AIARA: Artificial Intelligence Enabled Highly Adaptive Robots for Aerospace Industry

AIARA：人工智能为航空航天工业提供高度自适应机器人

• 批准号: 543881-2019
• 负责人: Najjaran, Homayoun
• 金额: $ 9.62万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=693440
• 关键词: AIARA; Artificial; Intelligence; Enabled; Highly

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.

使用自适应机器人的敏捷制造是先进制造、工业4.0的提供，能够更高效、精益和更具成本效益地生产定制化、大规模但小批量的产品。它被认为是严重依赖体力劳动的制造业的最终解决方案。航空航天工业是因缺乏自动化而遭受严重影响的行业之一，导致新飞机订单积压，并阻碍其快速采取更高效的飞机设计和先进材料。通过在制造新飞机时使用机器人来提高自动化水平，不仅迫切需要降低成本，而且还需要提高航空航天业的质量和安全性。然而，由于批量小、部件大、产品多样性以及操作的复杂性和变化性高，用于汽车工业和电子设备装配线的传统工业机器人不适用于航空航天工业。因此，目前在航空航天工业中针对每项特定任务对机器人进行编程或教学的做法即使不是徒劳，也是有限的。在先进制造和工业 4.0 中，机器人具有智能性、适应性强，可以经过训练来处理不同的设备、工具、产品和材料，而无需显式编程。然而，基于人工智能的学习方法需要大量数据来捕获所有可能的物理经验来训练机器人，这可能过于昂贵或不可用。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，利用模拟和虚拟环境的学习结果，为广泛的航空航天制造工艺稳健地训练多臂自适应机器人。这一研究合作伙伴关系汇集了 UBC、Kinova Inc. 和 Element AI 这两家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，为航空航天业更有效的制造构建解决方案。