一种用于无人机目标定位的可扩展去中心化强化学习框架：基于循环PPO
A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO
AI总结：
针对感知退化环境下的无人机目标定位问题，提出基于Recurrent PPO的去中心化强化学习框架，支持单/双无人机模式，双机模式虽准确率略低但定位步数更少，为无人机集群目标定位提供了高效方案。
摘要：
无人机（UAV）技术的快速发展催生了环境监测、灾害响应和农业勘测等众多应用。提升多架去中心化无人机的集群行为，可通过更高效的协同作业显著优化这些应用场景。本研究针对无GNSS/GPS信号等感知退化环境下的目标定位任务，探索了Recurrent PPO（近端策略优化）模型。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：检测传感器与目标信号传感器。单无人机模型准确率达93%，双无人机模型准确率达86%，且后者定位目标所需的平均步数更少。这证明了本方法在无人机集群中的应用潜力，可在复杂环境条件下高效精准地定位辐射型目标。
英文摘要：
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.
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