oltean (2025-2028), Safety-Critical Network Scheduling for Avionics (SCHEME project)

PhD Students

• Modie Al Shakarchi (2025-2028), Real-time Adaptive Digital Twin Robotics for Virtual Production (RAVEN project), co-supervisor: Dr. Pengcheng Liu
• Manal Abdelrahman (2023-2026), An Industrial Digital Twin Framework (DDTwins project), co-supervisor: Suresh Perinpanayagam

Academic Visitors

• Haochun Liang (2025-2026), Real-time containers for Mixed-Criticality Real-Time Systems, Visiting PhD Student (CSC)

UG Students

• Charlie Piper (2025-2026), Adaptive Decentralised Coordination Algorithms for Autonomous Robotic Swarms in Planetary Surface Missions
• Huzaifa Thakur (2025-2026), Imitation Learning for Humanoid Robot Training
• Luke Tissiman (2025-2026), Criticality-aware Scheduling and Path Planning for Fault-Tolerant Cooperative Multi-Robot Systems
• Maciek Racis (2025-2026), People Detection for Autonomous Retail and Smart Refrigeration Systems
• Wrijurekh Mukherjee (2025-2026), People Detection for Autonomous Retail and Smart Refrigeration Systems
• Mikolaj Wyrzykowski (2025-2026), Imitation Learning for Humanoid Robot Training
• Mohamed Eljak (2025-2026), Edge AI for Real-Time Anomaly Detection in Robotic Systems
• Rosie Kern (2025-2026), Criticality-aware Scheduling and Path Planning for Fault-Tolerant Cooperative Multi-Robot Systems
• Sam Knight (2025-2026), Embedded GPU Interference Analysis for Real-Time Robotics

Student Intern:

• Jixiang Zhen (2026), Digital Twin for Humanoid Robots in Virtual Production

MSc Students

• Zilun Zhang (2025-2026), Randomised DAG generator with AI based on small samples

Research Advisors

• Prof. Iain Bate (CS HoD), University of York, UK
• Prof. FREng. Alan Burns, University of York, UK
• Prof. FREng. John McDermid (ISA Director), University of York, UK

Past Members

PhD:

• Dr. Zou Jie (2019-2023), Safety-Driven Timing-Predictable and Resource-Efficient Scheduling for Autonomous Systems, co-supervisor: Prof. John McDermid → Research Fellow, University of York

BSc:

• Chase Mo (2024-2025), Traffic detection for deriving KPIs
• Dom Decicco (2024-2025), Routerless Network-on-Chip optimization
• James Sutton (2024-2025), Multi-robot scheduling for warehouses
• Phoebe Russell (2024-2025), Traffic control with back-pressure
• Aron Hogarth (2024-2025), Gamification on High Education studying and learning of programming
• Riko Puusepp (2024-2025), AR for accelerating learning of programming
• Dean Kenny (2020-2021), Simulating and improving the scheduling in time-sensitive networks

MSc:

• Xiyu Fang (2025), Randomised DAG generator for evaluating scheduling performance
• Zirui Yuan (2023), Simulation and Optimization of Routerless Networks-on-Chips
• Zhijian Wang (2021), Priority Assignment Algorithms in Multiprocessor Real-Time Systems with Shared Resources
• Zixun Yu (2021), Smart Intersection Control with Back-Pressure Algorithms (co-supervised)

Open-Source Projects

Please check our GitHub: ReFLEX-Lab-York.