NRI: INT: COLLAB: Synergetic Drone Delivery Network in Metropolis

NRI：INT：COLLAB：大都市的协同无人机交付网络

• 批准号: 1830512
• 负责人: Xiaofeng Wang
• 金额: $ 17.5万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830512&HistoricalAwards=false
• 关键词: NRI; INT; COLLAB; Synergetic; Drone

Synergetic Drone Delivery Network in MetropolisThe rapid growth of e-commerce demands has put additional strain on dense urban communities resulting in increased traffic of delivery trucks while slowing down the pace of delivery operations. With recent quick-purchase innovations like the Amazon Dash button, e-commerce drastically modified the consumers behavior to buy smaller products separately and regularly, adding more load to delivery operations. Another growing trend is the offering of fast delivery services such as same-day and instant delivery. Instacart, Uber Eats and Amazon Now are examples of services that can fulfill a delivery order in just under 2 hours. These services rely heavily on the infrastructure of ride-sharing vehicles as Uber or Lyft drivers. This solution offers great flexibility to the consumer, but a single person can only deliver one purchase order to a customer at a time, and it is not scalable or cost-effective. There is an unquestionable need to redesign the current method of distribution packages in urban environments. This project envisions a framework that synergizes manipulatable distribution networks, comprising autonomous flying robots (drones) with existing transport networks, towards enhanced autonomy and economics in logistics. Imagine that a ride-sharing vehicle outfitted with a docking device for packages on its roof is traveling through a distribution center towards downtown. A drone can place a package on the vehicle's roof while it drives by the distribution center, and another drone can recover the package once the vehicle is driving through another distribution center in proximity to its destination. An operator that owns several base stations, at each of which it employs a network of drones to pick packages from the respective base station and drop it on a ground vehicle assigned to the package, is a required assumption by the framework. The ground vehicles can be public transport vehicles (PTVs), ride-sharing vehicles (RSVs), or operator owned vehicles (OOVs), which carry the package for most of the distance.The approach relies on three main thrusts: i) socially aware robotics, ii) safe and robust motion planning and execution, iii) cooperative network logistics. Motion planning for robots will be developed with account of peoples perception of safety, privacy, and comfort. Socially-aware motion planning methods to generate trajectories with guarantees of safety in the presence of obstacles and humans will be developed. Psychological experiments will be developed to study human's subtle behavior in response to the presence of multiple drones using virtual reality test environment. Local control algorithms will be developed for each drone to follow a feasible collision free path. Robust local communication protocols will be investigated so that flying robots can perform collaborative tasks over busy air/ground traffic conditions and unreliable communication networks. Another objective is to achieve robust and safe rendezvous with fast moving vehicles under communication, schedule, and other modeling uncertainties. Algorithms that generate (possibly multi-hop) routes for each package, consisting of vehicle route segments, with the objective of minimizing cumulative delivery time, will be developed. The series of vehicle segments on which each package travels, and the associated schedule, is required as input for drones. This in turn necessitates solving the underlying network design problem for the centralized entity, to determine locations of distribution centers (bases) and number of OOVs required for feasible and reliable delivery of all packages, while explicitly estimating uncertainty from traffic trends and overall frequency of travel of RSVs between various points in the network. Game-theoretic mechanisms that incentivize cooperation among multiple independent operators of PSVs and RSVs will be developed. Mechanisms have to be specifically designed to ensure truthful bidding, because the objectives of the operator, the RSVs and the PSVs are not naturally aligned.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大都市的协同无人机送货网络电子商务需求的快速增长给人口稠密的城市社区带来了额外的压力，导致送货卡车的流量增加，同时减缓了送货业务的步伐。有了亚马逊Dash按钮等最近的快速购买创新，电子商务极大地改变了消费者的行为，让他们分开并定期购买较小的产品，为送货操作增加了更多负担。另一个日益增长的趋势是提供快速递送服务，如当天和即时递送。Insta、Uber Eats和Amazon现在都是可以在不到2小时内完成送货订单的服务实例。作为优步或Lyft的司机，这些服务严重依赖拼车车辆的基础设施。此解决方案为消费者提供了极大的灵活性，但一个人一次只能向一个客户发送一个采购订单，而且它不具有可扩展性或成本效益。毫无疑问，有必要重新设计目前在城市环境中分发包裹的方法。该项目设想了一个框架，将可操纵的分销网络(包括自主飞行机器人(无人机)与现有运输网络)协同作用，以增强物流的自主性和经济性。想象一下，一辆车顶上装有包裹停靠装置的拼车正通过一个配送中心向市中心行驶。一架无人机可以在经过配送中心时将包裹放在车辆的屋顶上，而另一架无人机可以在车辆通过目的地附近的另一个配送中心时收回包裹。拥有几个基站的运营商，在每个基站上都使用无人机网络从各自的基站挑选包裹，并将其投放到分配给包裹的地面车辆上，这是该框架必须假定的。地面车辆可以是公共交通车辆(PTV)、拼车车辆(RSV)或运营商自有车辆(OOV)，它们携带包裹的大部分距离。该方法依赖于三个主要推力：i)社会性感知机器人，ii)安全和健壮的运动规划和执行，iii)合作网络物流。机器人的运动规划将考虑到人们对安全、隐私和舒适性的感知。将开发具有社会意识的运动规划方法，以生成在存在障碍物和人类的情况下确保安全的轨迹。心理学实验将利用虚拟现实测试环境来研究人类在面对多架无人机时的微妙行为。将为每架无人机开发本地控制算法，使其遵循可行的无碰撞路径。将研究强大的本地通信协议，以便飞行机器人能够在繁忙的空中/地面交通条件和不可靠的通信网络中执行协作任务。另一个目标是在通信、进度和其他建模不确定性的情况下，实现与快速移动的车辆稳健和安全的交会。将开发为每个包裹生成(可能是多跳)路线的算法，这些路线由车辆路线段组成，目标是最大限度地减少累计交付时间。无人机需要输入每个包裹旅行的一系列车辆段和相关的时间表。这进而需要为中央实体解决底层网络设计问题，以确定可行且可靠地递送所有包裹所需的配送中心(基地)的位置和OOV的数量，同时明确地估计交通趋势和RSV在网络中不同点之间的总行进频率的不确定性。将建立博弈论机制，激励PSV和RSV的多个独立运营商之间的合作。机制必须专门设计，以确保真实的竞标，因为运营商、RSV和PSV的目标并不自然地一致。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image

