Search

Manufacturers now face the challenges of increased product variety, the need for small-to-medium-quantity production, shortened product life cycles, and reduced costs due to changes in customer demand. Total factory automation, including the automation of assembly as well as indirect (overhead) functions, must be realized for these challenges to be met. Ricoh's factory for the production of the Plain Paper Copier (PPC) has developed a new production system employing hardware, software and “humanware”. This system has an automated production support system (RINKS), assembly system, and physical distribution system. It is network composed of computer and terminals. This article discusses the development and implementation of Ricoh's system.

Customization of manipulator configurations using modularity and reconfigurability aspects is receiving much attention. Modules presented so far in literature deals with the conventional and standard configurations. This paper presents the 3D printable, light-weight and unconventional modules: MOIRs' Mark-2, to develop any custom `n'-Degrees-of-Freedom (DoF) serial manipulator even with the non-parallel and non-perpendicular jointed configuration. These unconventional designs of modular configurations seek an easy adaptable solution for both modular assembly and software interfaces for automatic modeling and control. A strategy of assembling the modules, automatic and unified modeling of the modular and reconfigurable manipulators with unconventional parameters is proposed in this paper using the proposed 4 modular units. A reconfigurable software architecture is presented for the automatic generation of kinematic and dynamic models and configuration files, through which, a designer can design, validate using visualization, plan and execute the motion of the developed configuration as required. The framework developed is based upon an open source platform called as Robot Operating System (ROS), which acts as a digital twin for the modular configurations. For the experimental demonstration, a 3D printed modular library is developed and an unconventional configuration is assembled, using the proposed modules followed by automatic modeling and control, for a single cell of the vertical farm setup.

With the rapid development of cloud manufacturing (CMfg) technology in Industrial Internet based on the concept of manufacturing as a service, how to effectively and quickly implement manufacturer scheduling in a cloud manufacturing environment has aroused widespread attention of researchers. Manufacturers in the cloud manufacturing environment are virtualized and digitized, and then aggregated in the form of cloud data. As a direct provider of cloud manufacturing services, manufacturers shift the service mode from the traditional decentral type to a centralized type. However, in practice, manufacturers can't respond promptly to the needs of customer. The scheduling of manufacturers will directly affect the quality of cloud manufacturing services. This paper research the problem of manufacturer scheduling in the cloud manufacturing environment and analyzes the factors that affect the manufacturer resource scheduling in five categories including task load degree, task reliability, manufacturing efficiency, manufacturing resource richness and Internet of Things (IoT) match degree. At the same time, we establishes the scheduling index and the objective function model. The Chaos Optimization Algorithm (COA) is used to solve the objective function and achieve scheduling of manufacturers under different scheduling tasks. The simulation results show that the proposed scheduling index is comprehensive and effective. Based on the math model of manufacturer scheduling, all the factors that influence the manufacturer scheduling in the cloud manufacturing environment can be merged together, and the complicated scheduling problem also can be transformed into the mathematical problem to solve. In this paper, the manufacturer scheduling algorithm based on Chaos Theory can finish the scheduling of manufacturers under various tasks and make the cloud manufacturing platform provide manufacturing services to users in time.

The production planning and control of aircraft remanufacturing system is becoming a critical issue with the development and application of reusable aircraft parts. With the unpredictable demands of remanufactured products, the aircraft parts remanufacturing system (APRS) operates with various disruptions, which could seriously influence the system's efficiency. To generate timely and efficient response towards the frequent disruptions in APRS, an adaptive rescheduling policy is presented in this paper with a trigger and a re-scheduler. The trigger evaluates the system performance loss caused by the disruptions with a specific Relative Performance Deviation Index (RPDI) and determines when the re-scheduler is activated to re-optimize the schedule. The re-scheduler then intelligently selects the optimal rescheduling method by an improved random forest (IRF) based on the system status described by a set of reduced but important features. In order to deal with the data imbalance in different classes in the remanufacturing system, this paper develops a problem-oriented conditional generative adversarial networks (CGAN) for data augmentation. Aiming to demonstrate the effectiveness of the proposed method, an experiment is conducted with three commonly used classification approaches. And, the results show the competitiveness of the proposed approach in reducing the rescheduling trigger times and the rescheduling performance improvement.

