GNN-based decentralized perception in multi-robot systems for predicting worker actions

Imran, Ali, Beltrame, Giovanni et St-Onge, David. 2025. « GNN-based decentralized perception in multi-robot systems for predicting worker actions ». IEEE Robotics and Automation Letters, vol. 10, nº 6. pp. 6336-6343.

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Résumé

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 swarminspired 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.

Type de document:	Article publié dans une revue, révisé par les pairs
Professeur:	Professeur St-Onge, David
Affiliation:	Génie mécanique
Date de dépôt:	23 avr. 2025 18:06
Dernière modification:	22 mai 2025 16:32
URI:	https://espace2.etsmtl.ca/id/eprint/30850

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