GNN-based Decentralized Perception in Multirobot Systems for Predicting Worker Actions
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 43 references that are in the map. First posted on arXiv 8 Jan 2025.
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References · 13
- 2017Gate-Variants of Gated Recurrent Unit (GRU) Neural Networkscited by 239
- 2018When will you do what? - Anticipating Temporal Occurrences of Activitiescited by 106
- 2019Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networkscited by 103
- 2019Graph Neural Networks for Decentralized Multi-Robot Path Planningcited by 84
- 2019What the Constant Velocity Model Can Teach Us About Pedestrian Motion Predictioncited by 78
- 2020Spatiotemporal Relationship Reasoning for Pedestrian Intent Predictioncited by 71
- 2022Multi-Robot Collaborative Perception with Graph Neural Networkscited by 34
- 2017Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localizationcited by 33
- 2021A comparison of LSTM and GRU networks for learning symbolic sequencescited by 24
- 2024Comparing the Pearson and Spearman Correlation Coefficients Across Distributions and Sample Sizes: A Tutorial Using Simulations and Empirical Datacited by 16
- 2021Autonomous Vehicles Drive into Shared Spaces: eHMI Design Concept Focusing on Vulnerable Road Userscited by 3
- 2024Pedestrian-Vehicle Interaction in Shared Space: Insights for Autonomous Vehiclescited by 3
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