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We study planning problems faced by robots operating in uncertainenvironments with incomplete knowledge of state, and actions that are noisyand/or imprecise. This paper identifies a new problem sub-class that modelssettings in which information is revealed only intermittently through someexogenous process that provides state information periodically. Severalpractical domains fit this model, including the specific scenario thatmotivates our research: autonomous navigation of a planetary exploration roveraugmented by remote imaging. With an eye to efficient specialized solutionmethods, we examine the structure of instances of this sub-class. They lead toMarkov Decision Processes with exponentially large action-spaces but for which,as those actions comprise sequences of more atomic elements, one may establishperformance bounds by comparing policies under different informationassumptions. This provides a way in which to construct performance boundssystematically. Such bounds are useful because, in conjunction with theinsights they confer, they can be employed in bounding-based methods to obtainhigh-quality solutions efficiently; the empirical results we presentdemonstrate their effectiveness for the considered problems. The foregoing hasalso alluded to the distinctive role that time plays for these problems -- morespecifically: time until information is revealed -- and we uncover and discussseveral interesting subtleties in this regard.

Planning under periodic observations: bounds and bounding-based solutions