Abstract
Cooperative planning is one of the critical problems in the field of multi-agent system gaming. This work focuses on cooperative planning when each agent has only a local observation range and local communication. We propose a novel cooperative planning architecture that combines a graph neural network with a task-oriented knowledge fusion sampling method. Two main contributions of this paper are based on the comparisons with previous work: (1) we realize feasible and dynamic adjacent information fusion using GraphSAGE (i.e., Graph SAmple and aggreGatE), which is the first time this method has been used to deal with the cooperative planning problem, and (2) a task-oriented sampling method is proposed to aggregate the available knowledge from a particular orientation, to obtain an effective and stable training process in our model. Experimental results demonstrate the good performance of our proposed method.
摘要
协同规划是多智能体系统博弈领域的关键问题之一。本文聚焦每个智能体只有一个局部观测范围和局部通信情况下的协作规划。提出一种新型协同规划框架, 该框架将图神经网络与融合任务信息采样方法相结合。本文的两个主要贡献是基于与前期工作的比较:(1)使用图采样与聚合方法(GraphSAGE)实现动态近邻智能体信息融合, 这是该方法首次用于处理协同规划问题;(2)提出一种面向任务的采样方法, 从特定方向聚合知识, 使所提模型获得高效、稳定的训练过程。实验结果证明了所提方法的有效性。
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Barer M, Sharon G, Stern R, et al., 2014. Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem. Proc 21st European Conf on Artificial Intelligence, p.961–962. https://doi.org/10.3233/978-1-61499-419-0-961
Dresner K, Stone P, 2008. A multiagent approach to autonomous intersection management. J Artif Intell Res, 31(1):591–656.
Enright JJ, Wurman PR, 2011. Optimization and coordinated autonomy in mobile fulfillment systems. Proc 9th AAAI Conf on Automated Action Planning for Autonomous Mobile Robots, p.33–38.
Hamilton WL, Ying R, Leskovec J, 2017. Inductive representation learning on large graphs. Proc 31st Int Conf on Neural Information Processing Systems, p.1025–1035.
Krizhevsky A, Sutskever I, Hinton GE, 2017. ImageNet classification with deep convolutional neural networks. Commun ACM, 60(6):84–90. https://doi.org/10.1145/3065386
Li QB, Gama F, Ribeiro A, et al., 2020. Graph neural networks for decentralized multi-robot path planning. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.11785–11792. https://doi.org/10.1109/IROS45743.2020.9341668
López J, Pérez D, Zalama E, 2011. A framework for building mobile single and multi-robot applications. Robot Autonom Syst, 59(3–4):151–162. https://doi.org/10.1016/j.robot.2011.01.004
Ortega A, Frossard P, Kovačević J, et al., 2018. Graph signal processing: overview, challenges, and applications. Proc IEEE, 106(5):808–828. https://doi.org/10.1109/JPROC.2018.2820126
Prorok A, Kumar V, 2017. Privacy-preserving vehicle assignment for mobility-on-demand systems. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.1869–1876. https://doi.org/10.1109/IROS.2017.8206003
Sartoretti G, Kerr J, Shi YF, et al., 2019. Primal: pathfinding via reinforcement and imitation multi-agent learning. IEEE Robot Autom Lett, 4(3):2378–2385. https://doi.org/10.1109/LRA.2019.2903261
Singhal V, Dahiya D, 2015. Distributed task allocation in dynamic multi-agent system. Int Conf on Computing, Communication and Automation, p.643–648. https://doi.org/10.1109/CCAA.2015.7148452
Standley T, Korf R, 2011. Complete algorithms for cooperative pathfinding problems. Proc 22nd Int Joint Conf on Artificial Intelligence, p.668–673.
van den Berg J, Lin M, Manocha D, 2008. Reciprocal velocity obstacles for real-time multi-agent navigation. IEEE Int Conf on Robotics and Automation, p.1928–1935. https://doi.org/10.1109/ROBOT.2008.4543489
van den Berg J, Guy SJ, Lin M, et al., 2011. Reciprocal n-body collision avoidance. In: Pradalier C, Siegwart R, Hirzinger G (Eds.), Robotics Research. Springer, Berlin, Heidelberg, p.3–19. https://doi.org/10.1007/978-3-642-19457-3_1
van den Berg JP, Overmars MH, 2005. Prioritized motion planning for multiple robots. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.430–435. https://doi.org/10.1109/IROS.2005.1545306
Veloso M, Biswas J, Coltin B, et al., 2015. Cobots: robust symbiotic autonomous mobile service robots. 24th Int Joint Conf on Artificial Intelligence, p.4423–4429.
Verma JK, Ranga V, 2021. Multi-robot coordination analysis, taxonomy, challenges and future scope. J Intell Rob Syst, 102:10. https://doi.org/10.1007/s10846-021-01378-2
Wu ZH, Pan SR, Chen FW, et al., 2021. A comprehensive survey on graph neural networks. IEEE Trans Neur Netw Learn Syst, 32(1):4–24. https://doi.org/10.1109/TNNLS.2020.2978386
Wurman PR, D’Andrea R, Mountz M, 2007. Coordinating hundreds of cooperative, autonomous vehicles in warehouses. Proc 19th National Conf on Innovative Applications of Artificial Intelligence, p.1752–1759.
Yu JJ, LaValle SM, 2013. Structure and intractability of optimal multi-robot path planning on graphs. Proc 27th AAAI Conf on Artificial Intelligence, p.1443–1449.
Zhou J, Cui GQ, Hu SD, et al., 2020. Graph neural networks: a review of methods and applications. AI Open, 1:57–81. https://doi.org/10.1016/j.aiopen.2021.01.001
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the National Natural Science Foundation of China (No. 62073183)
Contributors
Hanqi DAI and Weining LU designed the research. Hanqi DAI and Weining LU conducted the experiments and drafted the paper. Xianglong LI, Jun YANG, and Yanze LIU helped organize the paper. Deshan MENG, Yanze LIU, and Bin LIANG revised the paper. Hanqi DAI and Weining LU finalized the paper.
Compliance with ethics guidelines
Hanqi DAI, Weining LU, Xianglong LI, Jun YANG, Deshan MENG, Yanze LIU, and Bin LIANG declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Dai, H., Lu, W., Li, X. et al. Cooperative planning of multi-agent systems based on task-oriented knowledge fusion with graph neural networks. Front Inform Technol Electron Eng 23, 1069–1076 (2022). https://doi.org/10.1631/FITEE.2100597
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2100597