A scaling algorithm for the generation of jerk-limited trajectories in the operational space

Kinematic singularities represent a relevant problem for trajectories that are defined in the operational space. In case of industrial applications characterized by non-repetitive tasks, feasibility cannot be checked in advance, so that appropriate methods have been developed for the online management of otherwise critical situations. In this paper, a scaling scheme proposed in the past for the automatic handling of possibly unfeasible trajectories is revised in order to generate jerk-limited reference signals: close to critical points, trajectories are appropriately slowed down so as to guarantee an accurate tracking of the assigned path in the operational space. The actual performances of the proposed system have been experimentally verified on a commercial manipulator by means of extensive tests.     

Applications of virtual reality in maintenance during the industrial product lifecycle: A systematic review

As a state-of-the-art computer technology, virtual reality (VR) is considered to play an important role in helping manufacturing companies stay competitive in the international market. However, despite the achievements made in the field of VR, it is still an emerging technology that lacks deeper exploration and development in industrial application scenarios, especially in the coming fourth industrial revolution (Industry 4.0). This paper aims to systematically investigate the applications of VR in industrial maintenance to discover evidence of its values, limitations, and future directions so that VR can be guided to better serve manufacturing enterprises in remaining competitive in the coming Industry 4.0. A systematic literature review (SLR) methodology is adopted to review primary studies on this topic, by which 86 studies are ultimately included. The results show that VR has proved its value in benefiting maintenance issues through the product lifecycle. However, VR is still not an indispensable element for the lifecycle management of products regarding maintenance-related issues. Several key findings are concluded based on the analysis of the 86 studies. This review is valuable for researchers who are interested in the application of VR technology in maintainability design, maintenance training or maintenance task assistance.     

The biological transformation of industrial manufacturing – Technologies,status and scenarios for a sustainable future of the German manufacturing industry

The German manufacturing industry is forced to evolve its processes, techniques, and organizations due to increasing global competition and progressive sustainability requirements. In this context, the soaring possibilities of bio- and information technology have recently let few authors develop the vision of a biological transformation of manufacturing, a concept that to date has been barely concrete to politicians, scientists, and managers. In this paper, we present results of the first systematic assessment of the biological transformation of the German manufacturing industry. We chose a combination of the Delphi method and scenario planning in order to assess key technologies, determine the status quo of Germany and provide a forecast of potential developments. Thereupon, we identify ten fields of action for setting the course for a sustainable industrial value creation. We conclude with a summary and recommendations for decision makers in politics, industries and research.     

适合无线传感器网络的自适应动态电压调节算法*

对于能量有限的传感器网络,在计算复杂度较高的应用中,节省CPU的能耗具有重要意义。针对以事件为驱动的无线传感器网络的任务模式,提出一种基于零散任务模型的自适应DVS算法——ADVS。ADVS算法根据CPU的任务量实时调整工作频率和电压,能在很大程度上降低CPU能耗的同时,保证任务的实时性要求。理论分析和实验结果表明,ADVS算法的实际节能效果接近理论分析值的80%左右,可在很大程度上延长节点的生命周期。     

Deep learning-based method for vision-guided robotic grasping of unknown objects

Nowadays, robots are heavily used in factories for different tasks, most of them including grasping and manipulation of generic objects in unstructured scenarios. In order to better mimic a human operator involved in a grasping action, where he/she needs to identify the object and detect an optimal grasp by means of visual information, a widely adopted sensing solution is Artificial Vision. Nonetheless, state-of-art applications need long training and fine-tuning for manually build the object’s model that is used at run-time during the normal operations, which reduce the overall operational throughput of the robotic system. To overcome such limits, the paper presents a framework based on Deep Convolutional Neural Networks (DCNN) to predict both single and multiple grasp poses for multiple objects all at once, using a single RGB image as input. Thanks to a novel loss function, our framework is trained in an end-to-end fashion and matches state-of-art accuracy with a substantially smaller architecture, which gives unprecedented real-time performances during experimental tests, and makes the application reliable for working on real robots. The system has been implemented using the ROS framework and tested on a Baxter collaborative robot.     

