Digital Twin (DT) is a 'living' entity that offers potential with monitoring and improving functionality of interconnected complex engineering systems (CESs). However, lack of approaches for adaptively connecting the existing brownfield systems and their data limits the use of DTs. This paper develops a new DT design framework that uses ontologies to enable co-evolution with the CES by capturing data in terms of variety, velocity, and volume across the asset life-cycle. The framework has been tested successfully on a helicopter gearbox demonstrator and a mobile robotic system across their life cycles, illustrating DT adaptiveness without the data architecture needing to be modified. (C) 2020 The Author( s). Published by Elsevier Ltd on behalf of CIRP.
数字孪生 （DT） 是一个“活生生的”实体，它提供了监控和改进互连复杂工程系统 （CES） 功能的潜力。然而，缺乏自适应连接现有棕地系统及其数据的方法限制了DT的使用。本文开发了一种新的DT设计框架，该框架使用本体，通过在整个资产生命周期中捕获多样性、速度和数量方面的数据，实现与CES的协同演进。该框架已在直升机齿轮箱演示器和移动机器人系统的整个生命周期内成功进行了测试，说明了无需修改数据架构即可实现 DT 适应性。（C） 2020 作者。由Elsevier Ltd代表CIRP发布。

Under the influence of the global COVID-19 pandemic, the demand for medical equipment and epidemic prevention materials has increased significantly, but the existing production lines are not flexible and efficient enough to dynamically adapt to market demand. The human-machine collaboration system combines the advantages of humans and machines, and provides feasibility for implementing different manufacturing tasks. With dynamic adjustment of robots and operators in the production line, the flexibility of the human-machine collaborative production line can be further improved. Therefore, a parallel production line is set up as a parallel community, and the digital twin community model of the intelligent workshop is constructed. The fusion and interaction between the production communities enhance the production flexibility of the manufacturing shop. Aiming at the overall production efficiency and load balancing state, a digital twin-driven intra-community process optimization algorithm based on hierarchical reinforcement learning is proposed, and as a key framework to improve the production performance of production communities, which is used to optimize the proportion of human and machine involvement in work. Finally, taking the assembly process of ventilators as an example, it is proved that the intelligent scheduling strategy proposed in this paper shows stronger adjustment ability in response to dynamic demand as well as production line changes.
在全球COVID-19大流行的影响下，对医疗设备和防疫材料的需求大幅增加，但现有生产线的灵活性和效率不足以动态适应市场需求。人机协作系统结合了人机优势，为实现不同的制造任务提供了可行性。通过对生产线上机器人和操作人员的动态调整，可以进一步提高人机协同生产线的灵活性。因此，将一条平行生产线设置为平行社区，构建了智能车间的数字孪生社区模型。生产社区之间的融合和互动增强了制造车间的生产灵活性。针对整体生产效率和负载均衡状态，提出一种基于分层强化学习的数字孪生驱动的社区内流程优化算法，作为提升生产社区生产绩效的关键框架，用于优化人机参与工作比例。最后，以呼吸机装配过程为例，证明本文提出的智能调度策略在响应动态需求和产线变化时表现出较强的调节能力。

Gesture control is one of the effective and flexible communication method between humans and robots. However, it always depends on complex hardware and configurations in human-robot collaboration systems. Simplifying the design of gesture-interaction systems and avoiding miscommunication are challenging problems. In this paper, we proposed a method that utilizes an RGB sensor to realize spatial human-robot collaboration. A random forest based depth estimator is presented to supplement the additional spatial information for hand gesture recognition. Additionally, we demonstrate the construction of secure human-robot collaboration scenarios in Unity and validate our approach in real-world settings, based on which a digital twin system oriented to human-machine collaboration is constructed to realize rapid human-machine task simulation, safety specification testing, and real-scene applications development.
手势控制是人与机器人之间有效而灵活的通信方式之一。然而，它始终依赖于人机协作系统中的复杂硬件和配置。简化手势交互系统的设计并避免沟通不畅是具有挑战性的问题。本文提出了一种利用RGB传感器实现空间人机协作的方法。提出了一种基于随机森林的深度估计器，以补充手势识别的附加空间信息。此外，我们还演示了Unity中安全人机协作场景的构建，并在真实环境中验证了我们的方法，在此基础上构建了面向人机协作的数字孪生系统，以实现快速的人机任务模拟、安全规范测试和真实场景应用开发。

This article presents a methodology for the design of rehabilitation devices that considers factors involved in a clinical environment. This methodology integrates different disciplines that work together. The methodology is composed by three phases and 13 stages with specific tasks, the first phase includes the clinical context considering the requirements of the patient and therapist during the rehabilitation, the second phase is focused in engineering based on the philosophy of digital twin, and in the third phase is evaluated the device. This article explains the characteristics of the methodology and how it was applied in the design of an exoskeleton for passive rehabilitation of upper limb.
本文介绍了一种考虑临床环境中涉及的因素的康复设备设计方法。这种方法整合了协同工作的不同学科。该方法由三个阶段和 13 个具有特定任务的阶段组成，第一阶段包括考虑康复期间患者和治疗师要求的临床背景，第二阶段侧重于基于数字孪生理念的工程，第三阶段是评估设备。本文解释了该方法的特点，以及如何将其应用于上肢被动康复的外骨骼设计。

