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

2025-01-08

AI Summary

To address safety behavior prediction in industrial human-robot collaboration, this paper proposes a decentralized multi-robot perception framework. Each robot autonomously constructs a local spatial graph and fuses spatiotemporal information to model human motion dynamics via graph neural networks (GNNs). A swarm-based distributed consensus mechanism is introduced to achieve consistent cross-robot behavioral understanding without a central node. This work is the first to deeply integrate GNNs with distributed consensus decision-making, significantly enhancing system robustness against occlusions and communication failures. Experiments demonstrate that prediction accuracy improves consistently with increasing robot count and longer temporal sequences, validating the framework’s scalability and adaptability to dynamic environments.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Predictive Human Behavior
• Robotics
• Human-Robot Collaboration

Innovation

• Graph Neural Networks
• Collective Intelligence
• Predictive Human Behavior Modeling