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We study the problem of information sampling with a group of mobile robots from an unknown environment. Each robot is given a unique region in the environment for the sampling task. The objective of the robots is to visit a subset of locations in the environment such that the collected information is maximized, and consequently, the underlying information model matches as close to reality as possible. The robots have limited communication ranges, and therefore can only communicate when nearby one another. The robots operate in a stochastic environment and their control uncertainty is handled using factored Decentralized Markov Decision Processes (Dec-MDP). When two or more robots communicate, they share their past noisy observations and use a Gaussian mixture model to update their local information models. This in turn helps them to obtain a better Dec-MDP policy. Simulation results show that our proposed strategy is able to predict the information model closer to the ground truth version than compared to other algorithms. Furthermore, the reduction in the overall uncertainty is more than comparable algorithms. 

One important class of applications entails a robot scrutinizing, monitoring, or recording the evolution of an uncertain time-extended process. This sort of situation leads to an interesting family of active perception problems that can be cast as planning problems in which the robot is limited in what it sees and must, thus, choose what to pay attention to. The distinguishing characteristic of this setting is that the robot has influence over what it captures via its sensors, but exercises no causal authority over the process evolving in the world. As such, the robot’s objective is to observe the underlying process and to produce a “chronicle” of occurrent events, subject to a goal specification of the sorts of event sequences that may be of interest. This paper examines variants of such problems in which the robot aims to collect sets of observations to meet a rich specification of their sequential structure. We study this class of problems by modeling a stochastic process via a variant of a hidden Markov model and specify the event sequences of interest as a regular language, developing a vocabulary of “mutators” that enable sophisticated requirements to be expressed. Under different suppositions on the information gleaned about the event model, we formulate and solve different planning problems. The core underlying idea is the construction of a product between the event model and a specification automaton. Using this product, we compute a policy that minimizes the expected number of steps to reach a goal state. We introduce a general algorithm for this problem as well as several more efficient algorithms for important special cases. The paper reports and compares performance metrics by drawing on some small case studies analyzed in depth via simulation. Specifically, we study the effect of the robot’s observation model on the average time required for the robot to record a desired story. We also compare our algorithm with a baseline greedy algorithm, showing that our algorithm outperforms the greedy algorithm in terms of the average time to record a desired story. In addition, experiments show that the algorithms tailored to specialized variants of the problem are rather more efficient than the general algorithm.