多机器人在线学习未知场并高效覆盖，无需存储历史数据。
Online Learning and Coverage of Unknown Fields Using Random-Feature Gaussian Processes
- 用随机特征高斯过程实现增量更新，支持在线推理。
- 在时变环境中实现渐近无遗憾的覆盖性能，理论可证明。
- 适合动态环境下的多机器人协同感知与覆盖任务。
本文提出一种多机器人系统框架，用于同时学习和覆盖由未知且可能随时间变化的密度函数表征的区域。为克服传统高斯过程回归的局限，采用随机特征高斯过程（RFGP）及其在线版本（O-RFGP），支持在线与增量推断。结合基于Voronoi的覆盖控制与上置信界（UCB）采样策略，机器人团队能自适应聚焦关键区域，同时精化空间场建模以实现高效覆盖。O-RFGP的增量更新机制天然支持时变环境，可在不保留历史数据的前提下实现高效适应。据我们所知，首次通过基于损失函数的分析框架，建立了时不变设置下的渐近无遗憾保证。仿真与物理实验验证了该框架在时不变和时变密度函数下的有效性。
原文摘要 · Abstract (English)
This paper proposes a framework for multi-robot systems to perform simultaneous learning and coverage of a domain of interest characterized by an unknown and potentially time-varying density function. To overcome the limitations of Gaussian Process (GP) regression, we employ Random Feature GP (RFGP) and its online variant (O-RFGP) which enables online and incremental inference. By integrating these with Voronoi-based coverage control and Upper Confidence Bound (UCB) sampling strategy, a team of robots can adaptively focus on important regions while refining the learned spatial field for efficient coverage. The incremental update mechanism of O-RFGP naturally supports time-varying environments, allowing efficient adaptation without retaining historical data. Furthermore, to the best of our knowledge, we provide the first theoretical analysis of online learning and coverage through a regret-based formulation, establishing asymptotic no-regret guarantees in the time-invariant setting. The effectiveness of the proposed framework is demonstrated through simulations with both time-invariant and time-varying density functions, along with a physical experiment with a time-varying density function.
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