UrbanHuRo: 一种用于异构城市服务联合优化的双层人机协作框架
UrbanHuRo: A Two-Layer Human-Robot Collaboration Framework for the Joint Optimization of Heterogeneous Urban Services
AI总结：
UrbanHuRo提出一种双层人机协作框架，通过众包配送和城市感知优化异构城市服务，提升感知覆盖和快递员收入，减少逾期订单。
摘要：
在智慧城市愿景下，技术正被开发以提高城市服务效率并改善居民生活质量。然而，大多数现有研究专注于孤立地优化个别服务，而没有充分考虑异构城市服务之间可能的相互作用，这些相互作用可能带来更高的效率和改进的资源利用率。例如，人类快递员可以在其送货路线中收集交通和空气质量数据，而感知机器人可以在高峰时段协助按需配送，从而提高感知覆盖范围和配送效率。然而，不同城市服务的联合优化具有挑战性，因为可能存在冲突的目标，并且需要在动态环境中进行实时协调。在本文中，我们提出UrbanHuRo，一种用于异构城市服务联合优化的双层人机协作框架，通过众包配送和城市感知进行了演示。UrbanHuRo包含两个关键设计：(i) 一个可扩展的分布式基于MapReduce的K次子模函数最大化模块，用于高效的订单调度；(ii) 一种深度子模奖励强化学习算法用于感知路线规划。在真实世界数据集上的实验评估表明，UrbanHuRo在大多数情况下平均提高了29.7%的感知覆盖范围和39.2%的快递员收入，同时显著减少了逾期订单的数量。
英文摘要：
In the vision of smart cities, technologies are being developed to enhance the efficiency of urban services and improve residents' quality of life. However, most existing research focuses on optimizing individual services in isolation, without adequately considering reciprocal interactions among heterogeneous urban services that could yield higher efficiency and improved resource utilization. For example, human couriers could collect traffic and air quality data along their delivery routes, while sensing robots could assist with on-demand delivery during peak hours, enhancing both sensing coverage and delivery efficiency. However, the joint optimization of different urban services is challenging due to potentially conflicting objectives and the need for real-time coordination in dynamic environments. In this paper, we propose UrbanHuRo, a two-layer human-robot collaboration framework for joint optimization of heterogeneous urban services, demonstrated through crowdsourced delivery and urban sensing. UrbanHuRo includes two key designs: (i) a scalable distributed MapReduce-based K-submodular maximization module for efficient order dispatch, and (ii) a deep submodular reward reinforcement learning algorithm for sensing route planning. Experimental evaluations on real-world datasets from a food delivery platform demonstrate that UrbanHuRo improves sensing coverage by 29.7% and courier income by 39.2% on average in most settings, while also significantly reducing the number of overdue orders.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
