Showing papers in "Robotics and Computer-integrated Manufacturing in 2021"

Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review

Abstract: Human–robot collaboration is a main technology of Industry 4.0 and is currently changing the shop floor of manufacturing companies. Collaborative robots are innovative industrial technologies introduced to help operators to perform manual activities in so called cyber-physical production systems and combine human inimitable abilities with smart machines strengths. Occupational health and safety criteria are of crucial importance in the implementation of collaborative robotics. Therefore, it is necessary to assess the state of the art for the design of safe and ergonomic collaborative robotic workcells. Emerging research fields beyond the state of the art are also of special interest. To achieve this goal this paper uses a systematic literature review methodology to review recent technical scientific bibliography and to identify current and future research fields. Main research themes addressed in the recent scientific literature regarding safety and ergonomics (or human factors) for industrial collaborative robotics were identified and categorized. The emerging research challenges and research fields were identified and analyzed based on the development of publications over time (annual growth).

A big data-driven framework for sustainable and smart additive manufacturing

Abstract: From the last decade, additive manufacturing (AM) has been evolving speedily and has revealed the great potential for energy-saving and cleaner environmental production due to a reduction in material and resource consumption and other tooling requirements. In this modern era, with the advancements in manufacturing technologies, academia and industry have been given more interest in smart manufacturing for taking benefits for making their production more sustainable and effective. In the present study, the significant techniques of smart manufacturing, sustainable manufacturing, and additive manufacturing are combined to make a unified term of sustainable and smart additive manufacturing (SSAM). The paper aims to develop framework by combining big data analytics, additive manufacturing, and sustainable smart manufacturing technologies which is beneficial to the additive manufacturing enterprises. So, a framework of big data-driven sustainable and smart additive manufacturing (BD-SSAM) is proposed which helped AM industry leaders to make better decisions for the beginning of life (BOL) stage of product life cycle. Finally, an application scenario of the additive manufacturing industry was presented to demonstrate the proposed framework. The proposed framework is implemented on the BOL stage of product lifecycle due to limitation of available resources and for fabrication of AlSi10Mg alloy components by using selective laser melting (SLM) technique of AM. The results indicate that energy consumption and quality of the product are adequately controlled which is helpful for smart sustainable manufacturing, emission reduction, and cleaner production.

Digital twins for collaborative robots: A case study in human-robot interaction

Abstract: Human-robot collaboration (HRC) can expand the level of automation in areas that have conventionally been difficult to automate such as assembly. However, the need of adaptability and the dynamics of human presence are keeping the full potential of human-robot collaborative systems difficult to achieve. This paper explores the opportunities of using a digital twin to address the complexity of collaborative production systems through an industrial case and a demonstrator. A digital twin, as a virtual counterpart of a physical human-robot assembly system, is built as a ‘front-runner’ for validation and control throughout its design, build and operation. The forms of digital twins along system's life cycle, its building blocks and the potential advantages are presented and discussed. Recommendations for future research and practice in the use of digital twins in the field of cobotics are given.

Safety assurance mechanisms of collaborative robotic systems in manufacturing

Abstract: Collaborative robots (cobots) are robots that are designed to collaborate with humans in an open workspace. In contrast to industrial robots in an enclosed environment, cobots need additional mechanisms to assure humans’ safety in collaborations. It is especially true when a cobot is used in manufacturing environment; since the workload or moving mass is usually large enough to hurt human when a contact occurs. In this article, we are interested in understanding the existing studies on cobots, and especially, the safety requirements, and the methods and challenges of safety assurance. The state of the art of safety assurance of cobots is discussed at the aspects of key functional requirements (FRs), collaboration variants, standardizations, and safety mechanisms. The identified technological bottlenecks are (1) acquiring, processing, and fusing diversified data for risk classification, (2) effectively updating the control to avoid any interference in a real-time mode, (3) developing new technologies for the improvement of HMI performances, especially, workloads and speeds, and (4) reducing the overall cost of safety assurance features. To promote cobots in manufacturing applications, the future researches are expected for (1) the systematic theory and methods to design and build cobots with the integration of ergonomic structures, sensing, real-time controls, and human-robot interfaces, (2) intuitive programming, task-driven programming, and skill-based programming which incorporate the risk management and the evaluations of biomechanical load and stopping distance, and (3) advanced instrumentations and algorithms for effective sensing, processing, and fusing of diversified data, and machine learning for high-level complexity and uncertainty. The needs of the safety assurance of integrated robotic systems are specially discussed with two development examples.

