多机器人系统中异质性对群体行为的影响研究
Impact of Heterogeneity in Multi-Robot Systems on Collective Behaviors Studied Using a Search and Rescue Problem
摘要：
自然界中许多物种表现出共生关系，通过合作导致涌现行为，这有时超出了同一物种内的伙伴关系的范围。这些共生关系根据所涉及的利益水平分为互惠共生、共生和寄生。虽然这些伙伴关系是无处不在的性质，它是必要的，以了解设计异构多机器人系统（HMRS）的集体行为的好处。在本文中，我们研究的HMRS应用到搜索和救援问题的性能异质性的影响。由搜索者和救援者组成的组在个体机器人行为中具有多种程度的功能重叠和组组成，表现出不同程度的异质性。我们提出了一种新的技术，通过使用行为树来衡量代理的异质性，并使用它来获得异质性信息从我们的蒙特卡洛模拟。结果表明，在大多数情况下，该集团的异质性措施和救援效率之间存在正相关关系，证明了效益。然而，我们也看到的情况下，异质性可能会妨碍组的能力，指出需要确定最佳的异质性组所需的最大限度地受益于HMRS在现实世界中的应用。
英文摘要：
Many species in nature demonstrate symbiotic relationships leading to emergent behaviors through cooperation, which are sometimes beyond the scope of the partnerships within the same species. These symbiotic relationships are classified as mutualism, commensalism, and parasitism based on the benefit levels involved. While these partnerships are ubiquitous in nature, it is imperative to understand the benefits of collective behaviors in designing heterogeneous multi-robot systems (HMRS). In this paper, we investigate the impact of heterogeneity on the performance of HMRS applied to a search and rescue problem. The groups consisting of searchers and rescuers, varied in the individual robot behaviors with multiple degrees of functionality overlap and group compositions, demonstrating various levels of heterogeneity. We propose a new technique to measure heterogeneity in the agents through the use of Behavior Trees and use it to obtain heterogeneity informatics from our Monte Carlo simulations. The results show a positive correlation between the group's heterogeneity measure and the rescue efficiency demonstrating benefits in most of the scenarios. However, we also see cases where heterogeneity may hamper the group's abilities pointing to the need for determining the optimal heterogeneity in group required to maximally benefit from HMRS in real-world applications.
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