PurposeThis paper aim to solve the problem of low assembly success rate for 3c assembly lines designed based on classical control algorithms due to inevitable random disturbances and other factors,by incorporating intelligent algorithms into the assembly line, the assembly process can be extended to uncertain assembly scenarios.Design/methodology/approachThis work proposes a reinforcement learning framework based on digital twins. First, the authors used Unity3D to build a simulation environment that matches the real scene and achieved data synchronization between the real environment and the simulation environment through the robot operating system. Then, the authors trained the reinforcement learning model in the simulation environment. Finally, by creating a digital twin environment, the authors transferred the skill learned from the simulation to the real environment and achieved stable algorithm deployment in real-world scenarios.FindingsIn this work, the authors have completed the transfer of skill-learning algorithms from virtual to real environments by establishing a digital twin environment. On the one hand, the experiment proves the progressiveness of the algorithm and the feasibility of the application of digital twins in reinforcement learning transfer. On the other hand, the experimental results also provide reference for the application of digital twins in 3C assembly scenarios.Originality/valueIn this work, the authors designed a new encoder structure in the simulation environment to encode image information, which improved the model's perception of the environment. At the same time, the authors used the fixed strategy combined with the reinforcement learning strategy to learn skills, which improved the rate of convergence and stability of skills learning. Finally, the authors transferred the learned skills to the physical platform through digital twin technology and realized the safe operation of the flexible printed circuit assembly task.
目的旨在解决基于经典控制算法设计的3C装配线由于不可避免的随机扰动等因素导致装配成功率低的问题，通过将智能算法融入装配线，将装配过程扩展到不确定的装配 scenarios.Design/methodology/approachThis 工作，提出了一种基于数字孪生的强化学习框架。首先，作者使用Unity3D构建了与真实场景相匹配的仿真环境，并通过机器人操作系统实现了真实环境与仿真环境的数据同步。然后，作者在仿真环境中训练了强化学习模型。最后，通过创建数字孪生环境，作者将从仿真中学到的技能转移到真实环境中，并在真实场景下实现稳定的算法部署。研究结果在这项工作中，作者通过建立数字孪生环境，完成了技能学习算法从虚拟环境到现实环境的转移。一方面，实验证明了算法的渐进性以及数字孪生在强化学习迁移中的应用可行性。另一方面，实验结果也为数字孪生在3C装配场景中的应用提供了参考。原创性/价值在这项工作中，作者在仿真环境中设计了一种新的编码器结构来编码图像信息，从而提高了模型对环境的感知。同时，作者采用固定策略结合强化学习策略学习技能，提高了技能学习的收敛率和稳定性。 最后，作者通过数字孪生技术将所学技能转移到物理平台上，实现了柔性印刷电路组装任务的安全运行。

Applying the Multiple Drones System (MDS) to perform the repetitive and dangerous tasks for human in many complex environments has become a trend all around the world, due to the increasing capacity of mobile communication and the increasing intelligence of drone robots. However, to fulfill the target with less cost as much as possible, drones need to collaborate deeply with each other to make the optimal decision, which is now important and challenging. In this work, we focus on addressing the efficient task allocation among large-scale drones with the object of minimizing the resource waste and cost, which is proved to be NP-hard. Specifically, we first introduce the Digital Twin (DT) technology to dynamically construct the spatial structure for drones, in which a density clustering based algorithm is proposed to decompose the large-scale task allocation problem among all drones into smaller sub-problems among partial drones. Then, for each sub-problem, we propose an improved auction algorithm to allocate the sub-tasks to local drones according to the task difficulty and drone ability. The experimental results indicate that the proposed method outperforms the state-of-the-art methods in terms of the moving distance, resource utilization and task completion time, etc.
由于移动通信能力的提高和无人机机器人的智能化程度的提高，在许多复杂环境中应用多无人机系统（MDS）为人类执行重复性和危险的任务已成为全球趋势。然而，为了尽可能以更低的成本实现目标，无人机需要相互深入协作以做出最佳决策，这在现在很重要且具有挑战性。在这项工作中，我们专注于解决大型无人机之间的有效任务分配问题，以最大限度地减少资源浪费和成本，这被证明是NP难的。具体而言，我们首先引入数字孪生（DT）技术动态构建无人机的空间结构，其中提出了一种基于密度聚类的算法，将所有无人机之间的大规模任务分配问题分解为部分无人机之间的较小子问题。然后，针对每个子问题，提出一种改进的拍卖算法，根据任务难度和无人机能力将子任务分配给本地无人机。实验结果表明，所提方法在移动距离、资源利用率和任务完成时间等方面均优于现有方法。

The complexity of the marine environment adds unpredictable difficulties to researchers' exploration. To study the application prospects of high-fidelity digital models to the maritime field, this study adopts the blended -wing-body underwater glider (BWBUG) as an example. A novel construction approach of an underwater simulation platform is proposed based on digital twin (DT) technology. The building process is described from the perspectives of geometry, physics, environment and behavior. Based on the Gazebo simulation platform, the proposed approach includes the construction of a high-fidelity model for underwater scenarios and is equipped with a vehicle body containing various sensors. Under the influence of ocean currents, the twin sensor data are integrated in the proportional-integral-derivative (PID) control algorithm, combined with a robot operating system (ROS), to achieve its pitch attitude control. The overshoot was reduced to at most 0.06% and the settling time is reduced to 29.5 s compared with the traditional PID. The results demonstrate that the proposed framework not only validates the implementability of control algorithms for unmanned systems in actual marine environment, but also has the potential for performance prediction, design improvement, and safety assessment in the next generation vehicle design process.
海洋环境的复杂性给研究人员的探索增加了不可预测的困难。为了研究高保真数字模型在海洋领域的应用前景，本文以混合翼体水下滑翔机（BWBUG）为例。该文提出一种基于数字孪生（DT）技术的水下模拟平台构建方法。从几何、物理、环境和行为的角度描述了建筑过程。基于Gazebo仿真平台，该方法包括构建水下场景的高保真模型，并配备了包含各种传感器的车身。在洋流的影响下，将双传感器数据集成到比例-积分-微分（PID）控制算法中，结合机器人操作系统（ROS），实现其俯仰姿态控制。与传统PID相比，过冲降低至最多0.06%，建立时间降至29.5 s。结果表明，所提框架不仅验证了无人系统控制算法在实际海洋环境中的可实现性，而且在下一代车辆设计过程中具有性能预测、设计改进和安全评估的潜力。

