DECISION-MAKING HETEROGENEOUS UAV SWARM SYSTEM WITH NEURAL NETWORK-ENHANCED REINFORCEMENT LEARNING

Abstract

This article explores how artificial intelligence and automation are significantly impacting unmanned aerial vehicles (UAVs), moving from traditional roles to versatile applications. The paper addresses the problem of optimizing the composition of a UAV swarm for efficient task execution by proposing an expert decision-making system that integrates neural networks and reinforcement learning. This system dynamically selects the optimal configuration for heterogeneous UAV swarms, in particular, for searching for objects in unfamiliar terrain. In the experimental phase, an advanced level of system was implemented by combining neural networks and reinforcement learning, based on role-based and MADDPG algorithms for heterogeneous UAV swarms. Decentralized information fusion-based swarm decision making algorithm (IFDSDA) is presented to overcome communication obstacles. The experiment presents a concept for improving heterogeneous UAV swarms using a neural decision network based on reinforcement learning. The environment is represented by a three-dimensional space with objects to be searched in random locations. The neural network evolves its decision-making strategy during training episodes, having an architecture with an input layer that processes information about the UAV’s state, hidden layers, and an output layer that influences the swarm’s behavior. The paper describes the process of direct propagation, reward-based weight adjustment, and the role of the output layer in determining collective actions. The results demonstrate the effective distribution of UAV types by the swarm based on a neural network, reducing redundancy and resource waste, thereby increasing overall efficiency. The article highlights the optimal solution obtained during the experiment, accompanied by a visual representation of the reward results.