GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions

-- In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. This paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Collaborative robots are poised to become a cornerstone of Industry 5.0 [1], emphasizing human-centric design solutions to meet the flexibility demands of hyper-customized industrial processes [2]. Significant efforts have been directed toward identifying key enabling technologies to enhance robotic systems with advanced situational awareness and robust safety features for human coworkers. Two pivotal technologies stand out: individualized human-machine interaction systems that merge the strengths of humans and machines, and the application of AI to improve workplace safety [3].