Seemal Asif , Mikel Bueno , Pedro Ferreira , Paul Anandan , Ze Zhang , Yue Yao , Gautham Ragunathan , Lloyd Tinkler , Masoud Sotoodeh-Bahraini , Niels Lohse , Phil Webb , Windo Hutabarat , Ashutosh Tiwari
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引用次数: 0
摘要
本研究介绍了可重构和响应式机器人制造(R3M)架构,这是一个新颖的框架,旨在自主适应制造环境中不断变化的产品类型和需求。R3M 的核心是一个集成架构,通过先进的通信平台,确保关键模块之间的无缝数据流。这些模块是提供一系列对运营效率至关重要的服务的核心。该架构的关键是根据 ISO-12100 标准纳入自动风险评估,利用 ROS2 Gazebo 以即插即用的方式动态修改机器人技能。该架构的独特需求定义方法采用了 AutomationML (AML),实现了有效的系统集成和各种信息源的整合。这是通过创新性地使用基于技能的概念和 AML 类库来实现的,从而增强了系统在制造环境中的适应性和集成性。报告深入探讨了 AML 框架内对产品、设备和流程的复杂描述,强调了产品领域对盈利能力的战略考量,并区分了设备特征描述中的原子技能和复合技能。工艺领域是一个宝贵的知识库,通过工艺模式在高层次产品需求和具体设备能力之间架起了桥梁。这些元素在 R3M 框架内汇聚成一个多功能、可扩展的解决方案,有望彻底改变制造流程。实证结果凸显了该架构强大的感知能力,尤其侧重于机器人层压堆叠的实际应用,阐明了 R3M 部署所面临的固有挑战和实际成果。
Rapid and automated configuration of robot manufacturing cells
This study presents the Reconfigurable and Responsive Robot Manufacturing (R3M) architecture, a novel framework engineered to autonomously adapt to fluctuating product variants and demands within manufacturing environments. At the heart of R3M lies an integrated architecture that ensures a seamless data flow between critical modules, facilitated by an advanced communication platform. These modules are central to delivering a range of services crucial for operational efficiency. Key to the architecture is the incorporation of Automated Risk Assessment aligned with ISO-12100 standards, utilizing ROS2 Gazebo for the dynamic modification of robot skills in a plug-and-produce manner. The architecture's unique approach to requirements definition employs AutomationML (AML), enabling effective system integration and the consolidation of varied information sources. This is achieved through the innovative use of skill-based concepts and AML Class Libraries, enhancing the system's adaptability and integration within manufacturing settings. The narrative delves into the intricate descriptions of products, equipment, and processes within the AML framework, highlighting the strategic consideration of profitability in the product domain and distinguishing between atomic and composite skills in equipment characterization. The process domain serves as an invaluable knowledge repository, bridging the gap between high-level product demands and specific equipment capabilities via process patterns. The culmination of these elements within the R3M framework provides a versatile and scalable solution poised to revolutionize manufacturing processes. Empirical results underscore the architecture's robust perception abilities, with a particular focus on a real-world application in robotic lamination stacking, elucidating both the inherent challenges and the tangible outcomes of the R3M deployment.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.