The sphere-pipe intersecting curve is a kind of spatial complex curve, which is controlled by robot in the sphere-pipe welding. In this paper, a discrete method for robot welding by offline programming is put forward. To move the robot with the specified curve trajectory, it need to be discretized to generate the robot motion instructions. As the core innovation of this paper, a discrete method of sphere-pipe intersecting curve is given according to the chord error limit and principle of equal arc length, which guarantees the accuracy of robot welding. To achieve this goal, this paper has done the following related works. First, the geometrical model of the sphere-pipe intersection is established, which can cover most of the intersection modes. Second, this paper gives the parameter equations in two different coordinate systems because the expression is very complex. This operation will simplify the subsequent calculations. Although the curve expression is simplified, it is very complicated to find the maximum based on the definition of curvature. After experimental analysis, this paper presents a simple and effective algorithm for the calculation of maximum curvature. Finally, this paper takes the Puma560 robot for simulation, and the simulation results are used to verify the feasibility and effectiveness of our method.

Accurate, time-effective calibration and decoupling procedures of multi-axis Force/Moment (F/M) sensors are critical to the implementation of these sensors. Recently, many decoupling methods have been proposed by researchers, but the inherent coupling relationship among components of multi-axis F/M sensors has not been taken into account. In this paper, we thus proposed a novel Sparse Voltage Maximum Inter-class Variance (SVMIV) algorithm, which took advantage of the inherent relationship nature. Furthermore, a novel nonlinear decoupling method based on the Parallel Voltage-Extreme Learning Machine (PV-ELM) was also presented. To demonstrate the utility of the proposed approach, extensive comparisons were made between the proposed PV-ELM decoupling method and several conventional decoupling approaches such as Least Square (LS), Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), and Extreme Learning Machine (ELM). Results of real decoupling experiments demonstrated that the proposed the PV-ELM decoupling algorithm outperforms linear decoupling algorithms for six-axis F/M sensors. In addition, it was also proved that the PV-ELM decoupling method can decouple the outputs of six-axis F/M sensors with higher precision, faster speed, improved robustness, and faster convergence than the state-of-the-art nonlinear decoupling algorithms such as SVR, BP and ELM. Overall, this paper proposed a novel way to deal with the inherent coupling relationship of six-axis F/M sensors, and the experimental results demonstrated that the maximum I-type and II-type errors were below 0.356% and 0.270%, respectively, of full scale in all measured variables.

Transforming the manufacturing environment from manually operated production units to unsupervised robotic machining centres requires a presence of reliable in-process monitoring system. In this paper, we demonstrate a technique for automatic endpoint detection of weld seam removal in a robotic abrasive belt grinding process with the help of a vision system using deep learning. The paper presents the results of the first investigative stage of semantic segmentation of weld seam removal states using encoder-decoder convolutional neural networks (EDCNN). An experimental investigation using four different weld seam states on mild steel work coupon are trained using the VGG-16 network based on encoder-decoder architecture. The results demonstrate the potential of the developed vision based methodology as a tool for endpoint prediction of the weld seam removal in real time during a compliant abrasive belt grinding process. The prediction system based on semantic segmentation is able to monitor weld profile geometry evolution taking into account the varying belt grinding parameters during machining which will allow further process optimisation. National Research Foundation (NRF) This work was conducted within the Rolls-Royce@NTU Corporate Lab with support from the National Research Foundation (NRF) (Grant No. M-RT1.1 M4061298) Singapore under the Corp Lab@University Scheme.

This paper proposes a universal control strategy and novel haptic interface for the International Thermonuclear Experimental Reactor (ITER) welding/machining robot. The developed method implements a reliable position control and sense between the human and the robot as well as between the robot and the task environment in which provides highly accurate position control based on joystick inputs with real-time haptic capabilities. The proposed control structure has the characteristics of a universal technique independent of the actual control algorithm and it can be applied with other suitable control methods as a real-time control strategy. One of the contributions of this paper is that the proposed control method combines a directed random search method and real-time simulation to develop an intelligent controller in which each generation of parameters is tested on-line by the real-time simulator before being applied to the real process. The controller was evaluated on a hydraulic position servo system where the simulator of the hydraulic system was built based on Markov chain Monte Carlo (MCMC) method. A Particle Swarm Optimization (PSO) algorithm combined with the foraging behavior of E. coli bacteria was utilized as the directed random search engine. PSO is influenced by the simulation of social behavior in which each individual agent of the possible solution population, benefits from its history and its interactions with other agents within the population. This sharing of knowledge helps facilitate faster convergence to an optimal solution. From the other side, the effect of Bacterial Foraging Optimization (BFO) of E. coli is that the agent is making a decision to which position to move. The agent does this with attention to the previous data stored in the memory as the best past position. These selection behaviors of particles help to avoid poor foraging and improve foraging strategies. In conclusion, the proposed control and haptic strategy allows the operator to be kinetically plugged into the work environment and simultaneously provides force feedback sense with high controllability without neglecting the system dynamics. Post-print / Final draft

