发表机构
The Hong Kong University of Science and Technology (Guangzhou); Beijing Institute of Technology（香港科技大学（广州）; 北京理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究动态环境中基于送货无人机的参与式传感问题，提出双时间尺度强化学习框架TSRL，该框架分宏观和微观两层协作决策，实验表明其显著优于基线，在杭州和上海分别提升系统利润20.1%和46.6%。
AI 中文摘要
使用无人机进行城市传感已成为一种强大的范式，通过灵活的空中众包来监测城市状况，如空气质量和噪音水平。尽管有这种潜力，但现有的基于无人机的传感方法忽略了像风这样会严重影响无人机速度和能源效率的环境干扰。将现有方法直接应用于动态环境中的联合送货和传感范式面临两个严峻挑战：随着机群规模扩大的可扩展性瓶颈，以及宏观任务调度和微观速度控制之间的多时间尺度决策异质性。为解决这些问题，我们将问题形式化为SensUAV并提出双时间尺度强化学习框架（TSRL）。具体而言，TSRL将决策分为两个协作层。在宏观层面，任务嵌入传感调度器通过分别编码不同任务特征并在任务选择前顺序评估无人机适用性来处理可扩展性。在微观层面，风感知速度控制器学习细粒度速度调度以适应动态环境变化。在真实世界数据集上的大量实验表明，TSRL显著优于基线，在杭州平均系统利润提高20.1%，在上海提高46.6%。
英文摘要
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 disturbances like wind that drastically impact drone velocity and energy efficiency. Consequently, directly applying existing methods to this joint delivery and sensing paradigm in dynamic environments faces two severe challenges: (1) scalability bottlenecks as fleet sizes expand; and (2) multi-timescale decision heterogeneity between macro task dispatching and micro velocity control. To tackle these, we formalize the problem as SensUAV and propose a Two TimeScale Reinforcement Learning framework (TSRL). Specifically, TSRL separates decision-making into two cooperative layers. At the macro level, a task-embedding sensing dispatcher handles scalability by separately encoding distinct task features and sequentially evaluating UAV suitability before task selection. At the micro level, a wind-aware velocity controller learns fine-grained velocity scheduling to adapt to dynamic environmental variations. Extensive experiments on real-world datasets demonstrate that TSRL significantly outperforms baselines, achieving average system profit improvements of 20.1% in Hangzhou and 46.6% in Shanghai.
CommentsAccepted to ACM SIGSPATIAL 2026 (Research Paper Track)
