ランダム特徴ガウス過程を用いた未知場のオンライン学習と被覆制御
Online Learning and Coverage of Unknown Fields Using Random-Feature Gaussian Processes
複数ロボットが未知で時間変化する密度場をオンラインで学習しながらボロノイ被覆制御で効率的にカバーする枠組みを提案し、後悔解析による理論保証と実機実験を示した。
詳しい要約
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著者: Ruijie Du, Ruoyu Lin, Yanning Shen, Magnus Egerstedt
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