Energy in Brazil is generated predominantly by hydroelectricity. The advantages of hydroelectric power include the fact that it is a clean source of energy. Nevertheless, the impoundment of great amounts of water represents a risk to the area downstream in the face of an accident. Even though the chances of rupture of a dam are small, the consequences are often catastrophic. Therefore, monitoring the structures of a dam is essential to prevent disasters to the environment and population and ensure the safety of its operation. This paper describes the implementation of a real-time online monitoring system for the dam of Campos Novos Power Plant. The concrete-faced rockfill dam (CFRD) is 202 m high and 592 m long on the crest. The system comprises a digital twin, a robotic total station (RTS) system, and other automated sensors. The digital twin is a tridimensional structural model of the dam. The finite element method is used to calculate displacements and stress state of the rockfill and concrete slab. RTS measurements are made hourly ensuring the safe operation of the dam.
巴西的能源主要来自水力发电。水力发电的优势包括它是一种清洁能源。然而，在发生事故时，蓄水对下游地区构成风险。尽管大坝破裂的可能性很小，但后果往往是灾难性的。因此，监测大坝的结构对于防止对环境和人口的灾害并确保其运行安全至关重要。本文介绍了坎波斯诺沃斯电厂大坝实时在线监测系统的实施。混凝土面堆石坝（CFRD）高202米，顶部长592米。该系统包括数字孪生、机器人全站仪 （RTS） 系统和其他自动传感器。数字孪生是大坝的三维结构模型。采用有限元法计算堆石场和混凝土板的位移和应力状态。RTS测量每小时进行一次，确保大坝的安全运行。

Recently, the rapid development of digital twin (DT) technology has been regarded significant in Cyber -physical systems (CPS) promotion. Scholars are focusing on the theoretical architecture and implementing applications, in order to establish a high-fidelity, dynamic, and full-lifecycle DT model and achieve a deep fusion of real and virtual. As a typical complex system with multi-disciplines, multi-physics, and multi-domain characteristics, industrial robot (IR) involves various processes and elements from the two other levels of the system: components and production lines. Their complex relationships lead to a huge challenge to build a comprehensive DT model. Current researchers usually concentrates on single-layer services because of limited construction methodology, which results in enormous isolated models, and leads to low reusable system blocks, finite scalability, and high costs of design, adjustment, upgrade, and maintenance. To address these issues, a standardized methodology and a hierarchical, modular, and generic architecture are proposed to depict comprehensive and variable industrial robot digital twin (IRDT). Firstly, the ontology information model is presented by analyzing variable factors systematically. Then, model-based system engineering (MBSE) based methodology is introduced, including construction process and variants management. After modeling process of three levels (problem domain, solution main, and implementation domain) and four viewpoints (requirement, structure, behavior, and parameter), a generic architecture of IRDT is constructed and a feature-based variants management method is described. Besides, a six-axis IRDTS is implemented to illustrate the mapping of logical architecture and physical system as a multi-level elements and processes representation example. And the steps of numerical evaluations consist of system delay and derivation. Finally, results show the effectiveness and the potential of the proposed theoretical methodology for constructing IRDTS and other industrial applications.
近年来，数字孪生（DT）技术的快速发展在信息物理系统（CPS）的推广中具有重要意义。学者们正专注于理论架构和应用的实现，以期建立一个高保真、动态、全生命周期的DT模型，实现真实与虚拟的深度融合。工业机器人（IR）是一个典型的复杂系统，具有多学科、多物理场和多领域特性，涉及系统其他两个层次的各种过程和元素：组件和生产线。它们之间的复杂关系为构建全面的DT模型带来了巨大的挑战。由于构建方法有限，目前的研究人员通常专注于单层服务，这导致了巨大的孤立模型，并导致可重用的系统模块低、可扩展性有限以及设计、调整、升级和维护的高成本。针对这些问题，该文提出一种标准化的方法和分层、模块化和通用的架构来描述全面多变的工业机器人数字孪生（IRDT）。首先，通过系统分析变量因素来建立本体信息模型;然后，介绍了基于模型的系统工程（MBSE）方法，包括施工过程和变体管理。经过三个层次（问题域、解决方案主点和实现域）和四个视点（需求、结构、行为和参数）的建模过程，构建了IRDT的通用架构，并描述了一种基于特征的变体管理方法。 此外，还实现了一个六轴IRDTS，以说明逻辑架构和物理系统的映射作为多级元素和过程表示示例。数值评估的步骤包括系统延迟和推导。最后，结果表明了所提出的理论方法在构建IRDTS和其他工业应用中的有效性和潜力。

The impossibility of visual inspection and the complexity of combined loads hamper the quantitative assessment, lifetime prediction and control of underground pipelines during their lifecycles. A methodology based on mobile augmented reality (MAR) and Brillouin fiber optic sensors (BFOSs) is presented to build a digital twin (DT) for underground pipelines. Field experiments were carried out to demonstrate that the proposed method can quantitatively assess and predict the structural safety of an underground pipeline from the DT. The results demonstrate that the distributed sensor networks can measure important but unpredictable deformations (i.e., longitudinal bending and axial thermal strain), the joint data-physics driven model can estimate the structural stress state more accurately than the common calculation model, and the MAR-based human-asset interaction interface enables more intuitive, efficient, automated operation and maintenance (O&M). In the future, in-line robotic systems and localized damage models should be further adopted for lifecycle O&M.
目视检查的不可能性和组合荷载的复杂性阻碍了地下管道在其生命周期内的定量评估、寿命预测和控制。提出了一种基于移动增强现实（MAR）和布里渊光纤传感器（BFOS）的方法，用于构建地下管道的数字孪生（DT）。通过现场试验，验证了所提方法能够定量评估和预测DT地下管道的结构安全性。结果表明，分布式传感器网络能够测量重要但不可预测的变形（即纵向弯曲和轴向热应变），数据物理联合驱动模型可以比普通计算模型更准确地估计结构应力状态，基于MAR的人机交互接口能够实现更直观、高效、自动化的运维（O&M）。未来，全生命周期运维应进一步采用在线机器人系统和局部损伤模型。

