Autonomous Cargo

用于工业应用的联网和自动驾驶汽车（cav）在封闭环境（例如机场）和更广泛的物流应用（例如货运中心）中提供安全，随时可用和零排放的货物运输，车辆可以全天候运行以处理货物和运送人员。在空勤方面，人员短缺目前非常严重，造成飞机返航延误和经济损失。此外，频繁发生的空侧人工操作事故伤害了操作人员，损坏了飞机和货物。该项目将由RDM的自动驾驶汽车部门Aurrigo领导。Aurrigo已经开发并测试了自动驾驶汽车，包括用于公路和非公路操作的载客AUTO-Shuttle和用于机场行李处理的AUTO-Dolly。在这个项目中，RDM Aurrigo将开发技术来制造能够承载高达7.5吨的空运货物搬运车，并与全球货运搬运公司UPS合作进行测试，UPS代表了第一个客户。自动货运原型将在东米德兰兹机场UPS中心的现场环境中开发和测试。为了扩大自动驾驶汽车的部署，RDM将重新开发用于航空货运业务的Auto-Sim工具。该项目将开发、验证和验证CAV技术，并提供对优化操作所需基础设施的更多了解。关键的是，它将为任何希望实现CAV操作的组织提供一种方法，以产生这样做的业务案例。我们将开发技术，将任何货物处理区域的数字地图与货物吞吐量和外部因素（例如航班时刻表）的数据结合起来。根据这些数据，可以对cav的最佳数量和类型进行建模，并且该工具可以说明需要哪些支持基础设施。它将能够通过引入自动驾驶汽车和流程来模拟效率（减少延误和更快的飞机周转）和容量（每小时更多的航班）。它还将模拟处理成本降低带来的成本降低，以及容量增加带来的潜在收入增加。Aurrigo将开发和测试自动货运车，以验证数字模型，该模型将在东米德兰兹机场的测试阶段运行，以验证技术并开发供应链。

Connected and autonomous vehicles (CAVs) for industrial applications offer safe, readily available and zero emission transportation of goods within closed environments (e.g. airports) and wider logistics applications (e.g. freight hubs) where vehicles can operate 24/7 to handle goods and move people. In airside operations, staff shortages are currently critical, causing delays in turning aircraft around and financial losses. Furthermore, frequent airside manual handling accidents injure human operators and damage aircraft and cargo.The project will be led by RDM's autonomous vehicle arm, Aurrigo. Aurrigo have developed and tested autonomous vehicles including a passenger carrying AUTO-Shuttle for on and off-highway operations and AUTO-Dolly for airport baggage handling. In this project, RDM Aurrigo will develop the technology to build air cargo handling dollies, capable of carrying loads of up to 7.5t and test these in collaboration with UPS, a global freight handling company, who represent a first customer. An Auto-Cargo prototype will be developed and tested in a live environment, within the UPS hub at East Midlands Airport. To scale-up deployments of autonomous vehicles, RDM will redevelop the Auto-Sim tool for air cargo operations.The project will develop, verify, and validate CAV technology and provide increased understanding of the infrastructure needed for optimum operation. Critically, it will deliver a means for any organisation wishing to implement CAV operations to produce a business case for doing so. We will develop technology that can combine a digital map of any cargo handling area with data about cargo throughput and external factors (e.g. flight schedules). From this data, the optimum number and type of CAVs can be modelled and the tool can illustrate what supporting infrastructure is needed. It will be able to model efficiency (reduced delays and faster aircraft turnarounds) and capacity gains (more flights per hour) from introducing autonomous vehicles and processes. It will also model cost reductions from reduced handling costs and potential revenue increases from greater capacity. Aurrigo will develop and test an Auto-Cargo dolly to validate the digital model which will be operated during a testing phase at East Midlands Airport to validate the technology and develop a supply chain.