XR Neuroergonomic Cobot-Supported Manual Assembly TestBed

This TestBed is a human-centred Industry 5.0 workstation for manual assembly, combining Extended Reality (XR), collaborative robotics, and neuroergonomic assessment in a single setup. It is designed for low-batch industrial assembly tasks where operators need clear instructions, intuitive human-robot interaction, and support for attention-intensive work. The setup builds on earlier ETF/ICEF work and replaces a fragmented baseline system with a more compact XR-based solution that integrates spatial assembly guidance, gaze/gesture/voice interaction, robot coordination, and mental focus assessment. The concept as a neuroergonomic workcell with EEG-based workload assessment, nonobtrusive HMI, graphical assembly guidance, a collaborative robot assistant, and an intelligent task scheduler, showing improved performance, fewer errors, and reduced mental demand. In the XR4Human-SERVE 5.0 implementation, the target use cases are assembly assistance and training assistance for real industrial products.

Name of Principal Investigator: Nikola Knezevic
Position / institutional role: Assistant Professor
Info Email: knezevic@etf.rs
ORCID persistent identifier (PID): 0000-0002-0262-8956
Name of Host Organization  University of Belgrade – School of Electrical Engineering (ETF) 
Department or Lab   Department of Signals and Systems, ETF Robotics lab 
Name of Building  Palace of Science 
Physical Address   Kralja Milana 11, 11000 Beograd, Serbia 
Website Links  https://robot.etf.bg.ac.rs 
Institutional contact name  Nikola Knezevic
Institutional contact email  knezevic@etf.rs
Non-academic stakeholders
Industrial Partners; Startups; Professional Associations; SMEs; Community
Academic stakeholders
Undergraduate students; PhD students; MSc students; Researchers
Other types of stakeholders
System integrators, Industrial trainers, Human factors / ergonomics specialists
Application case: Short description:
XR Collaborative Assembly The TestBed is used for real manual assembly scenarios in which the worker receives spatially aligned instructions through HoloLens 2 while the cobot delivers parts and supports the task flow. In the XR4Human-SERVE 5.0 plan, the industrial validation covers three product scenarios: GS-100, GS-200, and GP-100. Target measurable outcomes include at least 5% assembly-time improvement and at least 50% reduction in faulty parts relative to the stated baseline.
XR-assisted onboarding and training assistance The same setup can be used for novice-worker onboarding and guided training, with multiple levels of instruction detail and remote assistance from more experienced workers. This use case is explicitly positioned as a way to speed up training, improve knowledge transfer, and capture where trainees need support. The target measurable outcome is at least 30% training-time reduction across the selected scenarios.
Neuroergonomic monitoring and fatigue-aware task support The TestBed can be used to study operator focus, distraction, and workload during assembly by combining eye-gaze signals with workload assessment methods such as NASA TLX and, in the prior/papered setup, EEG-derived indices. In the published paper, the workstation concept is evaluated through workload, errors, task duration, and gesture accuracy, and the authors report a notable correlation between EEG workload indices and NASA scores. In the XR4Human-SERVE 5.0 plan, at least 10 participants are foreseen in industrial validation, with user feedback collected across the defined scenarios.

list of hardware components with their brief descriptions:

HTC Vive Focuse Vision Pro, Franka Emika Panda, EEG mBrainTrain

list of software components with their brief descriptions:

XR4Human-SERVE 5.0 application stack, OpenXR,