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Spatial Temporal Graph Neural Networks for Decentralized Control of Robot Swarms
Conference proceeding   Open access

Spatial Temporal Graph Neural Networks for Decentralized Control of Robot Swarms

Siji Chen, Yanshen Sun, Peihan Li, Lifeng Zhou and Chang-Tien Lu
Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, pp 1-4
13 Nov 2023
DOI: https://doi.org/10.1145/3589132.3625630
Featured in Collection :   UN Sustainable Development Goals @ Drexel
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https://doi.org/10.1145/3589132.3625630View
Published, Version of Record (VoR) Open

Abstract

Computer systems organization -- Embedded and cyber-physical systems -- Robotics Computing methodologies -- Modeling and simulation Information systems -- Information systems applications -- Spatial-temporal systems
Recent research has explored the use of graph neural networks (GNNs) for decentralized control in swarm robotics. However, it has been observed that relying solely on local states is insufficient to imitate a centralized control policy. To address this limitation, previous studies proposed incorporating K-hop delayed states into the computation. While this approach shows promise, it can lead to a lack of consensus among distant flock members and the formation of small localized groups, ultimately resulting in task failure. Our approach is to include the delayed states to build a spatiotemporal GNN model (ST-GNN) by two levels of expansion: spatial expansion and temporal expansion. The spatial expansion utilizes K-hop delayed states to broaden the network while temporal expansion, can effectively predict the trend of swarm behavior, making it more robust against local noise. To validate the effectiveness of our approach, we conducted simulations in two distinct scenarios: free flocking and flocking with a leader. In both scenarios, the simulation results demonstrated that our decentralized ST-GNN approach successfully overcomes the limitations of local controllers. We performed a comprehensive analysis on the effectiveness of spatial expansions and temporal expansions independently. The results clearly demonstrate that both significantly improve overall performance. Furthermore, when combined, they achieve the best performance compared to global solution and delayed states solutions. The performance of ST-GNN underscores its potential as an effective and reliable approach for achieving cohesive flocking behavior while ensuring safety and maintaining desired swarm characteristics.

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Information Systems
Remote Sensing
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