Computer numerical control (CNC) systems are becoming multi-functional as the requirements for manufacturing quality, efficiency and product diversity are becoming more rigorous. The reconfigurability and scalability property of CNC are important for reducing manufacturing costs and functional redundancy and shortening manufacturing time. However, in current CNC systems, functions are closely coupled in a real-time operation environment. This has been a main obstacle for implementing a scalable and reconfigurable CNC system. This paper proposes a new type of CNC system named invisible numerical control (INC) system to simplify CNC architecture and improve CNC architecture reconfigurability and function reusability. INC system consists of three sub-systems, namely project center, motor controller and mobile terminal. The functions of the sub-systems are machine control instructions generation, execution of machine control instructions and process control respectively. The sub-systems are organized in loosely coupled manner. This is enabled by current computer/information resources and technologies such as high speed data transmission, large capacity and high speed storage, standard communication protocols and compiled operation of machining programs. By configuring different functions in project center, it can generates uniform machine control instructions for multiple and different types of machine tools. Therefore, reconfigurability and function reusability are achieved. This paper illustrates the functions, architecture and information model of INC and builds an INC prototype. Case studies show that by using current off-the-shelf hardware, INC can be implemented to undertake different manufacturing tasks.

Use of sensors in robotic welding for controlling the weld quality leads to replacement of manual welding operation in dangerous work environment in presence of high temperature and fumes even in small or medium scale enterprises. The seam tracking operation is very essential for extracting weld seam position which can be fed to robot controller for instructing robot along the weld seam path. The seam tracking operation can be executed by different types of sensors having their own merits and demerits. In this paper, different sensors and techniques used for seam tracking task in robotic welding have been discussed in detail. Each sensor has different method or technology of weld seam feature extraction which have been described by different authors in different ways. The chief tasks for seam tracking have been found to be weld starting and end point detection, weld edge detection, joint width measurement and weld path position determination with respect to robot co-ordinate frame. Thus sensors have a very important role in robotic welding for fully automating the system with in process real time monitoring of weld process parameters with the sensor feedback. In further discussion the practical use of different sensors in industries with a comparison of their advantages and disadvantages have been discussed. This Paper presents the role of sensors in robotic welding and a detail study of methodologies of weld seam position and geometry feature extraction by different sensors typically used for weld seam tracking.

Modern manufacturing systems require increased levels of automation for fast and low-cost production, but also high levels of flexibility and adaptability to dynamic production requirements. Human-robot interaction allows for closer collaboration in assembly tasks that require increased productivity by combining the performance of robotic systems with the flexibility and dexterity of human workers. However, the main challenge is establishing human safety while performing near to robots. Towards enabling safe human-robot collaboration several physical and software systems should be combined. In this paper, a cyber-physical system for enabling and controlling safe human-robot collaborative assembly operations is discussed. The approach considers a shared fenceless working space where humans, industrial robots, or other moving objects, such as auto-guided vehicles, may operate. The working space is monitored by optical sensors. In this context, human safety is the major challenge. The paper focuses on a Cyber-Physical System for enabling human-robot collaboration based on real-time evaluation of safety distance and a closed-loop control for triggering collision preventive actions. An initial implementation is evaluated in a specific use case in a laboratory machine shop with an industrial robot, and its results are compared regarding the use of one or more sensors. The comparison is performed in terms of system response time to detect human presence within a predefined safety zone.

Vessels constitute one of the most cost effective ways of transporting goods around the world. Despite the efforts, maritime accidents still occur, with catastrophic consequences. For this reason, vessels are submitted to periodical inspections for the early detection of cracks and corrosion. These inspections are nowadays carried out at a great cost. In order to contribute to make ship inspections safer and more cost-efficiently, this paper presents a novel framework to turn a Micro-Aerial Vehicle (MAV) into a flying camera that can virtually teleport the human surveyor through the different structures of the vessel hull. The system architecture has been developed to be reconfigurable so that it can fit different sensor suites able to supply a proper state estimation, being at the same time compatible with the payload capacity of the aerial platform and the operational conditions. The control software has been designed following the Supervised Autonomy paradigm, so that it is in charge of safety related issues such as collision avoidance, While the surveyor, within the main control loop, is supposed to supply motion commands while he/she is concentrated on the inspection at hand. In this paper, we report on an extensive evaluation of the platform capabilities and usability, both under laboratory conditions and on board a real vessel, during a field inspection campaign., This is a preprint version of publication with DOI: https://doi.org/10.1016/j.rcim.2018.09.009. This work is partially supported by FEDER funding, by the European Social Fund through Ph.D. scholarship FPI10-43175042V (Conselleria d'Educacio, Cultura i Universitats, Govern de les Illes Balears), by project nr. AAEE50/2015 (Direccio General d'Innovacio i Recerca, Govern de les Illes Balears).