Industrial robots used to assemble customized products in small batches require a lot of reprogramming. With this work we aim to reduce the programming complexity by autonomously finding the fastest assembly plans without any collisions with the environment. First, a digital twin of the robot uses a gym in simulation to learn which assembly skills (programmed by demonstration) are physically possible (i.e. no collisions with the environment). Only from this reduced solution space will the physical twin look for the fastest assembly plans. Experiments show that the system indeed converges to the fastest assembly plans. Moreover, pre-training in simulation drastically reduces the number of interactions before convergence compared to directly learning on the physical robot. This two-step procedure allows for the robot to autonomously find correct and fast assembly sequences, without any additional human input or mismanufactured products.
用于小批量组装定制产品的工业机器人需要大量的重新编程。通过这项工作，我们的目标是通过自主找到最快的装配计划来降低编程复杂性，而不会与环境发生任何冲突。首先，机器人的数字孪生在模拟中使用健身房来学习哪些组装技能（通过演示编程）在物理上是可行的（即不与环境发生碰撞）。只有从这种减少的解决方案空间中，物理孪生才能找到最快的装配计划。实验表明，该系统确实收敛到最快的装配计划。此外，与直接在物理机器人上学习相比，模拟中的预训练大大减少了收敛前的交互次数。这种两步程序允许机器人自主找到正确和快速的装配顺序，而无需任何额外的人工输入或错误制造的产品。

Traditional manufacturing systems face a major transition challenge toward intelligent and sustainable manufacturing systems. Energy-efficient manufacturing has become one of the main focuses of this transition because of the high and critical consumption of energy for the production of many industrial sectors. To this aim, modeling Energy Consumption (EC) behavior is a primary step to monitor and reduce consumption. However, it requires a deep understanding of the system's kinematic and dynamic behaviors. Hence, creating a model from scratch can be challenging, which may result in a model that does not correctly represent the real system. With the advancement of digital technologies, it is now possible to collect and analyze data from manufacturing systems in real-time. This opens the door to the possibility of modeling the energy consumption behavior of different machine states based on a data-driven approach and keeping the current energy consumption under control and monitored using real-time data from the equipment. Prior research has been conducted in the literature incorporating EC modeling into a Digital Twin (DT). However, the addressed issue remains an open challenge due to its complexity. The proposed methodology solves the lack of literature by proposing a meth-odology that makes the EC modeling within the reach of any researcher or practitioner in the field. The current paper proposes a data-driven methodology for integrating the EC model into Digital Twins. The methodology is based on measurements to identify different segments and sub-states of EC of production equipment, using techniques such as segmentation and regression. It relies on power absorption measurement of industrial equipment to generate EC related-parameters to be fed into the DT model to monitor the current operating condition of the physical system. This work contributes to the DT-based sustainable transition by allowing to monitor and quantitatively measure those parameters which could be controlled to reduce EC. A case study on an industrial robot is used to validate and assess the performance of the approach in a laboratory environment.
传统制造系统面临着向智能和可持续制造系统转型的重大挑战。节能制造已成为这一转型的主要焦点之一，因为许多工业部门的生产能源消耗很高，而且非常关键。为此，对能耗 （EC） 行为进行建模是监控和减少能耗的主要步骤。然而，它需要对系统的运动学和动力学行为有深入的了解。因此，从头开始创建模型可能具有挑战性，这可能会导致模型无法正确表示真实系统。随着数字技术的进步，现在可以实时收集和分析来自制造系统的数据。这为基于数据驱动方法对不同机器状态的能耗行为进行建模打开了大门，并使用来自设备的实时数据来控制和监控当前的能耗。先前的研究已经在文献中进行了，将 EC 建模整合到数字孪生 （DT） 中。然而，由于其复杂性，所解决的问题仍然是一个悬而未决的挑战。所提出的方法通过提出一种方法来解决文献的缺乏问题，该方法使该领域的任何研究人员或从业者都可以进行 EC 建模。本文提出了一种数据驱动的方法，用于将EC模型集成到数字孪生中。该方法基于测量，使用分割和回归等技术来识别生产设备 EC 的不同部分和子状态。 它依靠工业设备的功率吸收测量来生成 EC 相关参数，并将其输入到 DT 模型中，以监控物理系统的当前运行状态。这项工作通过允许监测和定量测量那些可以控制以减少 EC 的参数，为基于 DT 的可持续转型做出了贡献。工业机器人的案例研究用于验证和评估该方法在实验室环境中的性能。

In response to the problem of bowl seedling detachment caused by the shaking of the transplanting machine in plant factories, this paper proposes a physical entity monitoring method for the digital twin (DT) plant factory transplanting system. The method is used to analyze the vibration signals of the transplanting machine under different operating conditions and explore the optimal working conditions. Firstly, a demand analysis for the physical entity of the DT plant factory transplanting system is conducted, focusing on practical applications. Then, an optimal deployment plan is designed based on the axiomatic design (AD) theory. Subsequently, a comparative analysis of the operating conditions of the plant factory transplanting equipment is carried out using data-driven approaches. Finally, the optimal working condition parameters are determined by comparing the modal vibration power spectral density of the transplanting equipment under different operating conditions. The results show that the maximum amplitude occurs in the Z-axis, with a magnitude of 2.1 m/s2. By comparing the cloud maps, it is evident that the vibration trends in the Z-axis and X-axis above the transplanting robotic arm are more pronounced compared to the Y-axis. This indicates that under the operating condition of transplanting 3000 plants per hour, a high transplanting efficiency can be maintained, and the vibration signals in the XYZ-axis above the transplanting robotic arm are relatively smooth, making them suitable for transplanting operations. This study combines digital twin technology to analyze the vibration signals of the plant factory transplanting machine under different operating conditions and explore the optimal working conditions. Compared to traditional monitoring platforms, this method facilitates the real-time visualization of different operating conditions of the transplanting machine in a virtual mapping, providing a more intuitive reflection of the equipment operation status.
针对植物工厂移栽机晃动导致碗状幼苗脱落的问题，提出一种数字孪生（DT）植物工厂移栽系统的物理实体监测方法。该方法用于分析移栽机在不同工况下的振动信号，探索最佳工况。首先，对DT植物工厂移栽系统的物理实体进行了需求分析，重点关注实际应用;然后，基于公理化设计（AD）理论设计最优部署方案;随后，使用数据驱动的方法对植物工厂移栽设备的运行条件进行比较分析。最后，通过比较不同工况下移栽设备的模态振动功率谱密度，确定最佳工况参数。结果表明：最大振幅出现在Z轴上，幅度为2.1 m/s2;通过比较云图，可以明显看出，与Y轴相比，移栽机械臂上方Z轴和X轴的振动趋势更为明显。这表明在每小时移栽3000株的运行条件下，可以保持较高的移栽效率，移栽机械臂上方XYZ轴的振动信号相对平滑，适合移栽作业。本研究结合数字孪生技术，分析植物工厂插栽机在不同工况下的振动信号，探索最佳工况。 与传统的监控平台相比，该方法便于在虚拟映射中实时可视化移栽机的不同运行条件，从而更直观地反映设备运行状态。

