Real-Time Digital-Twin-Based Cobot-Worker Collision Risk Prediction Using Unity, ROS, and UWB

Abstract

The growing use of collaborative robots (cobots) in industrial environments raises concerns about worker safety; flexible and adaptive systems are required to predict and prevent real-time cobot–worker collisions. This study proposes a digital-twin-based, cobot–worker collision risk-prediction system that utilizes the Unity game-development engine, robot operating system (ROS), and ultra-wideband (UWB) communication. The proposed system incorporates three modules: 1) real-time cobot–worker data capture, 2) digital-twin-based dynamic collision-zone management, and 3) collision-risk prediction. The core of the system is a data-driven cobot–worker digital twin that reflects the movements of the cobot and worker using ROS and UWB communication. This dynamic approach addresses the limitations of fixed safety-zone configurations by considering the cobot trajectory and the projected worker path within the digital-twin modules. The system demonstrated low latency in data transmission from ROS to Unity (average 13 ms) and from UWB to Unity (average 140 ms), through implementation and evaluation. It also accurately predicted the minimum distance between the worker and cobot, accounting for varying worker speeds and future prediction times. This research offers a promising solution for enhancing worker safety in collaborative robotic environments by providing a real-time, digital-twin-based collision risk prediction system with dynamic collision zone management.