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Research into several aspects of robot-enabled reconnaissance of randomfields is reported. The work has two major components: the underlying theory ofinformation acquisition in the exploration of unknown fields and the results ofexperiments on how humans use sensor-equipped robots to perform a simulatedreconnaissance exercise.  The theoretical framework reported herein extends work on robotic explorationthat has been reported by ourselves and others. Several new figures of meritfor evaluating exploration strategies are proposed and compared. Using conceptsfrom differential topology and information theory, we develop the theoreticalfoundation of search strategies aimed at rapid discovery of topologicalfeatures (locations of critical points and critical level sets) of a prioriunknown differentiable random fields. The theory enables study of efficientreconnaissance strategies in which the tradeoff between speed and accuracy canbe understood. The proposed approach to rapid discovery of topological featureshas led in a natural way to to the creation of parsimonious reconnaissanceroutines that do not rely on any prior knowledge of the environment. The designof topology-guided search protocols uses a mathematical framework thatquantifies the relationship between what is discovered and what remains to bediscovered. The quantification rests on an information theory inspired modelwhose properties allow us to treat search as a problem in optimal informationacquisition. A central theme in this approach is that "conservative" and"aggressive" search strategies can be precisely defined, and search decisionsregarding "exploration" vs. "exploitation" choices are informed by the rate atwhich the information metric is changing.

Decision Making for Rapid Information Acquisition in the Reconnaissance of Random Fields