The connotation of digital twin, and the construction and application method of shop-floor digital twin

Abstract: Digital twin (DT) technology provides a novel, feasible, and clear implementation path for the realization of smart manufacturing and cyber-physical systems (CPS). Currently, DT is applied to all stages of the product lifecycle, including design, production, and service, although its application in the production stage is not yet extensive. Shop-floor digital twin (SDT) is a digital mapping model of the corresponding physical shop-floor. How to build and apply SDT has always been challenging when applying DT technology in the production phase. To address the existing problems, this paper first reviews the origin and evolution of DT, including its application status in the production stage. Then, an implementation framework for the construction and application of SDT is proposed. Three key implementation techniques are explained in detail: the five-dimensional modeling of SDT; DT-based 3D visual and real-time monitoring of shop-floor operating status; and prediction of shop-floor operating status based on SDT using Markov chain. A DT-based visual monitoring and prediction system (DT-VMPS) for shop-floor operating status is developed, and the feasibility and effectiveness of the proposed method are demonstrated through the use of an engineering case study. Finally, a summary of the contributions of the paper is given, and future research issues are discussed.

Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning

Abstract: Cloud manufacturing is a new manufacturing model that aims to provide on-demand manufacturing services to consumers over the Internet. Service composition is an essential issue as well as an important technique in cloud manufacturing (CMfg) that supports construction of larger-granularity, value-added services by combining a number of smaller-granularity services to satisfy consumers’ complex requirements. Meta-heuristics algorithms such as genetic algorithm, particle swarm optimization, and ant colony algorithm are frequently employed for addressing service composition issues in cloud manufacturing. These algorithms, however, require complex design flows and painstaking parameter tuning, and lack adaptability to dynamic environment. Deep reinforcement learning (DRL) provides an alternative approach for solving cloud manufacturing service composition (CMfg-SC) issues. DRL as model-free artificial intelligent methods enables a system to learn optimal service composition solutions through training, which can therefore circumvent the aforementioned problems with meta-heuristics algorithms. This paper is dedicated to exploring possible applications of DRL in CMfg-SC. A logistics-involved QoS-aware DRL-based CMfg-SC is proposed. A dueling Deep Q-Network (DQN) with prioritized replay named PD-DQN is designed as the DRL algorithm. Effectiveness, robustness, adaptability, and scalability of PD-DQN are investigated, and compared with that of the basic DQN and Q-learning. Experimental results indicate that PD-DQN is able to effectively address the CMfg-SC problem.

Collision-free human-robot collaboration based on context awareness

Abstract: Recent advancements in human-robot collaboration have enabled human operators and robots to work together in a shared manufacturing environment. However, current distance-based collision-free human-robot collaboration system can only ensure human safety but not assembly efficiency. In this paper, the authors present a context awareness-based collision-free human-robot collaboration system that can provide human safety and assembly efficiency at the same time. The system can plan robotic paths that avoid colliding with human operators while still reach target positions in time. Human operators’ poses can also be recognised with low computational expenses to further improve assembly efficiency. To support the context-aware collision-free system, a complete collision sensing module with sensor calibration algorithms is proposed and implemented. An efficient transfer learning-based human pose recognition algorithm is also adapted and tested. Two experiments are designed to test the performance of the proposed human pose recognition algorithm and the overall system. The results indicate an efficiency improvement of the overall system.