In welding, the actual weld seam is the target path of welding robot. When the deviation between the actual pipelines and the ideal models cannot be ignored, special research should be carried out. This paper introduces a novel method to quantify and compensate the path deviation of actual intersecting curve, which is used to determine the welding path of robot. Weld location is a technical means, which can be used to find the position of some key points on intersecting curve. Combining the information of weld location with the inherent characteristics of intersecting curve, this paper has analyzed the results of actual welding experiments, all these works laid the foundation for the proposed algorithm. First of all, on the basis of previous studies, this paper introduces a kind of geometric model for ideal intersecting pipelines, this model can cover most of the ways of intersection. Secondly, based on this model, the factors leading to the deviation between the theoretical intersecting curve and the actual are analyzed. Generally speaking, there may be some connections or coupling between the deviation sources. In view of the above problem, this paper constructs model for each source of deviation and discusses the relationship between them, then quantifies the main sources of deviation by using the weld location information, especially the quantification of the main pipe's ovality. Subsequently, the deviation after the quantification is compensated to the parametric equations of the theoretical intersecting curve. As the goal of this paper, the actual intersecting curve's equations based on non-ideal model are obtained, which is regarded as the path of welding robot. The correctness and flexibility of the path planning approach are verified by the welding robot at last.

With the extensive application of automated guided vehicles in manufacturing system, production scheduling considering limited transportation resources becomes a difficult problem. At the same time, the real manufacturing system is prone to various disturbance events, which increase the complexity and uncertainty of shop floor. To this end, this paper addresses the dynamic flexible job shop scheduling problem with insufficient transportation resources (DFJSP-ITR) to minimize the makespan and total energy consumption. As a sequential decision-making problem, DFJSP-ITR can be modeled as a Markov decision process where the agent should determine the scheduling object and allocation of resources at each decision point. So this paper adopts deep reinforcement learning to solve DFJSP-ITR. In this paper, the multiobjective optimization model of DFJSP-ITR is established. Then, in order to make agent learn to choose the appropriate rule based on the production state at each decision point, a hybrid deep Q network (HDQN) is developed for this problem, which combines deep Q network with three extensions. Moreover, the shop floor state model is established at first, and then the decision point, generic state features, genetic-programming-based action space and reward function are designed. Based on these contents, the training method using HDQN and the strategy for facing new job insertions and machine breakdowns are proposed. Finally, comprehensive experiments are conducted, and the results show that HDQN has superiority and generality compared with current optimization-based approaches, and can effectively deal with disturbance events and unseen situations through learning.

Robotic machining is an increasing application due to various advantages of robots such as flexibility, maneuverability and competitive cost. For robotic machining, the machining accuracy is the major concern of current researches. And particular attention is paid to the proper modeling of manipulator stiffness properties, the cutting force estimation and the robot posture optimization. However, through our research, the results demonstrate the spindle configuration largely affects the deformation of the robot end-effector (EE). And it may even account for approximately half of the total deformation for machining applications with the force acting perpendicular to the tool. Furthermore, the closer distance between the tool tip and the EE does not mean that the deformation tends to be smaller. Thus, it is reasonable to consider optimizing the spindle configuration based on the optimal robot posture, thereby exhausting advantages of the robot and further reducing machining errors. In this paper, a spindle configuration analysis and optimization method is presented, aiming at confirming the great influence of the spindle configuration on the deformation of the robot EE and minimizing it. First, a deformation model based on the spindle configuration (SC-based deformation model) is presented, which establishes a mapping between the spindle configuration and the deformation of the robot EE. And it confirms the large effect of the spindle configuration on the deformation of the EE. Then, a complementary stiffness evaluation index (CSEI) is proposed. And it adopts matrix norms to evaluate the influence of the spindle configuration on the complementary stiffness matrix in the SC-based deformation model. Using this index, the proposed SC-based deformation model is simplified for the ODG-JLRB20 robot adopted in this paper. Finally, a spindle configuration optimization model is derived to minimize the simplified SC-based deformation model using an iterative procedure. With this model, the optimal spindle configuration with respect to the EE can be obtained for a specific machining trajectory. Experimental results conducted on the ODG-JLRB20 robot demonstrate the correctness and effectiveness of the present method.

Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.

This paper deals with the joint stiffness modeling and identification of the heavy duty industrial robot with the counterbalancing system (CBS), which is important for the workspace optimization and deflection compensation in the robotic manufacturing. Shortcomings of the traditional method for the joint stiffness modeling and corresponding identification are analyzed in this paper. Motivated by the advantage and limitations of the traditional approach, a new identification methodology based on the servo motor current and corresponding position deflection is proposed to obtain the accurate joint stiffness. To validate the effectiveness of the proposed method for the joint stiffness modeling and identification, an identification and validation experiment is carried out using the HH-150 heavy duty industrial robot. The results show that the proposed methodology is able to identify the joint stiffness of the heavy duty industrial robot with the CBS, and the proposed new methodology outperforms in terms of accuracy, convenience and efficiency for the joint stiffness identification.

Accelerated advancements in artificial intelligence and robotics have recently boosted the proliferation and adoption of robotic systems within various industries. In order to improve the capabilities of such systems that range from enabling remote processing to enhanced access to data, there has been integration of the cloud, and this gave rise to cloud robotics. Nevertheless, minimal published literature is available on a generic cloud robotic architecture that computer scientists or software developers can utilise and adapt to a particular use case scenario, as most existing cloud robotic architectures are scenario specific. To address this gap, a systematic review is conducted in this paper to illustrate the key components of cloud robotic systems through an architectural representation, in addition to enlighten how such systems are built and evaluated. For this, 44 recently published papers were systematically reviewed and analysed. Following the review, a generic architecture for cloud robotic system was realised, and it was found that there is no agreed consensus on the tools and technologies used in the build-up of such systems. Similarly, various approaches are used to evaluate such systems and the most common one is through case studies. However, there is a lack of frameworks that guide the evaluation process. This paper presents fascinating research concerns and potential for future study based on the data reported.

In the area of intelligent manufacturing, recognising the interacting features on a CAD model is a critical yet challenging task as topology structures of features are damaged due to the feature interaction. Some of the learning-based feature recognition methods produce less favourable results when recognising highly interacting features, while some require a significant amount of 3D models for training, which present an increasing challenge in a real world scenario, especially whenever collecting large training data becomes too difficult and time-consuming. To this end, effective highly interacting feature recognition via small sample learning becomes bottleneck for learning-based methods. To tackle the above issue, the paper proposes a novel method named RDetNet based on single-shot refinement object detection network (RefineDet) which is capable of recognising highly interacting features with small training samples. In addition, the paper further utilises several data augmentation (DA) strategies to increase the amount of relevant 3D training models. Experiments carried out in this paper show that the proposed method yields favourable results in recognising highly interacting features by using small training samples (e.g. 32 models per class).

The automotive industry is facing a rapid technological evolution and the request of a very high level of customization of the products. This requires production systems able to manage a high variety of products with low volumes. To this aim, this paper focuses on multi-product assembly lines consisting of a set of stations with a robot operating the transportation and handling of the parts. Due to the high variety of the parts to be processed, perfect balancing is not possible, hence, proper control policies are requested to operate the line. The paper proposes a predictive–reactive scheduling approach to minimize the batch completion time by sequencing the tasks operated by shared resources in a context with uncertain processing times. The viability of the approach is demonstrated through the application to an industrial problem in the automotive industry.

This paper presents an innovative semi-offline trajectory synchronized algorithm for a cooperative AFP (Automated Fiber Placement) system which consists of a 6 degree-of-freedom (DOF) robot, a 6-RSS (Revolute-spherical-spherical) parallel robot, a spindle mounted on the platform to hold the mandrel. The cooperative AFP system is capable of manufacturing more complex composite components which pose high demand for trajectory planning than those by the current APF systems. However, for the cooperative AFP system, there might not be a feasible path for laying up the fiber due to the requirement of free from collisions and singularities in some cases. The semi-offline trajectory synchronized algorithm proposed in this paper can free the planned trajectory from strictly subjecting to the constraints and singularities by adding correction to pre-planned path online. Thus, the fiber can be laid up continuously without interruption. The correction is calculated based on the poses of the multi-robots detected by the photogrammetry sensor. When and how the offsets should be added to the parallel robot under the different circumstances are discussed in this paper. Experimental results demonstrate the successful avoidance of singularities and joint limits during manufacturing process.