• DOI: 10.1109/hpcc-dss-smartcity-dependsys57074.2022.00271
• 发表时间: 2022-10
• 期刊: 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)
• 作者: Xin Zhang;R. Abdelfattah;Yuqi Song;Samuel A. Dauchert;Xiaofen Wang

A Framework for Predictive Control of Sampled-Data Systems Using Sporadic Model Approximation

使用零星模型逼近的采样数据系统预测控制框架

• DOI: 10.23919/acc50511.2021.9482933
• 发表时间: 2021
• 期刊: American Control Conference
• 作者: Yang, Lixing;Dauchert, Samuel;Wang, Xiaofeng
• 通讯作者: Wang, Xiaofeng

Lebesgue-Approximation Model Predictive Control of Nonlinear Sampled-Data Systems

非线性采样数据系统的勒贝格近似模型预测控制

• DOI: 10.1109/tac.2019.2953147
• 发表时间: 2020-10
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Jie Tao;Lixing Yang;Zheng-Guang Wu;Xiaofeng Wang;Hongye Su
• 通讯作者: Hongye Su

An Effective Approach for Multi-label Classification with Missing Labels

• DOI: 10.1109/hpcc-dss-smartcity-dependsys57074.2022.00259
• 发表时间: 2022-10
• 期刊: 2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)
• 作者: Xin Zhang;R. Abdelfattah;Yuqi Song;Xiaofeng Wang

Self‐triggered predictive control of nonlinear systems using approximation model

• DOI: 10.1002/rnc.6322
• 发表时间: 2022-07
• 期刊: International Journal of Robust and Nonlinear Control
• 影响因子: 3.9
• 作者: Lixing Yang;Samuel A. Dauchert;Xiaofeng Wang