Online Estimation and Coverage Control with Heterogeneous Sensing Information

TL;DR

This paper introduces algorithms for multi-robot systems to effectively combine low- and high-fidelity sensory data for improved online estimation and coverage of physical phenomena, with proven convergence and demonstrated effectiveness.

Contribution

It proposes two novel algorithms, SMLC and DMLC, for heterogeneous multi-fidelity learning and coverage control, with theoretical convergence guarantees and empirical validation.

Findings

Algorithms converge asymptotically.

Effective integration of heterogeneous data improves estimation.

Numerical simulations demonstrate practical efficacy.

Abstract

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.