Cloud manufacturing ecosystem analysis and design

Abstract: The manufacturing industry is facing the impact of a dynamic market and intensive competition. Many companies are looking for a new approach to improve their business activities in a collaborative business ecosystem with other stakeholders. Cloud computing enables the sharing of manufacturing resources and capabilities between different stakeholders to support business and physical production. The purpose of this research is to explore approaches moving towards a cloud manufacturing ecosystem and present possible implications for practice. To fulfil the research objectives, a multiple-case study was conducted within sheet metal manufacturing companies. Business and technology related requirements for cloud-based collaborative manufacturing portals were collected through interviews with industrial practitioners from the sheet metal manufacturing perspective. Based on analysis a prototype model of a CloudEcosystem was presented to demonstrate the essential features of the portals. This research found that there are three different portal types for cloud manufacturing ecosystems depending on the value chain configuration. Close to real-time information provided by cloud-based platforms can create manufacturing ecosystems where machine owners, product designers and customers may collaborate and compete simultaneously. The CloudEcosystem designed in this research can provide a practical tool and technical solution to help manufacturers think about moving towards cloud manufacturing ecosystems.

EEG based arm movement intention recognition towards enhanced safety in symbiotic Human-Robot Collaboration

Abstract: Consumer markets demonstrate an observable trend towards mass customization. Assembly processes are required to adapt in order to meet the requirements of increased product complexity and constant variant updates. A concept to meet challenges within this trend, is a close collaboration between human workers and robots. Currently, in order to protect human operators, there are barriers and restrictions in place which prevent close collaboration. This is due to safety systems being mostly reactive, rather than anticipating motions or intentions. There are probabilistic models, which aim to overcome these limitations, yet predicting human behavior remains highly complex. Thus, it would be desirable to physically measure movement intentions in advance. A novel approach is presented of how upper-limb movement intentions can be measured with a mobile electroencephalogram (EEG). The human brain constantly analyses and evaluates motor movements up to 0.5 s before their execution. A safety system could therefore be enhanced to have an early warning of an upcoming movement. In order to classify the EEG-signals as fast as possible and to minimize fine-tuning efforts, a novel data processing methodology is introduced. This includes TimeSeriesKMeans labelling of movement intentions, which is then used to train a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). The results suggested high detection accuracies and potential time gains of up to 513 ms to be achieved in a semi-online system. Thus, the time advantages included in a simulation demonstrated the potential to increase a system's reaction time and therefore improve the safety and the fluency of Human-Robot Collaboration.

AR/MR Remote Collaboration on Physical Tasks: A Review

Abstract: This paper provides a review of research into using Augmented Reality (AR) and Mixed Reality(MR) for remote collaboration on physical tasks. AR/MR-based remote collaboration on physical tasks has recently become more prominent in academic research and engineering applications. It has great potential in many fields, such as real-time remote medical consultation, education, training, maintenance, remote assistance in engineering, and other remote collaborative tasks. However, to the best of our knowledge there has not been any comprehensive review of research in AR/MR remote collaboration on physical tasks. Therefore, this paper presents a comprehensive survey of research between 2000 and 2018 in this domain. We collected 215 papers, more than 80% of which were published between 2010 and 2018, and all relevant works are discussed at length. Then we elaborate on the review from typical architectures, applications (e.g., industry, telemedicine, architecture, teleducation and others), and empathic computing. Next, we made an in-depth review of the papers from seven aspects: (1) collection and classification research, (2) using 3D scene reconstruction environments and live panorama, (3) periodicals and conducting research, (4) local and remote user interfaces, (5) features of user interfaces commonly used, (6) architecture and sharing non-verbal cues, (7) applications and toolkits. We find that most papers (160 articles, 74.4%) are published in conferences, using co-located collaboration to emulate remote collaboration is adopted by more than half (126, 58.6%) of the reviewed papers, the shared non-verbal cues can be mainly classified into five types (Virtual Replicas or Physical Proxy(VRP), AR Annotations or a Cursor Pointer(ARACP), avatar, gesture, and gaze), the local/remote interface is mainly divided into four categories (Head-Mounted Displays(HMD), Spatial Augmented Reality(SAR), Windows-Icon-Menu-Pointer(WIMP) and Hand-Held Displays(HHD)). From this, we can draw ten conclusions. Following this we report on issues for future works. The paper also provides an overall academic roadmap and useful insight into the state-of-the-art of AR/MR remote collaboration on physical tasks. This work will be useful for current and future researchers who are interested in collaborative AR/MR systems.

