A key problem of robotic environmental sensing and monitoring is that of sensing: How can a team of robots plan the most informative observation to minimize the uncertainty in modeling and predicting an environmental? This paper presents two principled approaches to efficient-theoretic path planning based on entropy and mutual information for in situ active sensing of an important broad class of-occurring environmental phenomena called anisotropic fields. Our algorithms are novel in addressing a trade-off between active sensing and time efficiency. An important practical consequence is that our can exploit the spatial correlation structure of Gaussian-based anisotropic fields to improve time efficiency while preserving-optimal active sensing performance. We analyze the time complexity of our and prove analytically that they scale better than state-of-the-art with increasing planning horizon length. We provide theoretical on the active sensing performance of our algorithms for a class of tasks called transect sampling, which, in particular, can be with longer planning time and/or lower spatial correlation along the. Empirical evaluation on real-world anisotropic field data shows that algorithms can perform better or at least as well as the state-of-the-art while often incurring a few orders of magnitude less computational, even when the field conditions are less favorable.
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Cao et al. (2013) studied this question.