CAREER: Integrative Resource Optimization Framework for Large-scale Drone Delivery Systems

职业：大型无人机交付系统的综合资源优化框架

• 批准号: 1944068
• 负责人: Yanchao Liu
• 金额: $ 50万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944068&HistoricalAwards=false
• 关键词: CAREER; Integrative; Resource; Optimization; Framework

This Faculty Early Career Development Program (CAREER) grant will contribute to the advancement of national prosperity and economic welfare by promoting safe, efficient and equitable use of low-altitude airspace for drone delivery operations. Drone delivery has the potential to reduce last-mile delivery time from hours to minutes and eliminate billions of individual trips that are otherwise unsafe, polluting and wasteful. However, safety and scalability challenges exist that currently impede commercial deployment at scale. This award supports research toward a fundamental understanding of how diverse resources can be managed to enable efficient city-scale drone delivery services. The design and operational challenges include optimal location of depots and rally points, capacity planning, fleet management, and dynamic vehicle routing under battery, traffic, and weather constraints. The research will support integration of aerial vehicle logistics, vehicle onboard intelligence, and air traffic management. The education program aims to generate new curricula, train new STEM talent and inspire new entrepreneurs in a new field at the intersection of aviation, robotics and operations research. The education program will also create undergraduate research opportunities, particularly for under-represented minorities in STEM. This project will introduce a general, optimization-based framework for modeling complex decision-making processes in aerial logistics operations, as well as new algorithms for solving problems modeled in such a framework. Three safety-critical resources, including ground stations, batteries, and the airspace, will be studied comprehensively and modeled holistically: the location and configuration of ground stations are informed by a suite of battery power prediction models, and in turn, determine the feasible paths of drones and hence the optimal dynamic allocation of airspace corridors. The research will develop (1) decomposable mathematical programs to tackle robust network design and continuous location problems, (2) new algorithms that combine evolutionary computing with convex optimization for solving nonlinear combinatorial problems, (3) a new framework for modeling stochastic multi-agent systems based on innovative extensions of Markov decision processes, and (4) improved algorithms for in-flight battery power management. The research approach integrates theoretical developments with practical implementation and is heavily informed by commercial application potential. Software apps will be developed and flight trials will be conducted to deliver practical relevance and impact.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.

这项教师早期职业发展计划（职业）赠款将通过促进安全，高效和公平的低空领空来促进国家繁荣和经济福利的发展。无人机交付有可能将最后一英里的交付时间从数小时减少到几分钟，并消除数十亿个不安全，污染和浪费的单独旅行。但是，目前存在大规模阻碍商业部署的安全性和可伸缩性挑战。该奖项支持对如何管理各种资源来实现有效的城市尺度无人机交付服务的基本了解。 设计和操作挑战包括仓库和拉力点的最佳位置，容量规划，车队管理以及电池，交通和天气限制下的动态车辆路由。该研究将支持航空车载物流，车载智能和空中交通管理的整合。该教育计划旨在在航空，机器人技术和运营研究的交叉点上建立新的课程，培训新的STEM人才并激发新的企业家。 该教育计划还将创造本科研究机会，特别是对于STEM中代表性不足的少数民族而言。该项目将介绍一个基于一般优化的框架，用于建模空中物流运营中的复杂决策过程，以及解决此类框架中建模的问题的新算法。将对包括地面站，电池和领空在内的三个安全至关重要的资源进行全面研究：地面站的位置和配置由一系列电池电源预测模型来告知，然后确定无人机的可行路径，并因此确定空间空间空间的最佳动态分配。这项研究将开发（1）可分解的数学程序，以解决强大的网络设计和连续的位置问题，（2）将进化计算与凸出的非线性组合问题求解的新算法相结合的新算法，（3）用于建模机构扩展的新框架，用于建模机器人的设备，以对机器进行构图，并改进了（4）改进的（4）改进了（4），并（4）改进了（4）。该研究方法将理论发展与实际实施相结合，并在商业应用潜力中大量了解。将开发软件应用程序，并将进行飞行试验以提供实际的相关性和影响。该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛的影响评估标准来通过评估来获得支持的。

项目成果

bsnsing: A Decision Tree Induction Method Based on Recursive Optimal Boolean Rule Composition

bsnsing：一种基于递归最优布尔规则组合的决策树归纳方法

• DOI: 10.1287/ijoc.2022.1225
• 发表时间: 2022
• 期刊: INFORMS Journal on Computing
• 影响因子: 2.1
• 作者: Liu, Yanchao

Optimized Landing of Drones in the Context of Congested Air Traffic and Limited Vertiports

在空中交通拥堵和垂直起落场有限的情况下优化无人机着陆

• DOI: 10.1109/tits.2020.3040549
• 发表时间: 2021
• 期刊: IEEE Transactions on Intelligent Transportation Systems
• 影响因子: 8.5
• 作者: Zhou, Zhenyu;Chen, Jun;Liu, Yanchao
• 通讯作者: Liu, Yanchao

A multi-agent semi-cooperative unmanned air traffic management model with separation assurance

具有间隔保证的多智能体半合作无人机交通管理模型

• DOI: 10.1016/j.ejtl.2021.100058
• 发表时间: 2021
• 期刊: EURO Journal on Transportation and Logistics
• 影响因子: 2.4
• 作者: Liu, Yanchao

Routing battery-constrained delivery drones in a depot network: A business model and its optimization–simulation assessment

• DOI: 10.1016/j.trc.2023.104147
• 发表时间: 2023-07
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Yan-chun Liu

A Smart Landing Platform With Data-Driven Analytic Procedures for UAV Preflight Safety Diagnosis

• DOI: 10.1109/access.2021.3128866
• 发表时间: 2021
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Zhenyu Zhou;Yanchao Liu