A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO
This addresses efficient coordination for UAV swarms in applications like disaster response, though it appears incremental as it builds on existing PPO methods.
The paper tackled target localization for UAVs in degraded environments like GPS-denied areas using a Recurrent PPO model, achieving 93% accuracy with a single drone and 86% with a decentralized two-drone approach that required fewer steps.
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.