This paper presents a new command generation technique titled VEPRO for computer controlled production machinery. In this method, the tool trajectory is described by a high-level scripting language that enables parametric representations of complex work-piece geometries. The interpreted script is then employed to generate the interpolation data required to compute a tool trajectory that is subjected to a number of kinematic constraints. A real-time interpolator is employed to provide the position commands required by each motion controller in a synchronous fashion. As a proof of concept, the proposed method is emulated on a PC using Python scripting language. The performance of the paradigm is rigorously assessed via two test cases involving different manufacturing techniques (e.g. pocket milling and 3D printing). Through the experimental results, the paper illustrates that the technique, which lends itself for real-time hardware implementation, exhibits satisfactory performance for all intensive purposes and that the method is technically feasible for a wide spectrum of manufacturing applications and systems. (C) 2017 Elsevier Ltd. All rights reserved.

This paper describes an improved three-dimensional (3D)-printed, low-cost, multi-functional, high-maneuverability, high-concealment, turtle-inspired mobile amphibious spherical robot for environmental monitoring and data collection. The major challenge in developing such a robot lies in its limited physical size and compact structure that allows for only one type of propulsion system to be used both on land and in water. This paper focuses on the optimization of the kinematic and hydrodynamic model of the amphibious spherical robot, so as to improve the control accuracy and stability of the robot. In order to optimize some kinematic and dynamic modeling parameters of the robot, such as the drag coefficient of robot, the angular velocity and swing angle of each joint, a solid model of the 3D-printed robot was built by SolidWorks. Our simulation results and theoretical calculations confirmed the validity of the virtual model and facilitated identification of key parameters in the design. The correctness of the modeling was demonstrated by the stability of consecutive crawling and underwater movements, providing a basis for driving and controlling methods for this amphibious robot, as well as guidance for the robot's gait trajectory. Combining the robot's crawling mechanism with related simulation results, an optimized prototype of the 3D-printed amphibious spherical robot was constructed. A series of crawling experiments on a common floor were performed with the improved robot prototype, which was also done using the previous robot. The results were evaluated by a novel optical positioning system, NDI Polaris. Moreover, several experiments were carried on land crawling and underwater swimming to verify the performance of the improved amphibious spherical robot. Comparison of experimental and simulation results demonstrated the improved robot had better amphibious motion performance, as well as more potentiality and applicability to the real structures.

This paper focuses on active wire tension control of motor winding machines.An ILSMC scheme for wire tension control is developed.A disturbance observer is used to implement sensorless wire tension control.The estimated wire speed is exploited in designing the tension control scheme.The stability and convergence proof of the proposed approach is provided. One of the most crucial factors that affects the winding quality in an automatic motor winding process is the regulation of wire tension. Most commercial automatic motor winding machines use passive devices such as a dancer arm or a hysteresis brake to adjust the wire tension. However, as the winding speed increases, these passive devices may not be able to react quickly enough to maintain constant wire tension and may cause the enameled wire to tremble. In order to cope with the aforementioned problems, this paper conducts an in-depth study on active wire tension control of automatic motor winding machines. In particular, an iterative learning sliding mode control scheme for wire tension control is developed in this paper, while a disturbance observer is employed to estimate the wire tension for the implementation of sensorless wire tension control. Moreover, due to the fact that the motion of the unwind roll is affected by the motion of the enameled wire, the estimated wire speed information is exploited in the design of the tension control scheme to lessen the lag phenomenon. Results of the winding experiments verify the effectiveness of the proposed wire tension control scheme.

Robot cell calibration is the basic commissioning task in robot applications. Traditional way is either tedious manual calibration or complex machine calibration with expensive sensors. This paper presents a novel solution to complete the robot cell calibration with touch panel, which is an easily accessible and low-cost device in daily use on tablets or smart phones. In this paper, we discuss the robot cell calibration problem and propose the touch panel based calibration system approach. In particular, the error effect of touch panel on calibration has been analyzed. The practical calibration performance has been validated via experiments.