A Human-Robot Collaboration Framework for Improving Ergonomics During Dexterous Operation of Power Tools

Abstract: In this work, we present a novel control approach to human-robot collaboration that takes into account ergonomic aspects of the human co-worker during power tool operations. The method is primarily based on estimating and reducing the overloading torques in the human joints that are induced by the manipulated external load. The human overloading joint torques are estimated and monitored using a whole-body dynamic state model. The appropriate robot motion that brings the human into the suitable ergonomic working configuration is obtained by an optimisation method that minimises the overloading joint torques. The proposed optimisation process includes several constraints, such as the human arm muscular manipulability and safety of the collaborative task, to achieve a task-relevant optimised configuration. We validated the proposed method by a user study that involved a human-robot collaboration task, where the subjects operated a polishing machine on a part that was brought to them by the collaborative robot. A statistical analysis of ten subjects as an experimental evaluation of the proposed control framework is provided to demonstrate the potential of the proposed control framework in enabling ergonomic and task-optimised human-robot collaboration.

A novel kinematic parameters calibration method for industrial robot based on Levenberg-Marquardt and Differential Evolution hybrid algorithm

Abstract: The poor absolute positioning accuracy of industrial robots is the main obstacle for its further application in precision grinding of complex surfaces, such as blisk, blade, etc Based on the established kinematic error model of a typical industrial robot FANUC M710ic/50, a novel kinematic parameters calibration method is proposed in this paper to improve the absolute positioning accuracy of robot The pre-identification of the kinematic parameter deviations of robot was achieved by using the Levenberg-Marquardt algorithm Subsequently, these identified suboptimal values of parameter deviations were defined as central values of the components of initial individuals to complete accurate identification by using Differential Evolution algorithm The above two steps, which were regarded as the core of this Levenberg-Marquardt and Differential Evolution hybrid algorithm, were used to obtain the preferable values for kinematic parameters of the robot On this basis, the experimental investigations of kinematic parameters calibration were conducted by using a laser tracker and numerical simulation method The results revealed that the robot positioning error decreased from 0994 mm, initial positioning error measured by laser tracker, to 0262 mm after calibration with this proposed hybrid algorithm The absolute positioning accuracy has increased by 4086% than that of the Levenberg-Marquardt algorithm, increased by 4031% than that of the Differential Evolution algorithm, and increased by 2514% than that of the Simulated Annealing algorithm This work shows that the proposed kinematic parameters calibration method has a significant improvement on the absolute positioning accuracy of industrial robot

Multi-agent reinforcement learning for online scheduling in smart factories

Abstract: Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.

Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm

Abstract: With the intensification of globalization, the competition among various manufacturing enterprises has become increasingly fierce, enterprises are developing in the direction of the product diversification, zero inventory or low inventory, and scheduling in production management has become more complicated. In this paper, machine and workpiece were as objects to study the problem of workshop scheduling in intelligent manufacturing environment. The resource scheduling model of intelligent manufacturing workshop was established with the goal of minimizing the maximum completion time, tardiness, machine load and energy consumption. The Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm was improved, and the evaluation function was established based on ranking level and crowding degree, then the competition mechanism was introduced. Random mutation strategy and crossover method based on process and machine was adopted to generate a new generation of populations. The elitist retention strategy was improved, the variable proportion method was designed to determine the probability, and the optimal solution is determined by the Analytic Hierarchy Process (AHP). The benchmark cases and practical production and processing problems were tested to verify the superiority and effectiveness of the improved algorithm.

