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Heterogeneous multi-robot sensing systems are able to characterize physicalprocesses more comprehensively than homogeneous systems. Access to multiplemodalities of sensory data allow such systems to fuse information betweencomplementary sources and learn richer representations of a phenomenon ofinterest. Often, these data are correlated but vary in fidelity, i.e., accuracy(bias) and precision (noise). Low-fidelity data may be more plentiful, whilehigh-fidelity data may be more trustworthy. In this paper, we address theproblem of multi-robot online estimation and coverage control by combining low-and high-fidelity data to learn and cover a sensory function of interest. Wepropose two algorithms for this task of heterogeneous learning and coverage --namely Stochastic Sequencing of Multi-fidelity Learning and Coverage (SMLC) andDeterministic Sequencing of Multi-fidelity Learning and Coverage (DMLC) -- andprove that they converge asymptotically. In addition, we demonstrate theempirical efficacy of SMLC and DMLC through numerical simulations.

Online Estimation and Coverage Control with Heterogeneous Sensing Information