We study the problem of distributed multi-robot coverage over an unknown, nonuniform sensory field. Modeling the sensory field as a realization of a Gaussian Process and using Bayesian techniques, we devise a policy which aims to balance the tradeoff between learning the sensory function and covering the environment. We propose an adaptive coverage algorithm called Deterministic Sequencing of Learning and Coverage (DSLC) that schedules learning and coverage epochs such that its emphasis gradually shifts from exploration to exploitation while never fully ceasing to learn. Using a novel definition of coverage regret which characterizes overall coverage performance of a multi-robot team over a time horizon $T$, we analyze DSLC to provide an upper bound on expected cumulative coverage regret. Finally, we illustrate the empirical performance of the algorithm through simulations of the coverage task over an unknown distribution of wildfires.
翻译:我们研究了在未知、非统一感官场上分布多机器人覆盖的问题。将感官场建模成高山进程,并使用巴耶斯技术,我们设计了一种政策,旨在平衡学习感官功能和覆盖环境之间的权衡。我们建议采用适应性覆盖算法,称为“学习和覆盖的决定因素(DSLC) ” (DSLC), 将学习和覆盖的阶段安排成一个适应性覆盖算法,使其重点从探索逐渐转向开发,同时永不停止学习。我们使用新颖的覆盖遗憾定义,将多机器人团队在时间跨度上的总体覆盖性能定性为$T$,我们分析DSLC,以提供预期累积覆盖的遗憾的上限。最后,我们通过对未知野火分布的覆盖任务进行模拟,来说明算法的经验性表现。