Design, Modelling and Validation of a Novel Extra Slender Continuum Robot for In-situ Inspection and Repair in Aeroengine

Abstract: In-situ aeroengine maintenance works are highly beneficial as it can significantly reduce the current maintenance cycle which is extensive and costly due to the disassembly requirement of engines from aircraft. However, navigating in/out via inspection ports and performing multi-axis movements with end-effectors in constrained environments (e.g. combustion chamber) is fairly challenging. A novel extra-slender (diameter-to-length ratio

A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

Abstract: Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.

Transfer learning enabled convolutional neural networks for estimating health state of cutting tools

Abstract: Effective Prognostics and Health Management (PHM) for cutting tools during Computerized Numerical Control (CNC) processes can significantly reduce downtime and decrease losses throughout manufacturing processes In recent years, deep learning algorithms have demonstrated great potentials for PHM However, the algorithms are still hindered by the challenge of the limited amount data available in practical manufacturing situations for effective algorithm training To address this issue, in this research, a transfer learning enabled Convolutional Neural Networks (CNNs) approach is developed to predict the health state of cutting tools With the integration of a transfer learning strategy, CNNs can effectively perform tool health state prediction based on a modest number of the relevant images of cutting tools Quantitative benchmarks and analyses on the performance of the developed approach based on six typical CNNs models using several optimization techniques were conducted The results indicated the suitability of the developed approach, particularly using ResNet-18, for estimating the wear width of cutting tools Therefore, by exploiting the integrated design of CNNs and transfer learning, viable PHM strategies for cutting tools can be established to support practical CNC machining applications

Quality-enabled decentralized IoT architecture with efficient resources utilization

Abstract: The Internet of Things (IoT) is a paradigm aimed at connecting everyday objects to the internet. IoT applications include smart cities, healthcare, agriculture, as well as the industry and manufacturing. The ability to monitor and control the physical world using information technology creates many opportunities. However, it also comes with some costs. The exponential growth of connected devices, the heterogeneity of IoT use cases, and the diversity of the network technologies yield a concern regarding IoT sustainability. With this work, we aim to contribute to this concern. In doing so, we introduce a novel representation model that is destined for (i) monitoring the IoT environment at runtime, (ii) expressing the overall quality of the system, and (iii) helping to utilize the available resources efficiently. We also define a feature set that describes the best the expectations of decentralized IoT platforms. Furthermore, we describe a quality-enabled decentralized IoT architecture too that incorporates the specified feature set as well as our representation model. Such solutions are necessary to improve and maintain IoT of the future and all its application domains, including the Industrial Internet of Things (IIoT). With the presented research, we aim to encourage the efficient utilization of resources and simplify the production of next-generation IoT solutions.

Iterative-learning error compensation for autonomous parking of mobile manipulator in harsh industrial environment

Abstract: In wide-area and multi-sites manufacturing scenarios, the mobile manipulator suffers from inadequate autonomous parking performance due to the harsh industrial environment. Instead of struggling to model various errors or calibrate multiple sensors, this paper resolves the above challenge by proposing an iterative-learning error compensation scheme that consists of offline pre-regulation and online compensation, which can improve the compensation efficiency and accommodate the error fluctuations caused by environmental fluctuations. Integrating an improved Monte-Carlo localization and eye-in-hand vision technique, an effective measurement system is firstly developed to accurately obtain the parking data without requiring superfluous facilities or cumbersome measurement. Then, after removing the data outliers utilizing the Grubbs test, offline pre-regulation is achieved to give a suitable initial value and increase the compensation convergence. To reduce the time-varying systematic errors and parking error fluctuations, online compensation is presented by offering an efficacious estimation of environmental fluctuations using fuzzy logic rules and providing an adaptive iterative-learning law. Finally, the feasibility and effectiveness of the presented compensation method are validated by extensive experiments implemented on a self-developed mobile manipulator.