Utilizing cable winding and industrial robots to facilitate the manufacturing of electric machines

Cable wound electric machines are used mainly for high voltage and direct-drive applications. They can be found in areas such as wind power, hydropower, wave power and high-voltage motors. Compared to conventional winding techniques, cable winding includes fewer manufacturing steps and is therefore likely to be better suited for automated production. Automation of the cable winding production step is a crucial task in order to lower the manufacturing costs of these machines. This article presents a production method using industrial robots for automation of cable winding of electric machine stators. The concept presented is validated through computer simulations and full-scale winding experiments, including a constructed robot-held cable feeder tool prototype. A cable wound linear stator section of an Uppsala University Wave Energy Converter and its winding process is used as a reference in this article. From this example, it is shown that considerable production cycle time and manufacturing cost savings can be anticipated compared to manual winding. The suggested automation method is very flexible. It can be used for the production of cable wound stators with different shapes and sizes, for different cable dimensions and with different winding patterns.     

Towards sustainable industry 4.0: A green real-time IIoT multitask scheduling architecture for distributed 3D printing services

As the keystones of the personalized manufacturing, the Industrial Internet of Things (IIoT) consolidated with 3D printing pave the path for the era of Industry 4.0 and smart manufacturing. By resembling the age of craft manufacturing, Industry 4.0 expedites the alteration from mass production to mass customization. When distributed 3D printers (3DPs) are shared and collaborated in the IIoT, a promising dynamic, globalized, economical, and time-effective manufacturing environment for customized products will appear. However, the optimum allocation and scheduling of the personalized 3D printing tasks (3DPTs) in the IIoT in a manner that respects the customized attributes submitted for each model while satisfying not only the real-time requirements but also the workload balancing between the distributed 3DPs is an inevitable research challenge that needs further in-depth investigations. Therefore, to address this issue, this paper proposes a real-time green-aware multi-task scheduling architecture for personalized 3DPTs in the IIoT. The proposed architecture is divided into two interconnected folds, namely, allocation and scheduling. A robust online allocation algorithm is proposed to generate the optimal allocation for the 3DPTs. This allocation algorithm takes into consideration meeting precisely the customized user-defined attributes for each submitted 3DPT in the IIoT as well as balancing the workload between the distributed 3DPs simultaneously with improving their energy efficiency. Moreover, meeting the predefined deadline for each submitted 3DPT is among the main objectives of the proposed architecture. Consequently, an adaptive real-time multi-task priority-based scheduling (ARMPS) algorithm has been developed. The built ARMPS algorithm respects both the dynamicity and the real-time requirements of the submitted 3DPTs. A set of performance evaluation tests has been performed to thoroughly investigate the robustness of the proposed algorithm. Simulation results proved the robustness and scalability of the proposed architecture that surpasses its counterpart state-of-the-art architectures, especially in high-load environments.     

A human-in-the-loop manufacturing control architecture for the next generation of production systems

In recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.     

Generative adversarial one-shot diagnosis of transmission faults for industrial robots

Transmission systems of industrial robots are prone to get failures due to harsh operating environments. Fault diagnosis is of great significance for realizing safe operations for industrial robots. However, it is difficult to obtain faulty data in real applications. To migrate this issue, a generative adversarial one-shot diagnosis (GAOSD) approach is proposed to diagnose robot transmission faults with only one sample per faulty pattern. Signals representing kinematical characteristics were acquired by an attitude sensor. A bidirectional generative adversarial network (Bi-GAN) was then trained using healthy signals. Inspired by way of human thinking, the trained encoder in Bi-GAN was taken out to perform information abstraction for all signals. Finally, the abstracted signals were sent to a random forest for the one-shot diagnosis. The performance of the present technique was evaluated on an industrial robot experimental setup. Experimental results show that the proposed GAOSD has promising performance on the fault diagnosis of robot transmission systems.     

