PFI-TT: Cloud-based Route Management Platform for Optimizing Last-Mile Logistics of Electric Truck and Drone Operations

PFI-TT：基于云的路线管理平台，用于优化电动卡车和无人机运营的最后一英里物流

• 批准号: 2313887
• 负责人: Sharan Srinivas
• 金额: $ 25万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313887&HistoricalAwards=false
• 关键词: PFI; TT; Cloud; based; Route

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to enable the safe and efficient use of unmanned aerial vehicles (UAVs or drones) and electric trucks for last-mile logistics, a multi-billion global market that accounts for 41% of the total supply cost and 8% of global greenhouse gas emissions. Specifically, the envisioned technology allows logistics service providers to establish reliable and optimized routing plans for a scalable fleet involving electric trucks and UAVs. The technology being developed in this project has the potential to: (i) increase the fleet utilization and productivity of last-mile service providers, (ii) reduce carbon footprint in supply chains, (iii) enhance operational safety and network communication reliability of drone-based deliveries, and (iv) decrease operational costs of last-mile logistics. The potential markets for the technology being developed in the project include many areas, such as medical delivery, e-commerce logistics, humanitarian operations, emergency response, and last-mile delivery. The methodological advancements and scientific understanding derived from this project can benefit policymakers, researchers and practitioners and strengthen the economic competitiveness of the United States.This project is developing and validating a cloud-based route optimization platform for electric trucks and UAVs to enable safe and efficient last-mile operations. A key technical challenge addressed in this project is the joint consideration of operational (i.e., optimizing vehicle’s route plan) and networking (packet forwarding strategy for communication among vehicles and ground servers) decisions for UAV and electric truck route management. The research objectives include the development and validation of (i) new optimization models and heuristic methods to efficiently solve the electric truck and UAV routing problem, (ii) new dynamic routing models that integrate machine learning and optimization for handling stochastic elements, while accounting for airspace and road network management, environmental factors and network communications, and (iii) network protocol design that features artificial intelligence (AI)-enabled algorithms for UAV trajectory planning based on awareness of environmental conditions. The project involves a modular strategy, thereby allowing the route planning algorithm to be scalable for any delivery methods (electric truck-only distribution, direct drone delivery, and hybrid truck-drone operations). The innovation in this project is evaluated based on field experimentation and pilot testing of a minimum viable product (MVP) in collaboration with multiple industrial partners.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.

这种合作伙伴关系对创新 - 技术翻译（PFI-TT）项目的更广泛的影响/商业潜力是为了使无人驾驶汽车（UAVS或无人机）和电动卡车的安全有效地使用最后一英里物流，这是一个众多的全球全球市场，占全球供应成本的41％，占全球全球温室气体的41％。具体而言，设想的技术使物流服务提供商可以为涉及电动卡车和无人机的可扩展机队建立可靠，优化的路由计划。该项目中开发的技术具有：（i）提高上一英里服务提供商的车队利用率和生产率，（ii）减少供应链中的碳足迹，（iii）提高基于无人机的交付的运营安全性和网络通信可靠性，以及（IV）降低了最后一英里物流的运营成本。该项目中开发的技术的潜在市场包括许多领域，例如医疗交付，电子商务物流，人道主义业务，紧急响应和最后一英里的交付。从该项目获得的方法论进步和科学理解可以使政治制定者，研究人员和从业人员受益，并增强美国的经济竞争力。该项目正在开发和验证基于云的电动卡车和无人机的基于云的路线优化平台，以启用安全有效的最后一部分操作。该项目中提出的关键技术挑战是对运营（即优化车辆路线计划）的联合考虑（即，用于在车辆和地面服务器之间进行通信的数据包转发策略）的决策，用于无人机和电动卡车路线管理。 The research objectives include the development and validation of (i) new optimization models and heuristic methods to effectively solve the electric truck and UAV routing problem, (ii) new dynamic Routing models that integrate machine learning and optimization for handling stochastic elements, while accounting for airspace and road network management, environmental factors and network communications, and (iii) network protocol design that features artificial intelligence (AI)-enabled algorithms for UAV trajectory planning基于对环境条件的认识。该项目涉及模块化策略，从而使路线计划算法可扩展到任何交付方法（仅电动卡车分销，直接无人机交付和混合型卡车无人机操作）。根据现场实验和试点测试对最低可行产品（MVP）进行了与多个工业合作伙伴的合作进行评估。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的评估标准来评估通过评估来获得的支持。