A Mobile Robotic Measurement System for Large-scale Complex Components Based on Optical Scanning and Visual Tracking

Abstract: Large-scale components with complex curved surfaces are the foundation of aerospace, energy, and transportation fields, while full-field 3D measurements along with accuracy analyses are critical to control manufacturing quality. Most of the existing measurement methodologies rely on manual inspection, and the accuracy and efficiency are unsatisfactory. This paper introduces an integrated mobile robotic measurement system for the accurate and automatic 3D measurement of large-scale components with complex curved surfaces. The measurement system is composed of a mobile manipulator, a fringe projection scanner and a stereo vision system, and it can provide accurate noncontact 3D measurements of large-scale complex components. By proposing a hand-eye calibration method and scanning pose tracking method based on a stereo vision system, the local point clouds obtained by scanning along with the movement of the mobile robot around the component can be accurately unified into a common reference frame. The proposed measuring system and method are verified by measuring and reconstructing the whole surface of a wind turbine blade model with a length of 2.8 m. The accuracy evaluation proves the effectiveness of the proposed system and method.

Hybrid active/passive force control strategy for grinding marks suppression and profile accuracy enhancement in robotic belt grinding of turbine blade

Abstract: Grinding marks and traces, as well as the over- and under-cutting phenomenon are the severe challenges in robotic abrasive belt grinding of turbine blades and it greatly limits the further application of robotic machining technology in the thin-walled blade fields. In the paper, an active force control method consisting of force/positon and PI/PD controller based on six-dimensional force/torque sensor is introduced to eliminate the grinding marks and traces, and a passive force control method including PID controller based on one-dimensional force sensor is proposed to reduce the over- and under-cutting phenomenon in robotic machining system. Then the Kalman filter information fusion methodology is adopted to combine the active and passive force control methods which could improve the controlled force accuracy and efficiency, as well as avoid the control interference. Finally both the test workpiece and turbine blade are employed to examine and verify the reliability and practicality of the proposed hybrid force control method by achieving the desired surface quality and higher profile precision.

How to include User eXperience in the design of Human-Robot Interaction

Abstract: In recent years Human-Robot Collaboration (HRC) has become a strategic research field, considering the emergent need for common collaborative execution of manufacturing tasks, shared between humans and robots within the modern factories. However, the majority of the research focuses on the technological aspects and enabling technologies, mainly directing to the robotic side, and usually neglecting the human factors. This work deals with including the needs of the humans interacting with robots in the design in human-robot interaction (HRI). In particular, the paper proposes a user experience (UX)-oriented structured method to investigate the human-robot dialogue to map the interaction with robots during the execution of shared tasks, and to finally elicit the requirements for the design of valuable HRI. The research adopted the proposed method to an industrial case focused on assembly operations supported by collaborative robots and AGVs (Automated Guided Vehicles). A multidisciplinary team was created to map the HRI for the specific case with the final aim to define the requirements for the design of the system interfaces. The novelty of the proposed approach is the inclusion of typically interaction design tools focusing in the analysis of the UX into the design of the system components, without merely focusing on the technological issues. Experimental results highlighted the validity of the proposed method to identify the interaction needs and to drive the interface design.

A peg-in-hole robot assembly system based on Gauss mixture model

Abstract: Using robots to assemble parts has always been a research hotspot, but the traditional analysis model of assembly mainly focuses on establishing the linear equation between the feedback force and the relative position of the peg and hole, which leads to high requirements for the material properties and geometric parameters of the assembly parts. In this paper, a new assembly strategy is proposed that learns skills from manual teaching to carry out the assembly process. A Gauss mixture model and regression is used to fit the teaching data, and then, the compliance control method is applied to conduct assembly when the geometric profile parameters and material elastic parameters of the assembly are inaccurate. Finally, the experiment was implemented under the tolerance is 0.18 mm, and the success rate reaches 100%. These findings verified the effectiveness of the compliance control method.

