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

TL;DR

This paper presents a decentralized perception framework using GNNs for multirobot systems to predict human actions in industrial environments, enhancing safety and collaboration through shared spatial-temporal data and consensus mechanisms.

Contribution

It introduces a novel decentralized GNN-based perception framework that enables multi-robot systems to collaboratively predict human actions in industrial settings.

Findings

Adding more robots improves prediction accuracy.

Longer temporal sequences enhance understanding of human behavior.

Consensus mechanisms increase system resilience.

Abstract

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.