The rapid advancements in unmanned aerial vehicles (UAVs) have unlocked numerous applications, including environmental monitoring, disaster response, and agricultural surveying. Enhancing the collective behavior of multiple decentralized UAVs can significantly improve these applications through more efficient and coordinated operations. In this study, we explore a Recurrent PPO model for target localization in perceptually degraded environments like places without GNSS/GPS signals. We first developed a single-drone approach for target identification, followed by a decentralized two-drone model. Our approach can utilize two types of sensors on the UAVs, a detection sensor and a target signal sensor. The single-drone model achieved an accuracy of 93%, while the two-drone model achieved an accuracy of 86%, with the latter requiring fewer average steps to locate the target. This demonstrates the potential of our method in UAV swarms, offering efficient and effective localization of radiant targets in complex environmental conditions.

翻译：无人机技术的快速发展催生了众多应用，包括环境监测、灾害响应和农业勘测。通过更高效协调的作业，增强多个去中心化无人机的群体行为可显著提升这些应用的性能。本研究探索了一种循环PPO模型，用于在感知退化环境（如无GNSS/GPS信号的区域）中进行目标定位。我们首先开发了单无人机目标识别方法，随后构建了去中心化双无人机模型。该方法可利用无人机搭载的两类传感器：探测传感器与目标信号传感器。单无人机模型实现了93%的准确率，双无人机模型达到86%的准确率，且后者定位目标所需的平均步数更少。这证明了我们的方法在无人机集群中的应用潜力，能够在复杂环境条件下实现对辐射目标的高效定位。