Utilizing Deep Reinforcement Learning to Control UAV Movement for Environmental Monitoring

Abstract

Unmanned aerial vehicles (UAVs) are increasingly used in various applications, including infrastructure inspection, traffic monitoring, remote sensing, mapping, and rescue. However, many applications have required UAVs to function autonomously, without human intervention to improve system performance. In this study, we propose a new approach to environmental monitoring using a group of UAVs equipped with sensors under the support of reinforcement learning. Regarding the communication system model, we assume that UAVs can cooperate with each other to learn and share information about the environment, and then relocate to an optimal position while managing connectivity and coverage. After that, we exploit reinforcement learning with a deep deterministic policy gradient (DDPG) algorithm to optimize environmental monitoring with the proposed algorithm. Specifically, the proposed algorithm aims to simulate an environmental monitoring system using UAVs with basic parameters. We further apply the proposed algorithm to evaluate network performance under different parameter settings. Numerical results validate the effectiveness of the proposed learning-based framework in monitoring and sensing data.