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

大都市的协同无人机配送网络电子商务需求的快速增长给密集的城市社区带来了额外的压力，导致送货卡车的交通量增加，同时减缓了送货运营的速度。随着最近的快速购买创新，如亚马逊Dash按钮，电子商务大大改变了消费者单独和定期购买较小产品的行为，增加了交付操作的负担。另一个不断增长的趋势是提供快速交付服务，如当天交付和即时交付。Instacart，Uber Eats和Amazon Now都是可以在不到2小时内完成交付订单的服务。这些服务在很大程度上依赖于Uber或Lyft司机等共乘车辆的基础设施。该解决方案为消费者提供了很大的灵活性，但一个人一次只能向客户交付一个采购订单，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，协同可操纵的分销网络，包括自主飞行机器人（无人机）与现有的运输网络，以提高物流的自主性和经济性。想象一下，一辆在车顶上配备了包裹对接设备的共乘车辆正在通过配送中心向市中心行驶。无人机可以在经过配送中心时将包裹放置在车辆的车顶上，并且一旦车辆行驶通过靠近其目的地的另一个配送中心，另一个无人机就可以回收包裹。拥有多个基站的运营商，在每个基站上使用无人机网络从相应的基站拾取包裹并将其放在分配给包裹的地面车辆上，这是框架所需的假设。地面车辆可以是公共交通车辆（PTV）、共乘车辆（RSV）或运营商自有车辆（OOV），它们在大部分距离内运送包裹。该方法依赖于三个主要方面：i）社会感知机器人，ii）安全可靠的运动规划和执行，iii）合作网络物流。机器人的运动规划将考虑到人们对安全、隐私和舒适的感知。将开发具有社会意识的运动规划方法，以生成在障碍物和人类存在下具有安全保证的轨迹。将开发心理实验，以研究人类的微妙行为，以应对多个无人机使用虚拟现实测试环境的存在。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。将研究鲁棒的本地通信协议，以便飞行机器人能够在忙碌繁忙的空中/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、调度和其他建模不确定性的情况下实现与快速移动的飞行器的鲁棒和安全的交会。将开发为每个包裹生成（可能是多跳）路线的算法，该路线由车辆路线段组成，目标是最大限度地减少累积交付时间。每个包裹在其上行进的一系列车辆段以及相关联的时间表需要作为无人机的输入。 这又需要解决集中式实体的基础网络设计问题，以确定配送中心（基地）的位置和所有包裹的可行且可靠的递送所需的OOV的数量，同时明确地估计来自网络中的各个点之间的RSV的交通趋势和总行进频率的不确定性。将开发激励PSV和RSV的多个独立运营商之间合作的博弈机制。由于运营商、RSV和PSV的目标并不自然一致，因此必须专门设计机制以确保真实的投标。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.