发表机构
IRIDIA, Université libre de Bruxelles; Toyota Motor Europe（布鲁塞尔自由大学IRIDIA; 丰田汽车欧洲公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对机器人集群在无外部定位系统时建模空间场的问题，提出无位置感知高斯过程回归（LU-GPR），利用本地感知和通信推断场分布并达成共同参考系，实验表明其可扩展且鲁棒，并可用于人群流动估计。
AI 中文摘要
机器人集群凭借其去中心化架构，是空间场（如水温、风速或地形高程）可扩展且稳健建模的自然工具。然而，现有方法依赖于外部定位系统，使每个机器人能够确定其在空间中的自身位置。在此，我们引入无位置感知的高斯过程回归（LU-GPR），作为在缺乏此类定位系统情况下空间场建模的解决方案。LU-GPR允许每个机器人在仅使用本地感知和通信的同时，推断空间中场后验均值和方差，并与其同伴就共同参考系达成一致。我们提出一种在线算法，使每个机器人能够在其本地参考系收敛到共同参考系的过程中，一致地推断局部估计。通过专家乘积模型，每个机器人还将同伴的估计与自身估计相结合，以获得全局模型。我们的结果表明，LU-GPR随机器人数量扩展良好，并且对有限通信范围具有鲁棒性。我们还展示了如何在实际监控场景中使用它来估计疏散人群的流动。
英文摘要
Robot swarms, by virtue of their decentralised architecture, are a natural tool for scalable, robust modelling of spatial fields, such as water temperature, wind velocity, or terrain elevation. However, existing methods rely on external positioning systems that allow each robot to determine its own position in space. Here, we introduce location-unaware Gaussian process regression (LU-GPR) as a solution to the modelling of spatial fields in the absence of such positioning systems. LU-GPR allows each robot to infer the posterior mean and variance of the field in space, while simultaneously agreeing on a common frame of reference with its peers, using only local sensing and communication. We propose an online algorithm that allows each robot to consistently infer local estimates as its local frame of reference converges to the common one. By means of a product of experts model, each robot also combines the estimates of its peers with its own to obtain a global model. Our results show that LU-GPR scales well with the number of robots and is robust to limited communication ranges. We also demonstrate how it can be used in real-world monitoring scenarios to estimate the flow of an evacuating crowd.
