Leader-follower UAVs formation control based on a deep Q-network collaborative framework

This study examines a collaborative framework that utilizes an intelligent deep Q-network to regulate the formation of leader-follower Unmanned Aerial Vehicles (UAVs). The aim is to tackle the challenges posed by the highly dynamic and uncertain flight environment of UAVs. In the context of UAVs, we have developed a dynamic model that captures the collective state of the system. This model encompasses variables like as the relative positions, heading angle, rolling angle, and velocity of different nodes in the formation. In the subsequent section, we elucidate the operational procedure of UAVs in a collaborative manner, employing the conceptual framework of Markov Decision Process (MDP). Furthermore, we employ the Reinforcement Learning (RL) to facilitate this process. In light of this premise, a fundamental framework is presented for addressing the control problem of UAVs utilizing the DQN scheme. This framework encompasses a technique for action selection known as epsilon\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document}-imitation, as well as algorithmic specifics. Finally, the efficacy and portability of the DQN-based approach are substantiated by numerical simulation validation. The average reward curve demonstrates a satisfactory level of convergence, and kinematic link between the nodes inside the formation satisfies the essential requirements for the creation of a controller.