Abstract
The increased deployment of multi-robot systems (MRS) in various fields has led to the need for analysis of system-level performance. However, creating consistent metrics for MRS is challenging due to the wide range of system and environmental factors, such as team size and environment size. This paper presents a new analytical framework for MRS based on dimensionless variable analysis, a mathematical technique typically used to simplify complex physical systems. This approach effectively condenses the complex parameters influencing MRS performance into a manageable set of dimensionless variables. We form dimensionless variables which encapsulate key parameters of the robot team and task. Then we use these dimensionless variables to fit a parametric model of team performance. Our model successfully identifies critical performance determinants and their interdependencies, providing insight for MRS design and optimization. The application of dimensionless variable analysis to MRS offers a promising method for MRS analysis that effectively reduces complexity, enhances comprehension of system behaviors, and informs the design and management of future MRS deployments.
Abstract (translated)
多机器人系统（MRS）在各种领域的广泛应用导致了系统级性能分析的需求。然而，为MRS创建一致的度量标准具有挑战性，由于涉及系统大小和环境大小的广泛范围因素。本文基于无度变量分析（维度无关变量分析）提出了一种新的MRS分析框架，这是一种通常用于简化复杂物理系统的数学技术。这种方法有效地将影响MRS性能的复杂参数压缩成可管理的一组无度变量。我们创建了包含机器人团队和任务关键参数的无度变量。然后，我们使用这些无度变量来拟合一个参数模型，该模型成功识别了关键绩效决定因素及其相互依赖关系，为MRS设计和优化提供了洞察。将维度无关变量分析应用于MRS提供了一种有前途的MRS分析方法，有效减少了复杂性，增强了系统行为的理解，并为未来MRS部署的设计和管理提供了指导。
URL
https://arxiv.org/abs/2405.01771
