Reachable Set Approximation as a Non-Cooperative Multi-Agent Coverage Game

被引:0
|
作者
Rajab, Fat-Hy Omar [1 ]
Shamma, Jeff S. [1 ]
机构
[1] Univ Illinois, Dept Ind Enterprise Syst Engn, Grainger Coll Engn, 117 Transportat Bldg,MC-238,104 S Mathews Ave, Urbana, IL 61801 USA
关键词
D O I
10.1109/CDC51059.2022.9992394
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We estimate the reachable set of a dynamical system by posing reachable set construction as a multi-agent coverage problem. As the terminology implies, the reachable set is the set of all states that can be reached within a specified time, using exogenous inputs with a specified bound, and starting from a specified initial condition. In multi-agent coverage, mobile agents self-deploy in an online manner to cover a region that is unknown a priori. The mapping between the two settings is the unknown region being the reachable set. Using time discretization and randomized spatial discretization, the proposed algorithm simultaneously generates a finite graph contained within the true reachable set and deploys the agents to optimally cover the graph. The utilized game-theoretic methods assure that, asymptotically, the agents self-deploy in a manner that provides optimal coverage with high probability. The accuracy of the approximation of the reachable set depends on the temporal and spacial discretization. The proposed algorithm is illustrated on different dynamical systems, where the performance is compared to related scenario-based approaches to reachable set estimation.
引用
收藏
页码:5351 / 5356
页数:6
相关论文
共 50 条
  • [21] Adaptive fault tolerant tracking control of heterogeneous multi-agent systems with non-cooperative target
    Dong, Lijing
    Liu, Kaige
    Du, Shengli
    Yan, Hao
    Shen, Haikuo
    INFORMATION SCIENCES, 2023, 622 : 1184 - 1195
  • [22] Multi-Agent Reinforcement Learning in Non-Cooperative Stochastic Games Using Large Language Models
    Alsadat, Shayan Meshkat
    Xu, Zhe
    IEEE CONTROL SYSTEMS LETTERS, 2024, 8 : 2757 - 2762
  • [23] NON-COOPERATIVE GAME APPROACH TO MULTI-ROBOT PLANNING
    Galuszka, Adam
    Swierniak, Andrzej
    INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE, 2005, 15 (03) : 359 - 367
  • [24] Multi-Agent Cooperative Area Coverage: Case Study Ploughing
    Janani, Alireza
    Alboul, Lyuba
    Penders, Jacques
    AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS, 2016, : 1397 - 1398
  • [25] Coverage control algorithm for wireless sensor networks based on non-cooperative game
    Liu H.
    Zhao H.
    Deng Y.
    Wang X.
    Yin R.
    Tongxin Xuebao/Journal on Communications, 2019, 40 (01): : 71 - 78
  • [26] Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach
    Nguyen, Khoi Khac
    Duong, Trung Q.
    Vien, Go Anh
    Le-Khac, Nhien-An
    Minh-Nghia Nguyen
    IEEE ACCESS, 2019, 7 : 100480 - 100490
  • [27] Cooperative Multi-agent Approach for Automated Computer Game Testing
    Shirzadeh-hajimahmood, Samira
    Prasteya, I. S. W. B.
    Dastani, Mehdi
    Dignum, Frank
    ENGINEERING MULTI-AGENT SYSTEMS, EMAS 2024, 2025, 15152 : 23 - 41
  • [28] Cooperative Game for the Roles Assignment of the Multi-agent Robot System
    Wei Na
    Liu Mingyong
    PROCEEDINGS OF 2018 IEEE 3RD ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC 2018), 2018, : 954 - 957
  • [29] APPROXIMATION OF A NASH EQUILIBRIUM POINT IN AN N-PERSON NON-COOPERATIVE GAME
    HANSEN, T
    ECONOMETRICA, 1971, 39 (04) : 222 - &
  • [30] Non-Cooperative Game for Capacity Offload
    Zhang, Feng
    Zhang, Wenyi
    Ling, Qiang
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2012, 11 (04) : 1565 - 1575