Abstract
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.
Highlights
Unmanned search and rescue is a highly autonomous task and there are many cases of such spatial search problems [1,2,3], such as resource exploration, sea fishing, border patrols, search fugitive, and troubleshooting
Due to the limitation of sensor accuracy and complex external interference, search agents cannot always obtain the correct information; the search agent can update the status of a target location by collecting and processing incomplete observations, an appropriate search strategy is still needed to guide it when and where to detect [7, 8]
The performance parameters of the search strategy are obtained by Monte Carlo simulation
Summary
Unmanned search and rescue is a highly autonomous task and there are many cases of such spatial search problems [1,2,3], such as resource exploration, sea fishing, border patrols, search fugitive, and troubleshooting. In order to solve the above problem, this paper proposes a Bayesian-based search decision framework and two adaptive strategies to guide the search agents to find a static target in an unknown place as soon as possible [9, 10]. In order to compare the impact of different strategies on the search process, a Bayesian-based search framework is proposed in [20], which provides a platform for comparison of search methods. (ii) In this paper, the evolution expression of sequence decision is derived, and a Bayesian-based search decision framework is proposed to deal with the incomplete information detected by the search agent.
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