The rapid growth of market share of Electrical Vehicles (EVs) and their increasing amount of electric and electronic components have introduced difficult challenges for future recycling of such vehicles. End of Life Vehicles (ELVs), together with Waste Electric and Electronic Equipment (WEEE), are renowned as an important source of secondary raw materials. In addition, a significant proportion of the hidden value at the End-of-Life (EoL) of the EVs is embedded in the light fractions containing complex material mixtures, i.e. the management of electronic components that has been rarely considered in the scientific literature. The purpose of this paper is to fill this gap through the use of an innovative disassembly approach to identify the profitability of recycling such electronic components. The novel approach, based on the utilisation of a robotic system, disassembles and extracts Strategically Important Materials (SIMs) from EV components, thereby improving the concentration of these materials prior to final recycling and refining processes. This paper presents the challenges in the robotic disassembly of Electrical and Electronic (E&E) components. A case study has also been included to demonstrate that an average 95% of the materials and their associated recovery value could be achieved.

A unique successful postprocessor for a hybrid parallel-serial machine tool is developed.Smart machining must be accomplished without forcing the tool to make sharp turns when HSM controls detect a turn approaching.NC codes are recalculated to form a new path for the pivot that can avoid the discretization near the singularity points in this study.The novelty of the paper is successfully to propose a new hybrid parallel-serial five-axis machining model.The manufacturers that are interested in integrating the inverse kinematics into the design process of their hybrid product. Instead of obsessively emphasizing to interpolate more points from the linearization algorithm, the NC codes are recalculated to form a new path for the pivot that can avoid the discretization near the singularity points in this study. In previous studies, orientable-spindle machines were directly used to generate smooth tool paths traversing singular positions through inverse kinematics. In many characteristics and practices, PKMs (parallel kinematic machines) and serial machines are the opposites of each other. Fully PKMs have relatively very limited working-space, especially in terms of orientation characteristics. Fully serial machines have a problem of error accumulation. This paper presents a modular method to construct a postprocessor system for a novel hybrid parallel-serial five-axis machine tool. A hybrid parallel-serial mathematical model is introduced to analyze a structural configuration. The configuration decomposition of machine tools is used to create the kernel of the postprocessor. The proposed modified DenavitHartenberg notation is used in the coordinate conversion procedure, and then an algorithm is used for developing the inverse kinematics of five-axis machines. The feasibility of solutions depends on the surface normal along the tool path satisfying certain orientation constraints. The proposed algorithm can be easily adapted to convert between cutter contact path and cutter location code and implemented on computer-aided design and computer-aided manufacturing systems. Examples with end-milling and side-milling tools are demonstrated and real cutting parts are implemented for verifying the algorithm.

With the recent introduction of ambitious industrial strategies such as Horizon 2020 and Industry 4.0, a massive focus has been placed on the development of an efficient robotic workforce. Amongst all the operations robotic systems can take care of, handling remains a preferred choice due to a combination of factors including its often repetitive nature and low skill requirement. The associated demand for grasping tools has led to an ever increasing market for manipulation end-of-arm tooling from which a handful of industry giants have emerged. Based on data publicly accessible from the catalogs of several well-known companies, this paper aims at presenting a review on the characteristics of pneumatic, parallel, two-finger, industrial grippers. Included in the specifications under scrutiny in this paper are: stroke, force, weight, as well as a performance index referred to as the C-factor. This last index is a combination of three of the aforementioned characteristics and has been proposed in the literature as a measure of the efficiency that a gripper is capable of reaching. As will be shown, by analyzing hundreds of specifications it appears that, indeed, the range of C-factors of the grippers built by one company can be often consistently different from these of competitors. Furthermore, an important bias for certain typical specifications can be observed in most of the grippers which seems at odd with the requirement of modern robotic systems. This latter remark will open up a closing discussion proposed in the last part of this paper on the future evolution of grippers based on emerging new products.

The coordination of cyclic multi-robot systems is a critical issue to avoid collisions but also to obtain the shortest cycle-time. This paper presents a novel methodology for trajectory and coordination optimisation of cyclic multi-robot systems. Both velocity tuning and time delays are used to coordinate the robots that operate in close proximity and avoid collisions. The novel element is the non-linear programming optimisation model that directly co-adjusts the multi-robot coordination during the trajectory optimisation, which allows optimising these as one problem. The methodology is demonstrated for productivity/smoothness optimisation, and for energy efficiency optimisation. An experimental validation is done for a real-world case study that considers the multi-robot material handling system of a multi-stage tandem press line. The results show that the productivity optimisation with the methodology is competitive compared to previous research and that substantial improvements can be achieved, e.g. up to 50% smoother trajectories and 14% reduction in energy consumption for the same productivity. This paper addresses the current lack of systematic methodologies for generating optimal coordinated trajectories for cyclic multi-robot systems to improve the productivity, smoothness, and energy efficiency.

