Uncertainty with UAV Search of Multiple Goal-oriented Targets

TL;DR

This paper presents a real-time algorithmic framework for UAV teams to efficiently locate multiple moving targets under uncertainty, improving detection speed and success rate compared to existing methods.

Contribution

The paper introduces a novel algorithmic framework combining entropy and stochastic belief for UAV target search under multiple uncertainties, with empirical validation.

Findings

Significant performance improvement over existing solutions

Effective in scenarios with unknown target locations and movements

Outperforms other methods in simulated target detection tasks

Abstract

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.