发表机构
Hangzhou International Innovation Institute of Beihang University; School of Automation Science and Electrical Engineering at Beihang University; State Key Laboratory of Intelligent Manufacturing Systems Technology; School of Transportation, Southeast University; Digital Health Research Center, Haihe Lab of ITAI; School of Cyber Science, Nankai University; China-SCO Digital Intelligence Applications (DIA) Joint Laboratory; College of Engineering, Abu Dhabi University; Faculty of Engineering and Architecture, Nisantasi University; School of Information Engineering, Guangdong University of Technology（北京航空航天大学杭州国际创新研究院; 北京航空航天大学自动化科学与电气工程学院; 智能制造系统技术国家重点实验室; 东南大学交通学院; 海河信息技术与人工智能实验室数字健康研究中心; 南开大学网络空间科学学院; 中国-上海合作组织数字智能应用联合实验室; 阿布扎比大学工程学院; 尼桑塔西大学工程与建筑学院; 广东工业大学信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究无人机辅助车联网中轨迹规划与路由增强问题，提出基于独立表格Q学习的分布式多智能体强化学习框架TRUAV，其智能体依局部信息运行，奖励设计兼顾多种因素，模拟显示该框架在多方面表现良好，还讨论了相关挑战与方向。
AI 中文摘要
无人机已成为下一代物联网生态系统的关键推动者，为智能城市环境中的动态车载自组织网络提供灵活的空中中继。然而，传统的无人机轨迹规划集中式方法需要持续的全局网络状态聚合，在密集城市部署的带宽和能量限制下不切实际。本文提出TRUAV，一种基于独立表格Q学习的分布式多智能体强化学习框架，用于无人机辅助车联网中的联合轨迹规划和路由增强。每个无人机配备本地Q学习智能体，仅根据局部可观测信息运行，无需全局状态交换。受潜在博弈启发的奖励设计鼓励智能体间的空间多样性和路由感知定位，同时考虑能耗。在有200辆移动车辆的大城市区域进行的数值模拟表明，TRUAV框架实现的网络覆盖和数据包交付率与集中式深度强化学习方法相当，同时还改善了中继延迟和能量效率。最后，讨论了分布式多智能体无人机辅助物联网系统面临的新挑战和未来研究方向。
英文摘要
Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VANETs) in smart city environments. However, conventional centralized approaches for UAV trajectory planning require continuous global network state aggregation, making them impractical under bandwidth and energy constraints typical of dense urban deployments. In this article, we present TRUAV, a distributed multi-agent reinforcement learning framework based on independent tabular Q-learning for joint UAV trajectory planning and routing enhancement in UAV-aided VANETs. Each UAV is equipped with a local Q-learning agent that operates purely on locally observable information, including vehicle density, packet queue states, and neighbor UAV positions, thereby eliminating the need for global state exchange. A potential-game-inspired reward design encourages spatial diversity and routing-aware UAV positioning among interacting agents while accounting for energy consumption. Numerical simulations over a large urban area with 200 mobile vehicles show that the proposed TRUAV framework achieves network coverage and packet delivery ratios comparable to centralized deep reinforcement learning methods, while also improving relay delay and energy efficiency. Finally, we discuss emerging challenges and future research directions for distributed multi-agent UAV-assisted IoT systems.
Comments7 pages, 3 figures, submitted to IEEE Internet of Things Magazine