The Laboratory Automation Plug & Play (LAPP) framework is an over-arching reference architecture concept for the integration of robots in life science laboratories. The plug & play nature lies in the fact that manual configuration is not required, including the teaching of the robots. In this paper a digital twin (DT) based concept is proposed that outlines the types of information that must be provided for each relevant component of the system. In particular, for the devices interfacing with the robot, the robot positions must be defined beforehand in a device-attached coordinate system (CS) by the vendor. This CS must be detectable by the vision system of the robot by means of optical markers placed on the front side of the device. With that, the robot is capable of tending the machine by performing the pick-and-place type transportation of standard sample carriers. This basic use case is the primary scope of the LAPP-DT framework. The hardware scope is limited to simple benchtop and mobile manipulators with parallel grippers at this stage. This paper first provides an overview of relevant literature and state-of-the-art solutions, after which it outlines the framework on the conceptual level, followed by the specification of the relevant DT parameters for the robot, for the devices and for the facility. Finally, appropriate technologies and strategies are identified for the implementation.
实验室自动化即插即用（LAPP）框架是用于在生命科学实验室中集成机器人的总体参考架构概念。即插即用的本质在于不需要手动配置，包括机器人的教学。本文提出了一个基于数字孪生（DT）的概念，概述了必须为系统的每个相关组件提供的信息类型。特别是，对于与机器人接口的设备，供应商必须事先在设备连接的坐标系 （CS） 中定义机器人位置。机器人的视觉系统必须通过放置在设备正面的光学标记来检测该 CS。这样一来，机器人就能够通过执行标准样品载体的拾取和放置式运输来照料机器。这个基本用例是LAPP-DT框架的主要范围。现阶段，硬件范围仅限于带有平行夹持器的简单台式和移动式机械手。本文首先概述了相关文献和最先进的解决方案，然后概述了概念层面的框架，然后规范了机器人、设备和设施的相关 DT 参数。最后，确定实施的适当技术和策略。

With UAVs becoming increasingly popular in the industry, vertical take-off and landing (VTOL) convertiplanes are emerging as a compromise between the advantages of planes and multicopters. Due to their large wing surface area, VTOL convertiplanes are subject to a strong wind dependence on critical phases such as take-off, landing, and hovering. Developing a new and improved unmanned aerial vehicle (UAV) is often expensive and associated with failures and accidents. This paper proposes the dynamic characterization of a commercial VTOL convertiplane UAV in copter mode and provides a novel method to estimate the aerodynamic forces and moments for any possible wind speed and direction. Starting from Euler's equations of rigid body dynamics, we have derived the mathematical formulation to precisely consider aerodynamic forces and moments caused by any wind speed and direction. This unique approach will allow for VTOL convertiplane UAVs to be trained and tested digitally in take-off, hovering, and landing maneuvers without the cost and hassle of physical testing, and the dependence on existing wind conditions. A digital twin of a VTOL convertiplane UAV in copter mode has been modeled and tested in the Gazebo robotics simulator. Take-off, hovering and landing maneuvers have been compared with and without the wind physics model. Finally, the simulator has been tested against real flight conditions (reproducing the mean wind speed and direction only), showing a natural and realistic behavior.
随着无人机在行业中越来越受欢迎，垂直起降（VTOL）直升飞机正在成为飞机和多旋翼飞机优势之间的折衷方案。由于机翼表面积大，垂直起降直升飞机在起飞、着陆和悬停等关键阶段受到强风的依赖。开发一种新的和改进的无人驾驶飞行器（UAV）通常很昂贵，并且与故障和事故有关。本文提出了直升机模式下商用垂直起降直升飞机无人机的动态表征，并提供了一种新颖的方法来估计任何可能的风速和风向的空气动力和力矩。从欧拉的刚体动力学方程开始，我们推导出了数学公式，以精确考虑由任何风速和风向引起的空气动力和力矩。这种独特的方法将允许垂直起降直升飞机无人机在起飞、悬停和着陆机动中进行数字训练和测试，而无需进行物理测试的成本和麻烦，也无需依赖现有的风力条件。在直升机模式下，垂直起降直升飞机无人机的数字孪生已在 Gazebo 机器人模拟器中进行了建模和测试。起飞、悬停和着陆机动已经与风物理模型进行了比较。最后，模拟器在真实飞行条件下进行了测试（仅再现平均风速和风向），显示出自然逼真的行为。

With the increasing attention to the state and role of people in intelligent manufacturing, there is a strong demand for human-cyber-physical systems (HCPS) that focus on human-robot interaction. The existing intelligent manufacturing system cannot satisfy efficient human-robot collaborative work. However, unlike machines equipped with sensors, human characteristic information is difficult to be perceived and digitized instantly. In view of the high complexity and uncertainty of the human body, this paper proposes a framework for building a human digital twin (HDT) model based on multimodal data and expounds on the key technologies. Data acquisition system is built to dynamically acquire and update the body state data and physiological data of the human body and realize the digital expression of multi-source heterogeneous human body information. A bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) based network is devised to fuse multimodal human data and extract the spatiotemporal features, and the human locomotion mode identification is taken as an application case. A series of optimization experiments are carried out to improve the performance of the proposed BiLSTM-CNN-based network model. The proposed model is compared with traditional locomotion mode identification models. The experimental results proved the superiority of the HDT framework for human locomotion mode identification.
随着人们对人在智能制造中的状态和作用的日益关注，对专注于人机交互的人-信息-物理系统（HCPS）的需求越来越大。现有的智能制造系统无法满足高效的人机协同工作。然而，与配备传感器的机器不同，人类的特征信息很难立即被感知和数字化。针对人体的高度复杂性和不确定性，本文提出了一种基于多模态数据构建人类数字孪生（HDT）模型的框架，并对关键技术进行了阐述。构建数据采集系统，动态采集和更新人体状态数据和生理数据，实现多源异构人体信息的数字化表达。设计了一种基于双向长短期记忆卷积神经网络（BiLSTM-CNN）的网络，融合多模态人类数据并提取时空特征，并以人体运动模式识别为应用案例。针对所提出的基于BiLSTM-CNN的网络模型的性能，进行了一系列优化实验。将所提模型与传统的运动模式识别模型进行了比较。实验结果证明了HDT框架在人体运动模式识别方面的优越性。

