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Received: 2022-11-23

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Junzhi YU

https://orcid.org/0000-0002-6347-572X

Lu CAO

https://orcid.org/0000-0001-5893-5085

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.8 P.1093-1116

http://doi.org/10.1631/FITEE.2200590


A survey of the pursuit–evasion problem in swarm intelligence


Author(s):  Zhenxin MU, Jie PAN, Ziye ZHOU, Junzhi YU, Lu CAO

Affiliation(s):  State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China; more

Corresponding email(s):   junzhi.yu@ia.ac.cn, yujunzhi@pku.edu.cn, caolu_space2015@163.com

Key Words:  Swarm behavior, Pursuit–, evasion, Artificial systems, Biological model, Collective motion


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Zhenxin MU, Jie PAN, Ziye ZHOU, Junzhi YU, Lu CAO. A survey of the pursuit–evasion problem in swarm intelligence[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1093-1116.

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Abstract: 
For complex functions to emerge in artificial systems, it is important to understand the intrinsic mechanisms of biological swarm behaviors in nature. In this paper, we present a comprehensive survey of pursuit–;evasion, which is a critical problem in biological groups. First, we review the problem of pursuit–;evasion from three different perspectives: game theory, control theory and artificial intelligence, and bio-inspired perspectives. Then we provide an overview of the research on pursuit–;evasion problems in biological systems and artificial systems. We summarize predator pursuit behavior and prey evasion behavior as predator–prey behavior. Next, we analyze the application of pursuit–;evasion in artificial systems from three perspectives, i.e., strong pursuer group vs. weak evader group, weak pursuer group vs. strong evader group, and equal-ability group. Finally, relevant prospects for future pursuit–;evasion challenges are discussed. This survey provides new insights into the design of multi-agent and multi-robot systems to complete complex hunting tasks in uncertain dynamic scenarios.

群体智能中的追逃围捕问题综述

穆祯鑫1,2,潘杰1,周子烨1,喻俊志1,曹璐2
1北京大学工学院先进制造与机器人系湍流与复杂系统国家重点实验室,中国北京市,100871
2国防科技创新研究院,中国北京市,100071
摘要:对于人工系统中涌现出的复杂功能,理解自然界中生物群体行为的内在机制至关重要。本文对生物集群中的一个关键问题-追逃围捕问题进行了全面的综述。首先,从博弈论、控制论与人工智能、生物启发3个不同视角对追逃围捕问题进行了回顾。然后,概述了生物系统和人工系统中追逃围捕问题研究进展,其中捕食者的追捕行为和猎物的逃避行为被概括为捕食者-猎物行为。之后,分别从强追捕者组vs.弱逃避者组、弱追捕者组vs.强逃避者组、相同能力组3个角度分析追逃围捕问题在人工系统中的应用。最后,讨论了未来追逃围捕问题面临的挑战和发展展望。本文为多智能体系统和多机器人系统在不确定动态场景下完成复杂狩猎任务的设计提供了新的见解。

关键词:群体行为;追逃问题;人工系统;生物模型;群集运动

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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