Autonomous Cargo

用于工业应用的互联和自动驾驶汽车（CAV）在封闭环境（例如机场）和更广泛的物流应用（例如货运枢纽）中提供安全、便捷和零排放的货物运输，在这些应用中，车辆可以全天候运行以处理货物和运送人员。在机场空侧业务方面，目前工作人员短缺问题十分严重，造成飞机延误和财务损失。此外，频繁的空侧人工操作事故会伤害操作人员，并损坏飞机和货物。该项目将由RDM的自动驾驶汽车部门Aurrigo领导。Aurrigo已经开发和测试了自动驾驶汽车，包括用于公路和非公路运营的载客汽车和用于机场行李处理的汽车。在该项目中，RDM Aurrigo将开发技术，以制造能够承载高达7.5吨的货物的航空货物搬运推车，并与代表第一客户的全球货运处理公司UPS合作进行测试。一个自动货运原型将在东米德兰机场的UPS枢纽内的现场环境中开发和测试。为了扩大自动驾驶车辆的部署，RDM将重新开发用于航空货运运营的Auto-Sim工具。该项目将开发、验证和验证CAV技术，并加深对优化运营所需基础设施的理解。重要的是，它将为任何希望实施CAV操作的组织提供一种方法，以产生这样做的商业案例。我们将开发技术，将任何货物装卸区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什么样的支持基础设施。它将能够通过引入自动驾驶车辆和流程来模拟效率（减少延误和更快的飞机周转）和容量增益（每小时更多的航班）。它还将通过降低处理成本来降低成本，并通过提高产能来增加潜在收入。Aurrigo将开发和测试自动货运小车，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.