In the context of Industry 4.0, a paradigm shift from traditional industrial manipulators to Collaborative Robots (CRs) is ongoing, with the latter serving ever more closely humans as auxiliary tools in many production processes. In this scenario, continuous technological advancements offer new opportunities for further innovating robotics and other areas of next -generation industry. For example, 6G could play a prominent role due to its human -centric view of the industrial domains. In particular, its expected dependability features will pave the way for new applications exploiting highly effective Digital Twin (DT)- and eXtended Reality (XR)-based telepresence. In this work, a novel application for the above technologies allowing two distant users to collaborate in the programming of a CR is proposed. The approach encompasses demanding data flows (e.g., point cloud -based streaming of collaborating users and robotic environment), with network latency and bandwidth constraints. Results obtained by analyzing this approach from the viewpoint of network requirements in a setup designed to emulate 6G connectivity indicate that the expected performance of forthcoming mobile networks will make it fully feasible in principle.
在工业 4.0 的背景下，从传统工业机械手到协作机器人 （CR） 的范式转变正在进行中，后者在许多生产过程中越来越接近人类作为辅助工具。在这种情况下，持续的技术进步为进一步创新机器人和下一代产业的其他领域提供了新的机会。例如，6G可以发挥重要作用，因为它以人为本的工业领域观点。特别是，其预期的可靠性特性将为利用基于高效数字孪生（DT）和扩展现实（XR）的远程呈现的新应用铺平道路。在这项工作中，提出了一种针对上述技术的新颖应用，允许两个远程用户在CR编程中进行协作。该方法包括要求苛刻的数据流（例如，基于点云的协作用户和机器人环境流），以及网络延迟和带宽限制。在旨在模拟6G连接的设置中，从网络需求的角度分析这种方法所获得的结果表明，即将到来的移动网络的预期性能将使其在原则上完全可行。

As autonomous vehicle development continues at growing speeds, so does the need to optimize, diagnose, and test various elements of autonomous systems under different conditions. Since such processes should be carried out in parallel, it may result in bottlenecks in development and increased complexity. The trend for Digital Twins offers a promising option for the diagnosis and testing to be carried out separately from the physical devices, incl. autonomous vehicles in the virtual world. The idea of intercommunication between virtual and physical twins provides possibilities to estimate risks, drawbacks, physical damages to the vehicle's drive systems, and the physical vehicle's critical conditions. Although providing communications between these systems arises at the speed that will be adequate to represent the physical vehicle in the virtual world correctly, it is still a trendy topic. This paper aims to demonstrate the enhancement of communications by using the Robot Operating System (ROS) as a middleware interface between two twinning systems by the example of the autonomous vehicle's propulsion drive. Data gathered from the physical and virtual worlds can be exchanged in the middle to allow for continuous training and optimization of the propulsion drive model, which would lead to more efficient path planning and energy-efficient drive of the autonomous vehicle itself. Additionally, a comparative analysis of ROS and its next version ROS2 is provided, discussing their differences and outlining drawbacks.
随着自动驾驶汽车的发展速度不断加快，在不同条件下优化、诊断和测试自动驾驶系统的各种元素的需求也在增加。由于这些过程应该并行进行，因此可能会导致开发瓶颈和复杂性增加。数字孪生的趋势为诊断和测试提供了一个有前途的选择，可以与物理设备分开进行，包括虚拟世界中的自动驾驶汽车。虚拟孪生和物理孪生之间的相互通信的想法为估计风险、缺点、车辆驱动系统的物理损坏以及物理车辆的临界条件提供了可能性。尽管在这些系统之间提供通信的速度足以正确表示虚拟世界中的物理车辆，但这仍然是一个时髦的话题。本文旨在通过自动驾驶汽车的推进驱动示例，演示使用机器人操作系统（ROS）作为两个孪生系统之间的中间件接口来增强通信。从物理世界和虚拟世界收集的数据可以在中间交换，以便对推进驱动模型进行持续训练和优化，从而实现更高效的路径规划和自动驾驶汽车本身的节能驱动。此外，还对ROS及其下一个版本ROS2进行了比较分析，讨论了它们的差异并概述了它们的缺点。

Robotic arms for industrial applications rely on expensive, complex solutions for high-precision positioning error compensation. Digital twins (DTs) provide virtual representations of physical assets to optimize their engineering performance, which helps address the above issues. To address this problem, this article proposes a DT-driven 3-D position information mutuality and positioning error compensation for robotic arm. A DT model is developed and a virtual sensor is modeled geometrically. Information exchange between the physical and virtual sensor enables the comparison of the actual and target arm pose. Through closed-loop alignment of the physical sensor data with the virtual output, the arm joints are dynamically adjusted to reduce positioning errors. Information mutuality significantly reduces the amount of calculation necessary to determine the robotic arm's actual angle of motion. Experimental results from various positions have confirmed the efficacy of the method, with a remarkable 81.23% reduction in positioning error.
工业应用中的机械臂依赖于昂贵、复杂的解决方案来实现高精度定位误差补偿。数字孪生 （DT） 提供物理资产的虚拟表示，以优化其工程性能，这有助于解决上述问题。针对这一问题，本文提出了一种DT驱动的机械臂三维位置信息互补和定位误差补偿方法。开发了DT模型，并对虚拟传感器进行了几何建模。物理和虚拟传感器之间的信息交换可以比较实际和目标手臂姿势。通过物理传感器数据与虚拟输出的闭环对齐，可以动态调整臂关节以减少定位误差。信息互惠性大大减少了确定机械臂实际运动角度所需的计算量。不同位置的实验结果证实了该方法的有效性，定位误差显著降低了81.23%。

