论文标题
无人用搜索多个目标目标的不确定性
Uncertainty with UAV Search of Multiple Goal-oriented Targets
论文作者
论文摘要
本文考虑了一个无人机团队在不确定性下搜索目标的复杂问题。无人机团队的目标是在达到选定目标之前尽快找到所有移动目标。考虑的不确定性是三倍：首先，无人机不知道目标的位置和目的地。其次，无人机的传感功能并不完美。第三，目标运动模型尚不清楚。我们建议将熵和随机信仰的实时算法框架用于无人机，旨在优化对所有目标快速成功检测的可能性。我们已经经验评估了算法框架，并且与其他解决方案相比，它显示出其效率和显着的性能提高。此外，我们已经使用对等设计的代理（PDA）评估了我们的框架，该框架是模拟目标的计算机代理，并表明我们的算法框架在这种情况下优于其他解决方案。
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.
