GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
This work addresses safety and collaboration issues for human-robot teams in industrial settings, but it is incremental as it builds on existing graph-based and swarm methods.
The paper tackles the problem of predicting human actions in industrial environments by introducing a decentralized perception framework for multi-robot systems, where results show that adding more robots and longer time sequences improves prediction accuracy and a consensus mechanism enhances system resilience.
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. Additionally, the consensus mechanism increases system resilience, making the multi-robot setup more reliable in dynamic industrial settings.