Valianti, Panayiota (University of Cyprus), Malialis, Kleanthis (University of Cyprus), Kolios, Panayiotis (University of Cyprus), Ellinas, Georgios (University of Cyprus)

Multi-Agent Reinforcement Learning for Multiple Rogue Drone Interception

Scheduled for presentation during the Regular Session "Autonomy" (FrA3), Friday, June 9, 2023, 11:10−11:30, Room 464

2023 International Conference on Unmanned Aircraft Systems (ICUAS), June 6-9, 2023, Lazarski University, Warsaw, Poland

This information is tentative and subject to change. Compiled on April 18, 2024

Abstract

Unmanned aerial vehicles (UAVs) are increasingly being utilized for a wide variety of applications. However, malicious or illegal UAV (drone) activity poses great challenges for public safety. To address such challenges, this work proposes a framework based on reinforcement learning (RL) in which multiple UAVs cooperatively jam multiple rogue drones in flight in order to safely disable their operation. The main objective is to select mobility and power level control actions for each UAV to best jam the rogue drones, while also accounting for the interference power received by surrounding communication systems. Simulation experiments are conducted to evaluate the performance of the proposed approach, demonstrating its effectiveness and advantages as compared to a centralized solution.