Abstract Industrial cloud robotics (ICR) integrates cloud computing with industrial robots (IRs). The capabilities of industrial robots can be encapsulated as cloud services and used for ubiquitous manufacturing. Currently, the digital models for process simulation, path simulation, etc. are encapsulated as cloud services. The digital models in the cloud may not reflect the real state of the physical robotic manufacturing systems due to inaccurate or delayed condition update and therefore result in inaccurate simulation and robotic control. Digital twin can be used to realize fine sensing control of the physical manufacturing systems by a combination of high-fidelity digital model and sensory data. In this paper, we propose a framework of digital twin-based industrial cloud robotics (DTICR) for industrial robotic control and its key methodologies. The DTICR is divided into physical IR, digital IR, robotic control services, and digital twin data. First, the robotic control capabilities are encapsulated as Robot Control as-a-Service (RCaaS) based on manufacturing features and feature-level robotic capability model. Then the available RCaaSs are ranked and parsed. After manufacturing process simulation with digital IR models, RCaaSs are mapped to physical robots for robotic control. The digital IR models are connected to the physical robots and updated by sensory data. A case is implemented to demonstrate the workflow of DTICR. The results show that DTICR is capable to synchronize and merge digital IRs and physical IRs effectively. The bidirectional interaction between digital IRs and physical IRs enables fine sensing control of IRs. The proposed DTICR is also flexible and extensible by using ontology models.

中文翻译：

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基于数字孪生的工业云机器人：框架、控制方法和实现

摘要 工业云机器人 (ICR) 将云计算与工业机器人 (IR) 集成在一起。工业机器人的能力可以封装成云服务，用于泛在制造。目前，用于过程仿真、路径仿真等的数字模型被封装为云服务。由于条件更新不准确或延迟，云中的数字模型可能无法反映物理机器人制造系统的真实状态，从而导致仿真和机器人控制不准确。数字孪生可以通过高保真数字模型和传感数据的结合，实现对物理制造系统的精细传感控制。在本文中，我们提出了一个基于数字孪生的工业云机器人（DTICR）框架，用于工业机器人控制及其关键方法。DTICR 分为物理红外、数字红外、机器人控制服务和数字孪生数据。首先，基于制造特征和特征级机器人能力模型，将机器人控制能力封装为机器人控制即服务（RCaaS）。然后对可用的 RCaaS 进行排名和解析。在使用数字 IR 模型模拟制造过程后，RCaaS 被映射到物理机器人以进行机器人控制。数字红外模型连接到物理机器人并通过感官数据进行更新。实施了一个案例来演示 DTICR 的工作流程。结果表明，DTICR 能够有效地同步和合并数字 IR 和物理 IR。数字 IR 和物理 IR 之间的双向交互可实现对 IR 的精细传感控制。