多机器人自适应覆盖控制，实时追踪动态变化的环境分布
Distributed Coverage Control for Time-Varying Spatial Processes
- 基于高斯过程的分布式策略，动态平衡探索与覆盖
- 每台机器人仅依赖自身和邻近数据，实现局部自主决策
- 通过筛选关键样本降低计算负担，适合真实环境部署
多机器人系统在环境监测中至关重要，尤其适用于追踪污染、土壤矿物质和水盐度等空间现象。本文针对密度分布未知且随时间变化的场景，提出一种完全分布式的覆盖控制策略。该策略利用高斯过程建模空间场，动态平衡学习场信息与最优覆盖之间的权衡。不同于现有方法，本方案考虑更贴近现实的时间变异性，使探索-利用权衡随时间动态调整。每台机器人仅使用自身采集的数据及邻近机器人共享的信息进行本地决策。为应对高斯过程的计算瓶颈，算法通过选择最具代表性样本，高效管理数据量。通过多组仿真与实验，结合真实世界数据现象，验证了该算法的有效性。
原文摘要 · Abstract (English)
Multi-robot systems are essential for environmental monitoring, particularly for tracking spatial phenomena like pollution, soil minerals, and water salinity, and more. This study addresses the challenge of deploying a multi-robot team for optimal coverage in environments where the density distribution, describing areas of interest, is unknown and changes over time. We propose a fully distributed control strategy that uses Gaussian Processes (GPs) to model the spatial field and balance the trade-off between learning the field and optimally covering it. Unlike existing approaches, we address a more realistic scenario by handling time-varying spatial fields, where the exploration-exploitation trade-off is dynamically adjusted over time. Each robot operates locally, using only its own collected data and the information shared by the neighboring robots. To address the computational limits of GPs, the algorithm efficiently manages the volume of data by selecting only the most relevant samples for the process estimation. The performance of the proposed algorithm is evaluated through several simulations and experiments, incorporating real-world data phenomena to validate its effectiveness.
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