Multi-scale evolution mechanism and knowledge construction of a digital twin mimic model

Abstract: Metal products are susceptible to factors such as cutting force, clamping force and heat in the machining process, resulting in product quality problems, such as geometric deformation and surface defects. The real-time observation and control of product quality are integral to optimizing machining process. Digital twin technologies can be used to monitor and control the quality of products via multi-scale based quality analysis. However, previous research on digital twin lacks a fine-grained expression and generation method for product multi-scale quality, making it impossible to carry out an in-depth analysis of product quality. Aiming at addressing this challenge, we study the multi-scale evolution mechanism of the digital twin model and explore the knowledge generation method of the digital twin data. The proposed method constructed the digital twin quality knowledge model from the macro, meso, and micro levels by utilizing the data of the digital twin mimic model. These multi-scale quality knowledge models can express product quality in a fine-grained way and provide data support for digital twin-based decision-making. Finally, we tested the method in monitoring and controlling the machining quality of an air rudder to verify the feasibility of the proposed method.

Industrial Blockchain : A state-of-the-art Survey

Abstract: As an underlying and backbone technology of Bitcoin, Blockchain attracted extensive attention worldwide in recent years due to its unique characteristics of decentralization, openness, immutability, anonymity, etc., which enables it to build a trust basis through recording the point-to-point decentralized transactions in an immutable way via the attached timestamp, thereby improving system efficiency and reducing the cost without relying on the central agent. As it is considered to be a potentially revolutionary technology, Blockchain has been introduced into various industrial fields including finance, supply chain, manufacturing, healthcare, energy, and smart city. In this paper, we conduct a state-of-the-art survey of industrial Blockchain in terms of published articles between 2017 and 2020, and worldwide Blockchain movement including North America, Europe, and the Asia Pacific region so far. We conduct a statistic analysis of the collected articles in terms of three dimensions, which are year of publication, leading research institutes and researchers, and article classification to present a multi-dimensional trend or conclusion. Besides, we analyse articles that are cited over a certain number of times in detail to investigate the hot research directions. Finally, the challenges, opportunities, and future perspectives are discussed to summarize the main obstacles of industrial Blockchain and identify the open research questions in the near future.

Research on flexible job shop scheduling under finite transportation conditions for digital twin workshop

Abstract: Flexible job shop scheduling is one of the most effective methods for solving multiple varieties and small batch production problems in discrete manufacturing enterprises. However, limitations of actual transportation conditions in the flexible job shop scheduling problem (FJSP) are neglected, which limits its application in actual production. In this paper, the constraint influence imposed by finite transportation conditions in the FJSP is addressed. The coupling relationship between transportation and processing stages is analyzed, and a finite transportation conditions model is established. Then, a three-layer encoding with redundancy and decoding with correction is designed to improve the genetic algorithm and solve the FJSP model. Furthermore, an entity-JavaScript Object Notation (JSON) method is proposed for transmission between scheduling services and Digital Twin (DT) virtual equipment to apply the scheduling results to the DT system. The results confirm that the proposed finite transportation conditions have a significant impact on scheduling under different scales of scheduling problems and transportation times.

