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: 1830554
• 负责人: Marco Pavone
• 金额: $ 28.73万
• 依托单位: Stanford University
• 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=1830554&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.

大都市中的协同无人机交付网络的快速增长，电子商务需求的迅速增长给密集的城市社区带来了额外的压力，从而导致交付卡车的流量增加，同时减慢了交付运营的速度。随着最近快速购买的创新（例如Amazon Dash按钮），电子商务对消费者行为进行了彻底的修改，以分别和定期购买较小的产品，从而为交付操作增加了更多负载。另一个增长的趋势是提供快速交货服务，例如当天和即时交付。 Instacart，Uber Eats和Amazon现在是可以在不到2个小时内完成送货订单的服务的示例。这些服务在很大程度上依赖于乘车共享车辆作为Uber或Lyft驾驶员的基础设施。该解决方案为消费者提供了极大的灵活性，但是一个人一次只能向客户提供一份采购订单，并且不可扩展或具有成本效益。 毫无疑问，需要重新设计在城市环境中当前的分销软件包方法。该项目设想了一个协同可操作的分销网络的框架，其中包括具有现有运输网络的自主飞行机器人（无人机），以增强物流中的自主权和经济性。想象一下，配备了屋顶上的包装设备的乘车共享车辆正在穿过配送中心前往市中心。无人机可以将包装放在车辆的屋顶上，同时它在配送中心驾驶时，一旦车辆驶过另一个分配中心，可以恢复该包装，以靠近其目的地。拥有多个基站的操作员，每个基站都使用无人机网络从相应的基站挑选包装并将其放在分配给包装的地面车辆上，这是该框架的必要假设。地面车辆可以是公共交通车辆（PTV），乘车共享车辆（RSV）或操作员拥有的车辆（OOV），它们在大多数距离内都携带包装。该方法取决于三个主要推力：i）社会意识到的机器人技术，ii），ii）II）安全和强大的运动计划和执行，iii II）合作网络logistics。由于人们对安全，隐私和舒适感的看法，将开发针对机器人的运动计划。在存在障碍和人类的情况下，将采用社会意识的运动计划方法，以确保安全性。将开发心理实验，以使用虚拟现实测试环境来响应多个无人机的存在来研究人类的微妙行为。将为每架无人机开发局部控制算法，以遵循可行的无碰撞路径。将研究强大的本地通信协议，以便飞行机器人可以通过繁忙的空气/地面交通条件和不可靠的通信网络执行协作任务。另一个目的是在沟通，时间表和其他建模不确定性下快速移动的车辆实现强大且安全的会合。将开发由车辆路线段组成的每个软件包（可能是多跳）路线的算法，以最大程度地减少累积交付时间的目的。每个包装都在上面进行的一系列车段以及相关的时间表是无人机输入的。 反过来，这需要解决集中式实体的基本网络设计问题，以确定分配中心（基地）的位置以及可行且可靠的所有软件包所需的OOV数量，同时明确估计了网络之间各个点之间RSV的交通趋势的不确定性和整体旅行频率。将开发激励多个PSV和RSV独立运营商之间合作的游戏理论机制。必须专门设计机制来确保真实的招标，因为操作员，RSV和PSV的目标并非自然而徒。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来获得支持的。

项目成果

Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networks

无人机和卡车在道路网络上的协调多代理寻路

• 发表时间: 2022
• 期刊: Autonomous agents and multiagent systems
• 作者: Choudhury, Shushman;Solovey, Kiril;Kochenderfer, Mykel J.;Pavone, Marco
• 通讯作者: Pavone, Marco

Efficient Large-Scale Multi-Drone Delivery using Transit Networks

使用交通网络进行高效的大规模多无人机交付

• DOI: 10.1613/jair.1.12450
• 发表时间: 2021
• 期刊: Journal of Artificial Intelligence Research
• 影响因子: 5
• 作者: Choudhury, Shushman;Solovey, Kiril;Kochenderfer, Mykel J.;Pavone, Marco
• 通讯作者: Pavone, Marco

Balancing fairness and efficiency in traffic routing via interpolated traffic assignment

• DOI: 10.1007/s10458-023-09616-7
• 发表时间: 2021-03
• 期刊: Autonomous Agents and Multi-Agent Systems
• 影响因子: 1.9
• 作者: Devansh Jalota;Kiril Solovey;Stephen Zoepf;M. Pavone

Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics

• DOI: 10.1109/lra.2022.3153712
• 发表时间: 2021-09
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Pan Zhao;Arun Lakshmanan;K. Ackerman;Aditya Gahlawat;M. Pavone;N. Hovakimyan