The process of complex product assembly-commissioning has the characteristics of high flexibility and firm dynamics. To overcome the drawbacks of manual assembly, deploying automated and intelligent techniques can greatly boost efficiency, improve flexibility, and enhance the quality control. The human-robot collaborative (HRC) technology combines the advantages of human capabilities and the efficiency and precision of robots. However, current HRC technology lacks of perception and cognitive ability, especially in dynamic environments. Therefore, this paper proposed a digital twin-driven HRC assembly-commissioning framework. In this framework, a virtual-physical mapping environment for HRC is constructed. In order to improve the cognitive ability of robot units to tasks, a motion intention recognition approach is proposed which integrates the feature of part into human joint sequences. To improve the adaptability of the robot unit to tasks, the assembly-commissioning task knowledge graph is developed to extract the action sequence of the robot unit in a timely manner. Moreover, the deep deterministic policy gradient (DDPG) is used to adaptively adjust the robot unit movement path in the process of assembly-commissioning. Finally, the effectiveness of the proposed method is verified by taking a particular type of automobile generator as a case study product.
复杂的产品装配调试过程具有灵活性高、动态性强等特点。为了克服手动装配的缺点，部署自动化和智能化技术可以大大提高效率、提高灵活性并增强质量控制。人机协作（HRC）技术结合了人类能力的优势和机器人的效率和精度。然而，目前的HRC技术缺乏感知和认知能力，特别是在动态环境中。因此，本文提出了一种数字孪生驱动的HRC装配调试框架。在此框架中，构建了HRC的虚拟物理映射环境。为了提高机器人单元对任务的认知能力，该文提出一种将零件特征整合到人体关节序列中的运动意图识别方法。为了提高机器人单元对任务的适应性，开发了装配-调试任务知识图谱，及时提取机器人单元的动作序列。此外，利用深度确定性策略梯度（DDPG）对装配调试过程中的机器人单元运动路径进行自适应调整。最后，以某类汽车发电机为例，验证了所提方法的有效性。

This paper presents a digital twin-driven fault diagnosis approach based on a graphical model and an adaptive extended Kalman filter algorithm for autonomous surface vehicles. In contrast with the traditional adaptive Kalman filter algorithm, where the fault parameters are treated as extended state variables, the newly proposed adaptive extended Kalman filter algorithm estimates the magnitude of the faults by calculating the parameter estimation gains directly from the sensor systems. To this end, the algorithm utilizes data from a global navigation satellite system receiver assuming the persistence of excitation conditions on the control inputs. The algorithm is tested in the Otter, an autonomous surface vehicle developed by Maritime Robotics, in which one of its propellers is faulty. Technically, the digital twin receives real-time data from the sensor system, estimates the magnitude of the actuator faults, and visualizes the results in a web-based application using JavaScript with Three.js library and Mapbox for real-world 3D map generation. Information regarding the magnitude of the faults is important for fault-tolerant control. Simulation and experimental results show the proposed approach is able to detect and estimate the actuator faults accurately.
本文提出了一种基于图形模型的数字孪生驱动故障诊断方法和一种用于自主地面车辆的自适应扩展卡尔曼滤波算法。与传统的自适应卡尔曼滤波算法相比，将故障参数视为扩展状态变量，新提出的自适应扩展卡尔曼滤波算法通过直接计算传感器系统的参数估计增益来估计故障的大小。为此，该算法利用来自全球导航卫星系统接收器的数据，假设控制输入上的激励条件持续存在。该算法在水獭（Otter）中进行了测试，水獭是由Maritime Robotics开发的自主水面航行器，其中一个螺旋桨有故障。从技术上讲，数字孪生从传感器系统接收实时数据，估计执行器故障的程度，并在基于 Web 的应用程序中使用带有 JavaScript 和 Three.js库和 Mapbox 将结果可视化，以生成真实世界的 3D 地图。有关故障大小的信息对于容错控制非常重要。仿真和实验结果表明，所提方法能够准确检测和估计执行器故障。

As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.
随着机器人技术的快速发展，越来越多的关于利用机器人在核电站（NPP）中执行自动化操作和维护任务的新想法正在被研究。然而，出于安全考虑，研究人员几乎没有机会在物理核电厂上测试他们的新机器人解决方案。从这个意义上说，高效和逼真的仿真环境在核电厂自动化系统的开发中起着至关重要的作用。在本文中，我们提出了一种能够实时模拟NPP的三维数字孪生系统的设计。该系统从全范围核电厂模拟器获取数据，以再现工厂的运行条件。此外，一组机器人执行温度和压力测量等检测任务的场景将突出其可用性。该系统可以开发智能机器人群，以便部署用于检查和维护目的。它将通过减少自主控制的不确定性和提供远程干预所需的工具，对许多类型的反应堆的自主控制和运行产生积极影响。

Weld penetration control has emerged as a critical area of research in the field of online control for ensuring the quality of robotic welds. Acoustic signals, which are known for their distinct temporal characteristics, play a pivotal role in the online assessment of weld quality. This study proposes a novel filter bank specifically tailored for robotic welding and investigates the working environment of robot welding. Twenty-six time-domain and frequency-domain features were extracted from weld acoustic signals, and statistical analyses and comparative methods were used to identify variations in defective signal features and interpret their physical significance. By leveraging these acoustic signal characteristics, this study established a predictive identification model and an online feedback controller. The predictive identification model effectively identified different penetration levels in the welding process, and the identification results served as a reference input for online regulation of the welding speed by the controller. Additionally, a digital twin system was developed, where the identification model and controller functioned as digital objects on a computer and an edge computer, respectively. Experimental tests demonstrated the superior performance of the system and model in accurately reflecting welding process penetration, regulating and stabilising the welding speed, and significantly enhancing the welding quality. The research presented in this paper offers notable advantages for the online control of welding penetration.
焊缝熔深控制已成为在线控制领域确保机器人焊缝质量的关键研究领域。声学信号以其独特的时间特性而闻名，在焊接质量的在线评估中起着举足轻重的作用。本研究提出了一种专门针对机器人焊接量身定制的新型滤波器组，并研究了机器人焊接的工作环境。从焊缝声学信号中提取了26个时域和频域特征，并采用统计分析和比较方法识别缺陷信号特征的变化并解释其物理意义。利用这些声学信号特性，建立了预测性识别模型和在线反馈控制器。预测性识别模型有效识别焊接过程中的不同熔深水平，识别结果可作为控制器在线调节焊接速度的参考输入。此外，还开发了数字孪生系统，其中识别模型和控制器分别作为计算机和边缘计算机上的数字对象。实验试验表明，该系统和模型在准确反映焊接工艺熔深、调节和稳定焊接速度、显著提高焊接质量方面具有优越性能。本文的研究为焊接熔深的在线控制提供了显著的优势。