A robot hand-eye calibration method of line laser sensor based on 3D reconstruction

Abstract: In the robotic eye-in-hand measurement system, a hand-eye calibration method is essential From the perspective of 3D reconstruction, this paper first analyzes the influence of the line laser sensor hand-eye calibration error on the 3D reconstructed point clouds error Based on this, considering the influence of line laser sensor measurement errors and the need for high efficiency and convenience in robotic manufacturing systems, this paper proposes a 3D reconstruction-based robot line laser hand-eye calibration method In this method, combined with the point cloud registration technique, the newly defined error-index more intuitively reflects the calibration result than traditional methods To raise the performance of the calibration algorithm, a Particle Swarm Optimization - Gaussian Process (PSO-GP) method is adopted to improve the efficiency of the calibration The experiments show that the Root Mean Square Error (RMSE) of the reconstructed point cloud can reach 01256 mm when using the proposed method, and the reprojection error is superior to those using traditional hand-eye calibration methods

Generic development methodology for flexible robotic pick-and-place workcells based on Digital Twin

Abstract: Together with the trends of mass personalization, flexible robotic applications become more and more popular. Although conventional robotic automation of workpiece manipulation seems to be solved, advanced tasks still need great amount of effort to be reached. In most cases, on-site robot programming methods, which are intuitive and easy to use, are not applicable in flexible scenarios. On the other hand, the application of offline programming methods requires careful modeling and planning. Consequently, this paper proposes a generalized development methodology for flexible robotic pick-and-place workcells, in order to provide guidance and thus facilitate the development process. The methodology is based on the Digital Twin (DT) concept, which allows the iterative refinement of the workcell both in the digital and in the physical space. The goal is to speed up the overall commissioning (or reconfiguration) process and reduce the amount of work in the physical workcell. This can be achieved by digitizing and automating the development, and maintaining sufficient twin closeness. With that, the operation of the digital model can be accurately realized in the physical workcell. The methodology is presented through a semi-structured pick-and-place task, realized in an experimental robotic workcell together with a reconfiguration scenario.

Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots

Abstract: Predictive maintenance has been proposed to maximize the overall plant availability of modern manufacturing systems. To this end, research has been conducted mainly on data-driven prognostic techniques for machinery equipment individual components. However, the lack of historical data together with the intricate design of industrial machines, e.g. robots, stimulate the use of advanced methods exploiting simulation capabilities. This paper aims to address this challenge by introducing a generic framework for the enhancement of advanced physics-based models with degradation curves. The creation of a robot's simulation model and its enrichment with data from the degradation curves of the robot's components is presented. Following, the extraction of information from degradation curves during the simulation of the robot's dynamic behaviour is addressed. The Digital Twin concept is employed to monitor the health status of the robot and ensure the convergence of the simulated to the actual robot behaviour. The output of the simulation can enable to estimate the future behaviour of the robot and make predictions for the quality of the products to be produced, as well as to estimate the robot's Remaining Useful Life. The proposed approach is applied in a case study coming from the white goods industry, where it is investigated whether the robot will experience some failure within the next 18 months.

Collision-free and smooth joint motion planning for six-axis industrial robots by redundancy optimization

Abstract: Planning collision-free and smooth joint motion is crucial in robotic applications, such as welding, milling, and laser cutting. Kinematic redundancy exists when a six-axis industrial robot performs five-dimensional tasks, and there are infinite joint configurations for a six-axis industrial robot to realize a cutter location data of the tool path. The robot joint motion can be optimized by taking advantage of the kinematic redundancy, and the collision-free joint motion with minimum joint movement is determined as the optimal. However, most existing redundancy optimization methods do not fully exploit the redundancy of the six-axis industrial robots when they conduct five-dimensional tasks. In this paper, we present an optimization method to solve the problem of inverse kinematics for a six-axis industrial robot to synthesize the joint motion that follows a given tool path, while achieving smoothness and collision-free manipulation. B-spline is applied for the joint configuration interpolation, and the sum of the squares of the first, second, and third derivatives of the B-spline curves are adopted as the smoothness indicators. Besides, the oriented bounding boxes are adopted to simplify the shape of the robot joints, robot links, spindle unit, and fixtures to facilitate collision detections. Dijkstra's shortest path technique and Differential Evolution algorithm are combined to find the optimal joint motion efficiently and avoid getting into a local optimal solution. The proposed algorithm is validated by simulations on two six-axis industrial robots conducting five-axis flank milling tasks respectively.