This paper considers the complex problem of a team of UAVs searching targets
under uncertainty. The goal of the UAV team is to find all of the moving
targets as quickly as possible before they arrive at their selected goal. The
uncertainty considered is threefold: First, the UAVs do not know the targets'
locations and destinations. Second, the sensing capabilities of the UAVs are
not perfect. Third, the targets' movement model is unknown. We suggest a
real-time algorithmic framework for the UAVs, combining entropy and
stochastic-temporal belief, that aims at optimizing the probability of a quick
and successful detection of all of the targets. We have empirically evaluated
the algorithmic framework, and have shown its efficiency and significant
performance improvement compared to other solutions. Furthermore, we have
evaluated our framework using Peer Designed Agents (PDAs), which are computer
agents that simulate targets and show that our algorithmic framework
outperforms other solutions in this scenario.

本文提出了一个基于实时算法框架的无人机小组搜索多个移动目标的方法，以优化快速及成功发现目标的概率，经过实验验证证明了该方法在不确定条件下的高效性和明显的优越性。