Bayesian-Based Search Decision Framework and Search Strategy Analysis in Probabilistic Search

In this paper, a sequence decision framework based on the Bayesian search is proposed to solve the problem of using an autonomous system to search for the missing target in an unknown environment. In the task, search cost and search efficiency are two competing requirements because they are closely related to the search task. Especially in the actual search task, the sensor assembled by the searcher is not perfect, so an effective search strategy is needed to guide the search agent to perform the task. Meanwhile, the decision-making method is crucial for the search agent. If the search agent fully trusts the feedback information of the sensor, the search task will end when the target is “detected” for the first time, which means it must take the risk of founding a wrong target. Conversely, if the search agent does not trust the feedback information of the sensor, it will most likely miss the real target, which will waste a lot of search resources and time. Based on the existing work, this paper proposes two search strategies and an improved algorithm. Compared with other search methods, the proposed strategies greatly improve the efficiency of unmanned search. Finally, the numerical simulations are provided to demonstrate the effectiveness of the search strategies.