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

• URL: http://arxiv.org/abs/2501.04193v1
• Date: Wed, 08 Jan 2025 00:06:38 GMT
• Title: GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
• Authors: Ali Imran, Giovanni Beltrame, David St-Onge,
• Abstract summary: This paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions.
• Score: 12.260881600042374
• License:
• 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.

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