Deep Reinforcement Learning Applied for Threat Engagement by Loyal Wingmen Drones - Aragão, Davi Guanabara De; Maximo, Marcos R. O. A.; Brancalion, José Fernando Basso (Anais do Simpósio Brasileiro de Robótica e Simpósio Latino Americano de Robótica (SBR/LARS); 2024: Anais do XVI Simpósio Brasileiro de Robótica e XV Workshop de Robótica na Educação, 2024)
This study explores using Loyal Wingmen drones in counter- UAV systems to detect and neutralize multiple aerial threats cooperatively. We employ Curriculum Learning and Bayesian Optimization to enhance the Proximal Policy Optimization algorithm, which trains a Deep Neural Network to perform high-level decision-making. The effectiveness of this network is assessed in simulations that pit it against an increasing number of Loitering Munitions, with a comparative analysis of a Behavior Tree agent. A key finding is the effective deployment of simulated LiDAR technology for dynamic multi-target detection, which surpasses traditional systems limited to observing a fixed number of entities.