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A multistate network approach for reliability evaluation of unmanned swarms by considering information exchange capacity

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  • Xu, Bei
  • Liu, Tao
  • Bai, Guanghan
  • Tao, Junyong
  • Zhang, Yun-an
  • Fang, Yining

Abstract

An unmanned swarm is a group of numerous unmanned ground or aerial vehicles performing tasks in a self-organized and self-adaptive manner to achieve an overall mission objective. Efficient and reliable information exchange among the agents is essential for the unmanned swarm to accomplish the mission. Thus, it is important to provide an accurate reliability model and a corresponding efficient method of evaluation for the unmanned swarm information exchange network (USIEN). In this paper, we develop a multistate network model for the reliability evaluation of USIEN that considers the capacity for information exchange. We propose an overall reliability metric to evaluate the reliability of USIEN under certain missions. In addition, an improved fast state-space decomposition (SSD) bounding algorithm with a reliability threshold for approximating the proposed reliability metric is developed. Results and comparisons with extant studies show that the proposed model and metric lead to a more realistic method to evaluate the reliability of USIEN. Efficiency investigations indicate that the proposed algorithm provides an efficient approximation of reliability for the large unmanned swarm. The proposed model and metric can be used to support mission planning and the design of an unmanned swarm.

Suggested Citation

  • Xu, Bei & Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-an & Fang, Yining, 2022. "A multistate network approach for reliability evaluation of unmanned swarms by considering information exchange capacity," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:reensy:v:219:y:2022:i:c:s0951832021006992
    DOI: 10.1016/j.ress.2021.108221
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    References listed on IDEAS

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    Cited by:

    1. Forghani-elahabad, Majid & Yeh, Wei-Chang, 2022. "An improved algorithm for reliability evaluation of flow networks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Xu, Bei & Bai, Guanghan & Liu, Tao & Fang, Yining & Zhang, Yun-an & Tao, Junyong, 2023. "An improved swarm model with informed agents to prevent swarm-splitting," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    3. Liu, Tao & Bai, Guanghan & Tao, Junyong & Zhang, Yun-An & Fang, Yining & Xu, Bei, 2022. "Modeling and evaluation method for resilience analysis of multi-state networks," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Kong, Linghao & Wang, Lizhi & Cao, Zhongzheng & Wang, Xiaohong, 2024. "Resilience evaluation of UAV swarm considering resource supplementation," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    5. Liu, Lujie & Yang, Jun, 2023. "A dynamic mission abort policy for the swarm executing missions and its solution method by tailored deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    6. Zhang, Changzhen & Yang, Jun & Wang, Ning, 2023. "Timely reliability modeling and evaluation of wireless sensor networks with adaptive N-policy sleep scheduling," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    7. Li, Hongxu & Sun, Qin & Zhong, Yuanfu & Huang, Zhiwen & Zhang, Yingchao, 2023. "A soft resource optimization method for improving the resilience of UAV swarms under continuous attack," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Elena Zaitseva & Vitaly Levashenko & Ravil Mukhamediev & Nicolae Brinzei & Andriy Kovalenko & Adilkhan Symagulov, 2023. "Review of Reliability Assessment Methods of Drone Swarm (Fleet) and a New Importance Evaluation Based Method of Drone Swarm Structure Analysis," Mathematics, MDPI, vol. 11(11), pages 1-26, June.

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