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This paper surveys recent results in pursuitevasion and autonomous search relevant to applications in mobile robotics. We provide a taxonomy of search problems that highlights the differences resulting from varying assumptions on the searchers, targets, and the environment. We then list a number of fundamental results in the areas of pursuit-evasion and probabilistic search, and we discuss field implementations on mobile robotic systems. In addition, we highlight current open problems in the area and explore avenues for future work.

In this paper, we consider the problem of active sensing using mobile nodes as a sensor network to estimate the state of a dynamic target. We propose a gradient-searchbased decentralized algorithm that demonstrates the benefits of distributed sensing. We then examine the task of tracking multiple targets, and address it via a simple extension to our algorithm. Simulation results show that our simple decentralized approach performs quite well and leads to interesting cooperative behavior.

Abstract-This paper presents the search problem formulated as a decision problem, where the searcher decides whether the target is present in the search region, and if so, where it is located. Such decision-based search tasks are relevant to many research areas, including mobile robot missions, visual search and attention, and event detection in sensor networks. The effect of control strategies in search problems on decisionmaking quantities, namely time-to-decision, is investigated in this work. We present a Bayesian framework in which the objective is to improve the decision, rather than the sensing, using different control policies. Furthermore, derivations of closed-form expressions governing the evolution of the belief function are also presented. As this framework enables the study and comparison of the role of control for decision-making applications, the derived theoretical results provide greater insight into the sequential processing of decisions. Numerical studies are presented to verify and demonstrate these results. I. INTRODUCTIONThe goal in a search problem is to generate the search paths in uncertain environments that best enable the searcher to locate a target (perhaps among other objects) using one or more mobile sensor platforms, possibly under resource constraints. Inspired by the seminal works of B. Koopman [1] and L.D. Stone [2], this physical search theory has been extensively developed.Beyond the search for targets using mobile sensors, however, the search problem can be generalized to represent the class of problems where choice of observations is controlled to best search for an object or outcome. For instance, the scheduling of individual sensor nodes in a wireless sensor network or the selection of the focus-of-attention in visual systems can also be formulated as search problems, where a control policy is generated to improve the information obtained by observations. This paper presents a formal framework which casts the search problem as a decision between hypotheses about the decision-maker's current knowledge. In the specific case typically considered in the physical search theory literature, the decision reflects a belief of whether or not the target of interest is present in the search region. However, our general formulation allows for analysis of the belief evolution as more observations are taken, as well as the definition of measures of the quality of the decision, such as performance and robustness of the decision-making process.Given its relevance to a variety of autonomous, mobile sensor applications, search theory has been studied extensively in the literature. In addition to the defining works mentioned previously, [3] discusses various components of the search problem, including classes of search paths, models

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