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I-Corps: Optimization-driven high-density air traffic management software for large-scale drone operations

I-Corps: Optimization-driven high-density air traffic management software for large-scale drone operations
I-Corps:用于大规模无人机操作的优化驱动的高密度空中交通管理软件
批准号:
2018127
负责人:
Yanchao Liu
金额:
$5.0万
依托单位:
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
项目状态:
已结题
起止时间:
2020-05-01 至 2023-10-31

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中文摘要
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英文摘要
The broader impact/commercial potential of this I-Corps project is to develop technology to make the low-altitude airspace more accessible for commercial drone delivery operations while ensuring a high level of operational safety and efficiency. Large-scale, organized deployment of delivery drones can unlock substantial efficiency gain in transportation systems in terms of reduced road congestion, reduced CO2 emission; in addition, it would offer health and convenience benefits to senior citizens, the ill, and mobility-challenged members of society. However, existing drone delivery practices are limited in scope, scale, and operating conditions due to inefficient air traffic management (ATM) paradigms. This project aims to discover a viable business model for translating technological innovations in unmanned ATM into an operable, city-scale, fully automated drone delivery system. The proposed system would allow safe use of the low-altitude airspace at an unprecedented density and efficiency, generating enormous savings for last-mile deliveries. This I-Corps project explores the development of a suite of novel optimization-driven algorithms in the areas of motion-planning, dynamic vehicle routing, communication and risk minimization to provide real-time ATM and trajectory control for a cooperative fleet of drones navigating a shared airspace. Existing ATM methods leave excessive separation margins due to human-level response time and control dexterity. In contrast, the proposed new system is optimized for high-density air traffic conditions, offering the capability to generate real-time adjustments to 4D flight trajectories, tolerating a smaller separation margin, and overseeing safe execution of control commands across the fleet. In a plug-and-play fashion, the proposed system will turn a collection of heterogeneous multicopter drones into an organized and intelligent fleet. The competitive advantage lies in the fusion of the state-of-the-art operations research (OR) models and algorithms onto drone platforms via commoditized cloud computing, IoT and communication infrastructures.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.
期刊论文(1)
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会议论文
DOI: 10.1016/j.trc.2023.104147
发表时间: 2023-07
期刊: Transportation Research Part C: Emerging Technologies
影响因子: --
作者: [Yan-chun Liu]
通讯作者: Yan-chun Liu
CAREER: Integrative Resource Optimization Framework for Large-scale Drone Delivery Systems
  • 批准号:
    1944068
  • 项目类别:
    Standard Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2020
  • 负责人:
    Yanchao Liu
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
供应链管理中的稳健型(Robust)策略分析和稳健型优化(Robust Optimization )方法研究
  • 批准号:
    70601028
  • 项目类别:
    青年科学基金项目
  • 资助金额:
    7.0万元
  • 批准年份:
    2006
  • 负责人:
    王明征
  • 依托单位: