AIARA: Artificial Intelligence Enabled Highly Adaptive Robots for Aerospace Industry

使用自适应机器人的敏捷制造是先进制造业的一种，工业4.0，能够更高效，更精益和更具成本效益地生产定制的，大规模但小批量的产品。它被认为是制造业因严重依赖体力劳动而受阻的最终解决方案。航空航天工业是那些严重缺乏自动化的行业之一，导致新飞机订单积压，并阻止其快速采用更有效的飞机设计和先进材料。通过使用机器人制造新飞机来提高自动化水平，不仅降低了成本，而且提高了航空航天工业的质量和安全性。然而，传统的工业机器人用于汽车工业和电子设备的装配线是不够的航空航天工业，因为小批量，大部件，产品的多样性和高度的复杂性和操作的变化。因此，在航空航天工业中，目前为机器人编程或教授每项特定任务的做法即使不是徒劳的，也是有限的。在先进制造业和工业4.0中，机器人是智能的，具有高度的适应性，可以训练来处理不同的设备，工具，产品和材料，而不需要明确的编程。然而，基于人工智能的学习方法需要大量的数据来捕捉所有可能的物理经验来训练机器人，这可能太昂贵或不可用。机器人技术的最新进展证明了从合成机器人经验和模拟中学习的可行性。在拟议的项目中，我们的目标是开发一种方法，使用仿真和虚拟环境的学习结果，以强大的训练多臂自适应机器人，用于各种航空航天制造过程。这项研究合作伙伴关系汇集了UBC、Kinova Inc.、和Element AI，这两家公司都是加拿大高科技领域的知名公司，并与德国航空航天中心DLR合作，为航空航天工业更有效的制造提供解决方案。

Agile manufacturing by the use of adaptive robots is the provision of advanced manufacturing, Industry 4.0, to enable more efficient, lean and cost-effective production of customized, large-scale but small batch size products. It is considered to be the ultimate solution for manufacturing industries hindered by their heavy reliance on manual labor. Aerospace industry is one of those industries that suffers greatly from lack of automation causing a backlog of new aircraft orders and prevents it from moving fast enough to adopt more efficient aircraft designs and advanced materials. An increased level of automation via the use of robots in manufacturing new aircrafts is pressing for not only cost reduction but also improved quality and safety in the aerospace industry. However, traditional industrial robots used in assembly lines of automotive industry and electronic devices is inadequate for the aerospace industry, because of small batch sizes, large components, diversity of products and a high level of complexity and variation in operations. Thus, the current practice of programming or teaching a robot for every specific task is limited, if not futile, in the aerospace industry. In advanced manufacturing and Industry 4.0, robots are intelligent, highly adaptive and can be trained to handle different equipment, tools, products and materials without a need for explicit programming. However, artificial intelligence-based learning methods require a large volume of data for capturing all possible physical experiences to train the robot, which can be too expensive or unavailable. Recent advances in robotics demonstrate the feasibility of learning from synthetic robot experiences and simulations. In the proposed project, we aim to develop a methodology to use learning results from simulation and virtual environments to robustly train a multi-arm adaptive robot for a wide range of aerospace manufacturing processes. This research partnership brings together the UBC, Kinova Inc., and Element AI, both prominent Canadian companies in the hi-tech sector, and in collaboration with the German Aerospace Centre DLR to build solutions for more effective manufacturing in aerospace industry.