面向人-机-环境共融的数字孪生协同技术

Digital-twin Collaborative Technology for Human-robot-environment Integration

数字孪生正在制造系统中发挥重要作用,然而在面向人机协助完成的复杂制造场景中,人-机-环境及其构成的数字孪生系统呈现出任务异构复杂、环境动态多变及其交互实时等特点。目前欠缺人-机-环境共融的数字孪生协同过程中智能方法相关研究,尤其是数字孪生模型在协同中的迁移和强化,以满足制造系统的鲁棒性和自适应能力。提出面向人-机-环境共融的数字孪生协同技术,从环境和任务两个核心来展开数字孪生协同的人机共融科学问题。首先给出协作装配环境的数字孪生体系,以虚拟装配的形式为人-机-任务交互提供理解;建立相应的空间模型与协同模型,为共融的孪生协同提供理论支持;最后,以最典型的人机共融制造场景(装配任务)为案例,在决策层基于迁移学习算法为机器人提供装配操作指引,同时通过强化学习算法优化机器人的具体执行动作。在不同型号产品的人机协同装配任务中,均可以生成相应的人机协作装配规划方案,证明了所提方法的可行性。

Digital twin is playing an important role in manufacturing system. However, in the complex manufacturing scene for human-robot collaboration, human-robot-environment and its digital twin system show the characteristics of heterogeneous and complex tasks, dynamic environment and real-time interaction. At present, the research on intelligent methods in the digital twin collaboration process of human-robot-environment integration is poor, especially the transfer and reinforcement of digital twin model in collaboration, so as to meet the robustness and adaptive ability of manufacturing system. The paper puts forward the digital twin collaboration technology for human-robot-environment integration, and launches the scientific problem of human-robot integration in digital twin collaboration from the two cores of environment and task. Firstly, the digital twin model of collaborative assembly environment is given to provide understanding for human-robot-task interaction in the form of virtual assembly;Secondly, the corresponding spatial model and collaboration model are established to provide theoretical support for the twin collaboration of integration;Finally, taking the most typical human-robot integrated manufacturing scenario(assembly task) as an example, the transfer learning algorithm is used to provide assembly operation guidance for the robot at the decision-making level, and the reinforcement learning algorithm is used to optimize the specific execution actions of the robot. In different types of products, the corresponding human-robot collaborative assembly planning schemes can be generated, which proves the feasibility of the proposed method.