研究人员开发了一种新颖的双时间尺度强化学习（TSRL）框架，以应对使用无人机进行城市传感的挑战。该框架解决了可扩展性问题以及宏观任务调度和微观速度控制之间决策的异质性问题。TSRL将决策分为两个层面：宏观层面的调度器负责编码任务特征并评估无人机适用性；微观层面的控制器则根据风等环境变化调整无人机速度。实验表明，TSRL显著提高了杭州和上海的系统利润。 AI
影响 这项研究可能带来更高效、可扩展的无人机城市监测系统，从而改善环境传感数据的收集。
排序理由 该集群描述了一篇关于人工智能特定应用新框架的详细研究论文。
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arXiv:2607.18874v1 Announce Type: new Abstract: Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.g., air quality and noise levels, through agile aerial crowdsourcing. Despite this potential, existing UAV…
Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.g., air quality and noise levels, through agile aerial crowdsourcing. Despite this potential, existing UAV-based sensing approaches overlook environmental…