多机器人通过图神经网络共享环境信息，实时预测工人动作。
GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
- 用空间图+时间序列建模人类行为，各机器人独立感知
- 增加机器人数量和时序长度可提升预测准确率
- 去中心化共识机制增强系统鲁棒性，适合工业动态场景
在工业环境中，预测人类行为对确保人机安全协作至关重要。本文提出一种去中心化的感知框架，使移动机器人能够以分布式方式理解并共享人类行为信息。每个机器人首先构建自身周围环境的空间图，并与其他机器人共享。这些共享的空间数据结合时间信息，用于追踪人类行为变化。采用群体启发式决策机制，确保所有机器人就人类动作达成统一理解。实验表明，增加机器人数量及使用更长的时间序列可提高预测准确性。此外，共识机制增强了系统的容错能力，使多机器人系统在动态工业环境中更具可靠性。
原文摘要 · Abstract (English)
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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