Incorporating sustainable criteria in a dynamic multi-objective recommendation planning tool for a continuous manufacturing process: A dairy case study

The activity of scheduling the production plan with the aim of achieving an optimal criterion has been explored in literature for several manufacturing sectors, in particular when it comes to solving scheduling NP-complete problems. In Dairy Manufacturing, determining an optimum criterion for the scheduling process has numerous internal and external challenges due to the complexity of this environment.The initial stages in the Dairy process are characterised by a continuous manufacturing environment and specific operational issues are observable: interruptions for the accomplishment of Cleaning-In-Place (CIP); a short raw material lifespan which demands a fast processing rate; and the stochastic raw material supply variation. By highlighting these three aspects, a critical trade-off emerges: CIP cycle-times heavily reduce the processing capacity, whereas the raw material processed requires an increase in available capacity due to the impact of seasonality, perishability and stochastic deliveries. Therefore, the scheduling plan must be dynamically readapted based on the current inventory, volume and frequency supplied, CIP cycle-times, maximum equipment running hours and downstream capacities.The aim of this research is to develop an integrated approach for generating equipment schedules under supply uncertainty typically observed in the dairy sector where criteria of sustainability are effortlessly incorporated for an improved decision-making process. An efficient Multi-objective Algorithm (MOA) combining conflicting key performance metrics such as minimising Work-In-Process (WIP), maximising Service Level Agreement (SLA), Utilisation and Energy consumption is proposed.The novelty consists of the ability to dynamically select trade-off criteria and visualise the optimum production plan according to the conditions defined by the decision-maker. The appropriate schedules are presented in a Pareto Frontier graph highlighting the entire non-dominance region according to the volume and frequency supplied. Even though sustainability metrics are usually ignored during production plan definitions, namely when a weak correlation between both environmental and profitable criteria is identified, the results demonstrate improved performance when both sustainable approaches are well explored.     

A parameter identification approach using optimal exciting trajectories for a class of industrial robots

In this paper, the problem of finding optimal exciting trajectories for parameter identification of industrial robots is investigated. A cost function of maximizing the minimum singular value of a recursive matrix is used in the optimization procedure. The optimal exciting trajectories obtained is insensitive with respect to the parameter perturbation. The identification accuracy and convergence speed or parameters is improved.     

Trajectory time scaling of a mobile robot to avoid dynamic obstacles on the basis of the INLVO

To ensure the collision safety of mobile robots, the velocity of dynamic obstacles should be considered while planning the robot’s trajectory for high-speed navigation tasks. A planning scheme that computes the collision avoidance trajectory by assuming static obstacles may result in obstacle collisions owing to the relative velocities of dynamic obstacles. This article proposes a trajectory time-scaling scheme that considers the velocities of dynamic obstacles. The proposed inverse nonlinear velocity obstacle (INLVO) is used to compute the nonlinear velocity obstacle based on the known trajectory of the mobile robot. The INLVO can be used to obtain the boundary conditions required to avoid a dynamic obstacle. The simulation results showed that the proposed scheme can deal with typical collision states within a short period of time. The proposed scheme is advantageous because it can be applied to conventional trajectory planning schemes without high computational costs. In addition, the proposed scheme for avoiding dynamic obstacles can be used without an accurate prediction of the obstacle trajectories owing to the fast generation of the time-scaling trajectory.     

基于X86平台和RSI的工业机器人步态自动控制系统设计

工业环境中的任务和工作场景是动态变化的,机器人需要能够根据环境的变化调整步态,以满足新的任务需求。为此,设计基于X86平台和RSI的工业机器人步态自动控制系统。以复杂指令集计算机为基础的X86架构设计机器人控制器主板,使系统具有高集成度和扩展性。利用超声波传感器和红外线传感器获取步态自动控制传感信号。使用基于AS5040型高精密非接触磁性转动编码器的步态关节控制器,通过总线扩展,定位关节运动方向。分析机械臂前后摆动步态规划轨迹,控制髋关节。使用RSI应用程序包控制点位运动,实现步态自动控制。实验结果表明,设计系统的膝关节x方向与实际轨迹只存在最大为20mm的误差,y方向与实际轨迹一致;髋关节x方向与实际轨迹只存在最大为20mm的误差,y方向与实际轨迹只存在最大为15mm的误差,能够提高控制精度,控制效果较好。     