The Internet of Robots (IoR) is proficient in handling complex tasks in challenging environments, yet it encounters challenges related to service and scenario diversity, risk reduction, and ultra-low latency requirements. To address these challenges, we propose an integrated architecture that enhances the IoR's adaptability, flexibility, robustness, and low latency. This is achieved through the introduction of network slicing, service-based architecture, and digital twin (DT). We have developed an open-source experimental platform to showcase the customizability of the proposed architecture. Slices with different requirements are set up in WiFi and cellular scenarios to demonstrate its versatility. Additionally, we present a DT-assisted deep reinforcement learning (DRL) approach for the IoR to improve DRL performance and mitigate risks associated with undesirable actions. The DT is employed to predict rewards and dynamic state transitions in the physical environment. Furthermore, we introduce a resource allocation method that combines data processing queue preemption and spectrum puncturing. This is designed to accommodate coexisting services, specifically enhanced mobile broadband (eMBB) and bursty ultra-reliable low latency communications (URLLC). Experimental and numerical results validate the effectiveness of our proposed methods, showing improvements in customizability, robustness, latency, and outage probability in IoR.
机器人互联网（IoR）擅长在具有挑战性的环境中处理复杂的任务，但它遇到了与业务和场景多样性、降低风险和超低延迟要求相关的挑战。为了应对这些挑战，我们提出了一种集成架构，以增强物联网的适应性、灵活性、鲁棒性和低延迟。这是通过引入网络切片、基于服务的架构和数字孪生 （DT） 来实现的。我们开发了一个开源实验平台来展示所提议架构的可定制性。在WiFi和蜂窝场景下设置了不同需求的切片，以展示其多功能性。此外，我们还提出了一种用于 IoR 的 DT 辅助深度强化学习 （DRL） 方法，以提高 DRL 性能并降低与不良行为相关的风险。DT 用于预测物理环境中的奖励和动态状态转换。此外，我们引入了一种结合了数据处理队列抢占和频谱穿刺的资源分配方法。这旨在适应共存的服务，特别是增强型移动宽带 （eMBB） 和突发超可靠低延迟通信 （URLLC）。实验和数值结果验证了我们提出的方法的有效性，表明了物联网在可定制性、鲁棒性、延迟和中断概率方面的改善。

The composite sheet layup process involves stacking several layers of a viscoelastic prepreg sheet and curing the laminate to manufacture the component. Demands for automating functional tasks in the composite manufacturing processes have dramatically increased in the past decade. A simulation system representing a digital twin of the composite sheet can aid in the development of such an autonomous system for prepreg sheet layup. While finite element analysis (FEA) is a popular approach for simulating flexible materials, material properties need to be encoded to produce high-fidelity mechanical simulations. We present a methodology to predict material parameters of a thin-shell FEA model based on real-world observations of the deformations of the object. We utilize the model to develop a digital twin of a composite sheet. The method is tested on viscoelastic composite prepreg sheets and fabric materials such as cotton cloth, felt, and canvas. We discuss the implementation and development of a high-speed FEA simulator based on the VegaFEM library. By using our method to identify sheet material parameters, the sheet simulation system is able to predict sheet behavior within 5 cm of average error and have proven its capability for 10 fps real-time sheet simulation.
复合板铺层工艺包括堆叠几层粘弹性预浸料板并固化层压板以制造组件。在过去十年中，复合材料制造过程中对功能任务自动化的需求急剧增加。代表复合板数字孪生的仿真系统可以帮助开发这种用于预浸料板铺层的自主系统。虽然有限元分析 （FEA） 是仿真柔性材料的常用方法，但需要对材料属性进行编码才能生成高保真力学仿真。我们提出了一种基于对物体变形的真实观察来预测薄壳有限元分析模型的材料参数的方法。我们利用该模型开发复合片材的数字孪生。该方法在粘弹性复合预浸料片材和棉布、毛毡、帆布等织物材料上进行了测试。我们讨论了基于VegaFEM库的高速有限元分析仿真器的实现和开发。通过使用我们的方法识别板材参数，板材模拟系统能够预测平均误差 5 cm 范围内的板材行为，并证明了其 10 fps 实时板材模拟的能力。

This paper presents a new system architecture for controlling industrial devices using Mixed Reality (MR) applications and a new method based upon them for measuring the distance between real and virtual points. The research has been carried out using a physical robot and its Digital Twin (DT). The possibility of controlling them using gestures recognized by Mixed Reality goggles has been presented. The extension of the robot's environment with a 3D model capable of following its movements and positions was also analyzed. The system was supervised by an industrial Programmable Logic Controller (PLC) serving as an end point for the data sent by the goggles and controlling the movements of the real robot by activating the corresponding control. The results of the preliminary measurements presented here concerned the responsiveness of the system and showing the influence of system parameters in the accuracy of distance estimation between measured points.
本文介绍了一种使用混合现实 （MR） 应用程序控制工业设备的新系统架构，以及一种基于它们测量真实点和虚拟点之间距离的新方法。该研究是使用物理机器人及其数字孪生（DT）进行的。已经提出了使用混合现实护目镜识别的手势来控制它们的可能性。还分析了机器人环境的扩展，以及能够跟踪其运动和位置的3D模型。该系统由工业可编程逻辑控制器（PLC）监督，该控制器作为护目镜发送的数据的端点，并通过激活相应的控制来控制真实机器人的运动。这里介绍的初步测量结果涉及系统的响应性，并显示了系统参数对测量点之间距离估计精度的影响。