マルチロボット系におけるGNNベースの分散知覚による作業者行動予測
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
各ロボットが周囲の空間グラフを構築・共有し、時系列情報と群知能的な合意形成を組み合わせて人間の行動を分散的に予測する枠組みを提案した。
著者: Ali Imran, Giovanni Beltrame, David St-Onge
分類: cs.RO, cs.AI, cs.MA
原文アブストラクト
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.
