Simulated Urban Environment for Last Mile Drone Logistics ("SimLogAI")
Simulated Urban Environment for Last Mile Drone Logistics ("SimLogAI")
批准号:
10080134
负责人:
金额:
$6.37万
依托单位国家:
英国
项目类别:
Collaborative R&D
财政年份:
2023
资助国家:
英国
项目状态:
已结题
起止时间:
2023 至 --
中文摘要
在复杂的供应链中,最后一英里交付对高价值制造(HVM)起着至关重要的作用。重要的组件比例至关重要,但相对较轻(例如<10kg的电子组件)。无人机将在短期内发挥破坏性作用,因为监管方面已经迈出了一大步,允许在特定要求下,在强大/受控的空中和地面风险缓解环境中进行超视距(BLOS)飞行。利用物联网连接(5G/wifi)的最新进展来控制和优化无人机的飞行路径,再加上数据驱动的人工智能模拟环境工具,将有可能整合一系列技术来创建自主的新商业模式,这些模式可以商业化,为时间至关重要的许多行业带来好处。物流被视为无人机应用中最大、最有前途的细分领域。本研究旨在优化人工智能和无人机支持的最后一英里交付之间的相互作用,以提高供应链效率,创造新的、系统化的、自动化的空中交付系统。通过人工智能系统集成,SimLogAI将能够支持制造业供应链的准时交付,同时减轻任何延误或经济处罚的风险。现有数据表明,尽管此类事件发生的频率各不相同,但一次发生就可能对生产造成毁灭性影响。由于对土地的需求很大,许多制造商都设在城市边缘地带。传统上,物流仅限于以化石燃料为动力的陆地解决方案,受到日益拥挤的道路网络的影响,并造成尾气排放。我们先进和可持续的人工智能无人机解决方案将支持更长的飞行路径和越来越重的有效载荷。使用无人机群、创新的控制和跟踪系统以及自主飞行路径优化,跨多个地点的供应链协调人工智能库存有可能通过数字技术彻底改变HVM的供应链。该项目将解决物流供应链利益相关者的端到端挑战和机遇,提高生产力,探索人工智能在HVM供应链中集成无人机的稳健性和弹性。它将考虑着陆和交付中心以及自主地面处理和运输系统(AGV/AutonomousGroundVehicle)、低碳排放交付增强、仿真数据验证路径/路由效率(空中/地面)和符合法规的空中安全保证。该项目旨在验证、调查和测试这一主题的分散方法,统一数字技术、连通性和政策领域,以开发与国家相关且可扩展的商业模式来转变物流,创建一个可操作的路线图,将这些元素结合在一起,同时确保信心、可信度和可靠性仍然是其核心。
英文摘要
Last-mile deliveries play a crucial role for high-value manufacturing (HVM) in complex supply chains. Significant proportions of components are critical yet relatively lightweight (e.g.<10kg electronic components).Drones will take a disruptive role in the near term as regulation has made a large step forward permitting beyond-line-of-sight(BLOS) flights under specific requirements, in a robust / controlled air and ground risk-mitigating environment. Using recent advances in IoT connectivity (5G/wifi) to control and optimize drone flight paths, coupled with data-driven AI simulation environment tools, it shall be possible to integrate a range of technologies to create autonomous new business models, which can be commercialized for the benefit of a number of sectors whereby time is of the essence. Logistics is seen as the largest and most promising subsegment in drone applications.This study seeks to optimize the interplay between artificial intelligence and drone-supported last-mile delivery to improve supply chain efficiencies and create new, systematic, automated, airborne delivery systems. Through AI systems integrations, SimLogAI will be able to support manufacturing supply chains' on-time delivery while mitigating any posed risks of delays or financial penalties. Existing data suggests that although the frequency of such events vary, a single occurrence could have a devastating effect on production.Many manufacturers are located in urban fringe sites because of expansive land requirements. Traditionally, logistics are confined to fossil fuel-powered land-based solutions, affected by increasingly congested road networks, and causing tailpipe emissions. Our advanced and sustainable AI drone solution will support longer flight paths with increasingly heavier payloads.Coordinated AI-type inventories with supply chains across multiple locations using swarms of drones, innovative control and tracking systems and autonomous flight path optimization have the potential to revolutionize supply chains of HVM through digital technologies.The project will address end-to-end challenges & opportunities for logistics supply chain stakeholders, providing enhancement to productivity, exploring the robustness & resilience of AI integrating drones in HVM supply chains. It will consider landing & delivery hubs as well as autonomous on-the-ground handling and transport systems (AGV/AutonomousGroundVehicle), low-carbon emissions delivery enhancements, simulation data validated path/ routing efficiency (air/ground) and regulation-compliant aerial safety assurance.This project seeks to validate, investigate and test the fragmented approaches to this subject, unifying digital technologies, connectivity and policy areas to develop nationally relevant and scalable business models to transform logistics, creating an actionable roadmap to combine these elements together whilst also ensuring confidence, trustworthiness & reliability remains at its core.
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