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.

通过使用自适应机器人的敏捷制造是提供高级制造，即行业4.0，以使自定义，大规模但小批量尺寸的产品更有效，精益和成本效益。它被认为是由于强烈依赖手动劳动而阻碍的制造业的最终解决方案。航空航天行业是由于缺乏自动化导致积压新飞机订单的自动化而大大遭受的行业之一，并防止其快速移动以采用更有效的飞机设计和高级材料。通过在制造新飞机中使用机器人的自动化水平提高，不仅降低了成本，而且还提高了航空航天行业的质量和安全性。但是，由于批量小的尺寸，较大的组件，产品多样性以及高度的复杂性和操作变化，因此航空航天行业的组装系列中使用的传统工业机器人对航空航天行业不足。因此，在航空航天行业中，当前针对每个特定任务的机器人进行编程或教机器人的实践是有限的，即使不是徒劳的。在高级制造和行业4.0中，机器人具有智能，高度适应性，可以接受培训以处理不同的设备，工具，产品和材料而无需明确的编程。但是，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体验来训练机器人，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体验和模拟中学习的可行性。在拟议的项目中，我们旨在开发一种方法，以使用模拟和虚拟环境中的学习结果来稳健地训练多臂自适应机器人，以进行广泛的航空航天制造过程。这项研究合作伙伴关系汇集了Hi-Tech行业的加拿大著名公司UBC，Kinova Inc.和Element AI，并与德国航空航天中心DLR合作，为航空航天行业的制造业制造更有效的解决方案。