Multi-Robot Gaussian Process Estimation and Coverage: A Deterministic Sequencing Algorithm and Regret Analysis
This work addresses coverage planning for multi-robot teams in uncertain environments, such as monitoring wildfires, and is incremental as it builds on Gaussian Process modeling and regret analysis.
The paper tackled the problem of distributed multi-robot coverage over an unknown, nonuniform sensory field by proposing the Deterministic Sequencing of Learning and Coverage (DSLC) algorithm, which balances learning and coverage to achieve an upper bound on expected cumulative coverage regret, with empirical validation in wildfire distribution simulations.
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.