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Learning-based formal synthesis of cooperative multi-agent systems with an application to robotic coordination | IEEE Conference Publication | IEEE Xplore

Learning-based formal synthesis of cooperative multi-agent systems with an application to robotic coordination


Abstract:

In our previous work [4], we proposed a learning-based formal top-down design framework to automatically synthesize coordination and control strategies for cooperative mu...Show More

Abstract:

In our previous work [4], we proposed a learning-based formal top-down design framework to automatically synthesize coordination and control strategies for cooperative multi-agent systems. Our main idea is to decompose a given team mission into individual local tasks and synthesizing local supervisors while guarantee the multi-agent performance by incorporating supervisor synthesis with compositional verification techniques. In this paper, we apply the top-down design framework to a multi-robot scenario that involves both request-response services and multi-robot coordination. Modified L* algorithms are adapted to both the local synthesis and the compositional verification to ensure that the collective behavior of the robots will eventually guarantee the satisfaction of the global specification. Computational and software tools are developed to integrate automatic supervisor synthesis and interrobot communication.
Date of Conference: 21-24 June 2016
Date Added to IEEE Xplore: 08 August 2016
ISBN Information:
Conference Location: Athens, Greece
Citations are not available for this document.

I. Introduction

Cooperative multi-agent systems, which consist of a team of spatially-distributed agents collaborating via communication to achieve desirable team tasks, represent a typical class of cyber-physical systems (CPS), and have shown great potential in both academic and industrial applications, ranging from transportation networks to robotic systems in recent years [3] [7] [10] [14] [15] [19].

Cites in Papers - |

Cites in Papers - IEEE (6)

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1.
Huimin Zhang, Lei Feng, Zhiwu Li, "Control of Black-Box Embedded Systems by Integrating Automaton Learning and Supervisory Control Theory of Discrete-Event Systems", IEEE Transactions on Automation Science and Engineering, vol.17, no.1, pp.361-374, 2020.
2.
Huanfei Zheng, Yue Wang, "A Distributed Framework for Dynamic Task Allocation of Multi-Robot Symbolic Motion Planning", 2019 American Control Conference (ACC), pp.3291-3296, 2019.
3.
Ryo Toyota, Toru Namerikawa, "Event-Triggered Formation Control of a Generalized Multi-Agent System", 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp.940-945, 2018.
4.
Huimin Zhang, Lei Feng, Naiqi Wu, Zhiwu Li, "Integration of Learning-Based Testing and Supervisory Control for Requirements Conformance of Black-Box Reactive Systems", IEEE Transactions on Automation Science and Engineering, vol.15, no.1, pp.2-15, 2018.
5.
Ryo Toyota, Toru Namerikawa, "Formation control of multi-agent system considering obstacle avoidance", 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pp.446-451, 2017.
6.
Kimon P. Valavanis, "Unmanned Aircraft Systems challenges in design for autonomy", 2017 11th International Workshop on Robot Motion and Control (RoMoCo), pp.73-86, 2017.

Cites in Papers - Other Publishers (4)

1.
Luke Antonyshyn, Jefferson Silveira, Sidney Givigi, Joshua Marshall, "Multiple Mobile Robot Task and Motion Planning: A Survey", ACM Computing Surveys, vol.55, no.10, pp.1, 2023.
2.
Huanfei Zheng, Yue Wang, "Parallel decomposition and concurrent satisfaction for heterogeneous multi-robot task and motion planning under temporal logic specifications", Discrete Event Dynamic Systems, 2021.
3.
Adeel Zaidi, Muhammad Kazim, Rui Weng, Dongzhe Wang, Xu Zhang, "Distributed Observer-Based Leader Following Consensus Tracking Protocol for a Swarm of Drones", Journal of Intelligent & Robotic Systems, vol.102, no.3, 2021.
4.
Andrea Casalino, Filippo Cividini, Andrea Maria Zanchettin, Luigi Piroddi, Paolo Rocco, "Human-robot collaborative assembly: a use-case application", IFAC-PapersOnLine, vol.51, no.11, pp.194, 2018.
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References

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