Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler technology for data collection from Internet of Things (IoT) devices. However, effective data collection is challenged by resource constraints and the need for real-time decision-making. In this work, we propose a novel framework that integrates semantic communication with UAV command-and-control (C&C) to enable efficient image data collection from IoT devices. Each device uses Deep Joint Source-Channel Coding (DeepJSCC) to generate a compact semantic latent representation of its image to enable image reconstruction even under partial transmission. A base station (BS) controls the UAV's trajectory by transmitting acceleration commands. The objective is to maximize the average quality of reconstructed images by maintaining proximity to each device for a sufficient duration within a fixed time horizon. To address the challenging trade-off and account for delayed C&C signals, we model the problem as a Markov Decision Process and propose a Double Deep Q-Learning (DDQN)-based adaptive flight policy. Simulation results show that our approach outperforms baseline methods such as greedy and traveling salesman algorithms, in both device coverage and semantic reconstruction quality.

翻译：无人机已成为物联网设备数据采集的关键使能技术。然而，资源约束与实时决策需求对有效数据采集构成挑战。本文提出一种新型框架，将语义通信与无人机指挥控制相结合，实现物联网设备图像数据的高效采集。每个设备采用深度联合信源信道编码生成图像的紧凑语义潜在表征，即便在部分传输条件下仍能实现图像重建。基站通过传输加速度指令控制无人机轨迹，目标是在固定时间范围内维持与各设备的足够驻留距离，以最大化平均图像重建质量。为处理这一复杂权衡并应对指挥控制信号的时延问题，我们将问题建模为马尔可夫决策过程，并提出基于双重深度Q学习的自适应飞行策略。仿真结果表明，该方法在设备覆盖率和语义重建质量方面均优于贪婪算法与旅行商算法等基线方法。