Online Estimation and Coverage Control with Heterogeneous Sensing Information
This addresses the challenge of efficiently using heterogeneous sensing data in multi-robot systems for improved environmental monitoring, but it appears incremental as it builds on existing coverage control methods with a focus on data fusion.
The paper tackles the problem of multi-robot online estimation and coverage control by combining low- and high-fidelity data to learn and cover a sensory function, proposing two algorithms (SMLC and DMLC) that are proven to converge asymptotically and demonstrated through numerical simulations.
Heterogeneous multi-robot sensing systems are able to characterize physical processes more comprehensively than homogeneous systems. Access to multiple modalities of sensory data allow such systems to fuse information between complementary sources and learn richer representations of a phenomenon of interest. Often, these data are correlated but vary in fidelity, i.e., accuracy (bias) and precision (noise). Low-fidelity data may be more plentiful, while high-fidelity data may be more trustworthy. In this paper, we address the problem of multi-robot online estimation and coverage control by combining low- and high-fidelity data to learn and cover a sensory function of interest. We propose two algorithms for this task of heterogeneous learning and coverage -- namely Stochastic Sequencing of Multi-fidelity Learning and Coverage (SMLC) and Deterministic Sequencing of Multi-fidelity Learning and Coverage (DMLC) -- and prove that they converge asymptotically. In addition, we demonstrate the empirical efficacy of SMLC and DMLC through numerical simulations.