Computation of effective regge trajectories for high energy two-body reactions

Title of program: EFFECTIVE REGGE TRAJECTORIES Catalogue number: ABCE Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland (see application form in this issue). Computer: IBM 360/67; Installation: Northumbrian Universities Multiple Access Computer, Durham, UK Operating system or monitor under which the program is executed: MICHIGAN TERMINAL SYSTEM Programming language used: FORTRAN IV High speed storage required: 46K words. No. of bits in a word: 32 Is the program overlaid? No No. of magnetic tapes required: None What other peripherals are used? Card Reader; Line Printer No. of cards in combined program and test deck: 1381 Card punching code: EBCDIC     

An accelerated alignment method for analyzing time sequences of industrial alarm floods

In industrial processes, analyzing and predicting process faults are quite important, which could help operators to take timely and effective responses to ensure process safety and prevent further losses, especially during alarm floods. Various fault analysis methods have been proposed so far, among which the alarm flood sequence alignment (AFSA) methods, unlike other traditional model-based or statistical methods, provide fault inference from the perspective of alarm sequence similarity assessment. A new AFSA method, the match-based accelerated alignment (MAA) is proposed to generate insightful and informative alarm sequence alignments. MAA focuses mainly on alarm match analysis and outperforms other methods in terms of robustness towards nuisance alarms and improved computational efficiency. More importantly, the alarm time information is well considered and explored in MAA, rendering its alignment results capable of revealing the real similarity of alarm floods. Numerical examples and a real chemical plant case are studied to demonstrate the effectiveness and efficiency of the proposed MAA method.     

Some aspects of dynamic 3D representation and control of industrial processes via the Internet

In our research we examine and use 3D representation of industrial processes, for example the novel methods of Incremental Sheet Forming. We also test 3D imaging methods on our industrial robot solving the Rubik's Cube. We have created 3D models of our robots and their environment in our laboratory to examine the behavior of different industrial processes both in the real, and in the 3D virtual environment. We have connected the 3D model with the real system with which we could extend the features of our robots with some services that exits in the virtual space. We have also established synchronized connections of the real and virtual systems, which enables us to control the real robots and machines from its 3D model via the Internet.     

A novel approach for the detection of anomalous energy consumption patterns in industrial cyber-physical systems

Most scenarios emerging from the Industry 4.0 paradigm rely on the concept of cyber-physical production systems (CPPS), which allow them to synergistically connect physical to digital setups so as to integrate them over all stages of product development. Unfortunately, endowing CPPS with AI-based functionalities poses its own challenges: although advances in the performance of AI models keep blossoming in the community, their penetration in real-world industrial solutions has not so far developed at the same pace. Currently, 90% of AI-based models never reach production due to a manifold of assorted reasons not only related to complexity and performance: decisions issued by AI-based systems must be explained, understood and trusted by their end users. This study elaborates on a novel tool designed to characterize, in a non-supervised, human-understandable fashion, the nominal performance of a factory in terms of production and energy consumption. The traceability and analysis of energy consumption data traces and the monitoring of the factory's production permit to detect anomalies and inefficiencies in the working regime of the overall factory. By virtue of the transparency of the detection process, the proposed approach elicits understandable information about the root cause from the perspective of the production line, process and/or machine that generates the identified inefficiency. This methodology allows for the identification of the machines and/or processes that cause energy inefficiencies in the manufacturing system, and enables significant energy consumption savings by acting on these elements. We assess the performance of our designed method over a real-world case study from the automotive sector, comparing it to an extensive benchmark comprising state-of-the-art unsupervised and semi-supervised anomaly detection algorithms, from classical algorithms to modern generative neural counterparts. The superior quantitative results attained by our proposal complements its better interpretability with respect to the rest of algorithms in the comparison, which emphasizes the utmost relevance of considering the available domain knowledge and the target audience when design AI-based industrial solutions of practical value. Finally, the work described in this paper has been successfully deployed on a large scale in several industrial factories with significant international projection.