A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO

TL;DR

This paper presents a scalable decentralized reinforcement learning framework using Recurrent PPO for UAV target localization in challenging environments, demonstrating high accuracy and efficiency in multi-drone coordination.

Contribution

The study introduces a novel decentralized Recurrent PPO approach for UAV target localization, effective in environments without GPS signals, with a two-drone model reducing localization steps.

Findings

Single-drone accuracy of 93%

Two-drone accuracy of 86% with fewer steps

Effective in GPS-denied environments

Abstract

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.