Multi-layered network and routing optimization for unmanned aerial vehicle traffic

无人机交通的多层网络和路由优化

• 批准号: 576624-2022
• 负责人: Gzara, FatmaF
• 金额: $ 2.71万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758132
• 关键词: Multi; layered; network; routing; optimization

As unmanned aerial vehicle (or drone) technology evolves, the ability to safely control drone flight along precise pathways opens the door to an alternative mode of transport. The unique characteristics of drones allow for greener alternatives to trucks for last mile delivery of consumer packages, rapid deployment of critical medical equipment, and the provision of critical infrastructure such as network signal or line of sight surveillance in emergency situations. As drone usage scales, infrastructure is required to enable the safe operation of many simultaneous flights. One required technology is a drone traffic management system to track ongoing drone flights and approve flight requests as they are made. This research aims to design the drone traffic networks and routing algorithms required by traffic management systems, in partnership with drone industry experts, and attentive to government regulations. The research will develop optimization models and solution methods to select the roads to be included in the network and to design their 3D projection into the skies, thus creating a network for routing drone traffic. The selected network should maximize potential traffic by providing short low-risk paths between anticipated drone origins and destinations, be robust to weather and other uncertainties, and be cost-effective to map and monitor. Given a mapped 3D network, the research will design routing tools to dictate how to handle flight requests as they are made in real-time. The rules should account for weather conditions, interference, delay, and congestion, and provide mechanisms for prioritizing emergency flights. The research results are crucial to develop network infrastructure and technology to plan and manage drone traffic at a large scale. The research will provide tools for regulatory bodies like Transport Canada to understand the challenges and limitation of the technology and inform their drone traffic regulations and drone technology adoption policies. Given the lack of research on the topic and the race of regulatory bodies to set the rules and safety protocols, the research outcomes will put Canada at the forefront of drone technology.

随着无人驾驶飞行器（或无人机）技术的发展，沿着精确路径安全控制无人机飞行的能力为另一种运输方式打开了大门。无人机的独特特性使其能够在最后一英里运送消费者包裹、快速部署关键医疗设备以及提供关键基础设施（如网络信号或紧急情况下的视线监视）方面成为卡车的更环保替代品。随着无人机使用规模的扩大，需要基础设施来保证多个同时飞行的安全运行。其中一项需要的技术是无人机交通管理系统，用于跟踪正在进行的无人机飞行，并批准飞行请求。本研究旨在与无人机行业专家合作，并注意政府法规，设计交通管理系统所需的无人机交通网络和路由算法。该研究将开发优化模型和解决方法，以选择包括在网络中的道路，并设计它们在天空中的3D投影，从而创建一个无人机交通路由网络。选定的网络应该通过在预期的无人机起点和目的地之间提供短的低风险路径来最大化潜在流量，对天气和其他不确定因素具有鲁强性，并且在地图和监控方面具有成本效益。给定一个映射的3D网络，该研究将设计路由工具，以指示如何处理实时发出的飞行请求。这些规则应考虑到天气状况、干扰、延误和拥堵，并提供优先安排紧急航班的机制。研究结果对于开发网络基础设施和技术以大规模规划和管理无人机交通至关重要。这项研究将为加拿大交通部等监管机构提供工具，帮助他们了解这项技术的挑战和局限性，并为他们的无人机交通法规和无人机技术采用政策提供信息。鉴于缺乏对该主题的研究以及监管机构制定规则和安全协议的竞赛，研究成果将使加拿大处于无人机技术的最前沿。