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.
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。
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Zhenxin MU designed the survey and drafted the paper. Junzhi YU guided the survey and edited the paper. Jie PAN and Junzhi YU helped organize the paper. Ziye ZHOU provided his experience on collective motion and made suggestions to improve the presentation of the paper. Lu CAO and Junzhi YU revised and finalized the paper.
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Zhenxin MU, Jie PAN, Ziye ZHOU, Junzhi YU, and Lu CAO declare that they have no conflict of interest.
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Project supported by the National Natural Science Foundation of China (Nos. U1909206, T2121002, 61903007, and 11972373)
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Mu, Z., Pan, J., Zhou, Z. et al. A survey of the pursuit–evasion problem in swarm intelligence. Front Inform Technol Electron Eng 24, 1093–1116 (2023). https://doi.org/10.1631/FITEE.2200590
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DOI: https://doi.org/10.1631/FITEE.2200590