In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

A key problem of robotic environmental sensing and moni-<lb>toring is that of active sensing: How can a team of robots<lb>plan the most informative observation paths to minimize<lb>the uncertainty in modeling and predicting an environmen-<lb>tal phenomenon? This paper presents two principled ap-<lb>proaches to efficient information-theoretic path planning based<lb>on entropy and mutual information criteria for in situ ac-<lb>tive sensing of an important broad class of widely-occurring<lb>environmental phenomena called anisotropic fields. Our pro-<lb>posed algorithms are novel in addressing a trade-off between<lb>active sensing performance and time efficiency. An impor-<lb>tant practical consequence is that our algorithms can exploit<lb>the spatial correlation structure of Gaussian process-based<lb>anisotropic fields to improve time efficiency while preserv-<lb>ing near-optimal active sensing performance. We analyze<lb>the time complexity of our algorithms and prove analyti-<lb>cally that they scale better than state-of-the-art algorithms<lb>with increasing planning horizon length. We provide the-<lb>oretical guarantees on the active sensing performance of<lb>our algorithms for a class of exploration tasks called tran-<lb>sect sampling, which, in particular, can be improved with<lb>longer planning time and/or lower spatial correlation along<lb>the transect. Empirical evaluation on real-world anisotropic<lb>field data shows that our algorithms can perform better or<lb>at least as well as the state-of-the-art algorithms while of-<lb>ten incurring a few orders of magnitude less computational<lb>time, even when the field conditions are less favorable.
Multi-Robot Informative Path Planning for Active Sensing of Environmental Phenomena: A Tale of Two Algorithms