Horsevad Nikolaj, Kwa Hian Lee, Bouffanais Roland
University of Ottawa, Ottawa, ON, Canada.
Singapore University of Technology and Design, Singapore, Singapore.
Front Robot AI. 2022 Jun 20;9:865414. doi: 10.3389/frobt.2022.865414. eCollection 2022.
In the study of collective animal behavior, researchers usually rely on gathering empirical data from animals in the wild. While the data gathered can be highly accurate, researchers have limited control over both the test environment and the agents under study. Further aggravating the data gathering problem is the fact that empirical studies of animal groups typically involve a large number of conspecifics. In these groups, collective dynamics may occur over long periods of time interspersed with excessively rapid events such as collective evasive maneuvers following a predator's attack. All these factors stress the steep challenges faced by biologists seeking to uncover the fundamental mechanisms and functions of social organization in a given taxon. Here, we argue that beyond commonly used simulations, experiments with multi-robot systems offer a powerful toolkit to deepen our understanding of various forms of swarming and other social animal organizations. Indeed, the advances in multi-robot systems and swarm robotics over the past decade pave the way for the development of a new hybrid form of scientific investigation of social organization in biology. We believe that by fostering such interdisciplinary research, a feedback loop can be created where agent behaviors designed and tested can assist in identifying hypotheses worth being validated through the observation of animal collectives in nature. In turn, these observations can be used as a novel source of inspiration for even more innovative behaviors in engineered systems, thereby perpetuating the feedback loop.
在动物群体行为研究中，研究人员通常依靠从野外动物身上收集经验数据。虽然收集到的数据可能非常准确，但研究人员对测试环境和所研究的主体的控制有限。使数据收集问题进一步恶化的是，对动物群体的实证研究通常涉及大量同种个体。在这些群体中，集体动态可能会在很长一段时间内发生，其间穿插着诸如捕食者攻击后集体躲避动作等极其迅速的事件。所有这些因素都凸显了试图揭示特定分类群中社会组织的基本机制和功能的生物学家所面临的严峻挑战。在此，我们认为，除了常用的模拟方法外，多机器人系统实验提供了一个强大的工具包，可加深我们对各种形式的群体行为和其他社会性动物组织的理解。事实上，过去十年多机器人系统和群体机器人技术的进步为生物学中一种新的混合形式的社会组织科学研究的发展铺平了道路。我们相信，通过促进这种跨学科研究，可以创建一个反馈循环，在这个循环中，设计和测试的主体行为可以帮助识别值得通过观察自然界中的动物群体来验证的假设。反过来，这些观察结果可以作为一种新的灵感来源，为工程系统中更具创新性的行为提供灵感，从而使反馈循环持续下去。