For a robot-based inspecting system calibration procedure in the production environment, it is desirable that the calibration technique should be automatic, time-saving and convenient to implement. The paper presents a new self-calibration method to calibrate and compensate for the robot-based inspecting system's kinematic errors. Compared with traditional calibration methods, this calibration approach has several unique features. First, the inspecting system can be calibrated without external measurement devices since an optical sensor is mounted to the robot end-effector as its integral part, so it can make the inspecting system realizing on-line calibration. Second, it simultaneously calibrates all the kinematic parameters of the inspecting system in one step to avoid error propagation rather than calibrate the hand-eye relationship, the robot itself and the robot exterior relationship separately. Third, the paper analyzes and eliminates redundant kinematic parameters in the inspecting system's kinematic model and derives a MDH model without redundancy. These features not only realize the automatic calibration of the inspecting system but also greatly reduce the number of identified parameters and avoid error propagation, therefore, enhance the efficiency and accuracy of parameter estimation. Experiments are conducted on a 6 DOF serial robot-based inspecting system to validate the good performance of the proposed method.

This paper presents a new Planar Least-Squares Projection (PLSP) method to accurately construct the layered contours directly from the point cloud for additive manufacturing. With the rapid development of 3D measuring technology, the dense and accurate point cloud, which can represent more accurate geometry information of a physical object than before, has been readily available. Additive manufacturing based directly on such point cloud is considered to be a promising alternative for fabricating the complex parts, but it requires the research on effective processing methods of the point set surface. In this paper, the presented PLSP method addresses this technical challenge for additive manufacturing. Mathematical modeling and processing of point cloud are presented to identify projections and construct accurate layered contours by considering both the least-square projection errors and projection distance selection criteria from measured point cloud. Technique of assigning new set of weights on measure scattered point cloud is discussed to achieve accurate layered contour development. The presented PLSP method with the new weight function and the consideration of point cloud deviation distances in sliced contour development is able to avoid the occurrence of the wrong projection that many other current methods suffer from. Technique of integrating the presented PLSP with the boundary point sequence curve (PSC) method is also discussed in this paper to eliminate contour development errors by considering the similarity in shape and closeness in distance for the ideal layered contours. Since this presented method does not involve the non- linear optimization, it is mathematically robust, and it can ensure the constructed layered contours lie accurately on the underlying nominal surface, and also has the ability of recognizing and processing the multi-contours on a common slicing plane. Both computational and experimental results based on the presented method are presented for validation.

This paper reports on a novel calibration method which enables completely automatic identification of the kinematics of a walking hexapod robotic machine tool. The method uses three on-board cameras and relies on a coupled model that combines kinematics and photogrammetry. Both the mathematical modelling and the actual implementation are detailed. Besides the calibration method, the paper proposes an analytical methodology to estimate the uncertainties of the identified kinematical parameters. The methodology is validated against both experimental results and against previously reported observability indexes. This methodology enables moving from qualitative indexes, observability indexes, to quantitative estimations. The methodology is applied to guaranty a calibration configuration that allows estimating the robot parameters with an uncertainty of 0.1 mm due to non-repeatability of the measurements.

An indispensable feature of an intelligent manipulator is its capability to quickly plan a short and safe path in the presence of obstacles in its workspace. Among the path planning methods, the probabilistic roadmap (PRM) method has been widely applied in path planning for a high-dimensional manipulator to avoid obstacles. However, its efficiency remains disappointing when the free space of manipulators contains narrow passages. To solve this problem, an improved PRM method is proposed in this paper. Based on a virtual force field, a new sampling strategy of PRM is presented to generate configurations more appropriate for practical application in the free space. Correspondingly, in order to interconnect these configurations to form a roadmap, a new connection strategy is designed, which consists of three stages and can gradually improve the connectivity of the roadmap. The contributions of this paper are as follows. The new sampling strategy can increase the sampling density at the narrow passages of the free space and reduce the redundancy of the samples in the wide-open regions of the free space; the three-stage connection strategy for interconnecting samples can ensure a high-connectivity roadmap; through synthesizing the above strategies, the improved PRM method is more suitable for path planning of manipulators to avoid obstacles efficiently in a cluttered environment. Simulations and experiments are carried out to evaluate the validity of the proposed method, and the method is available for manipulator of any degrees of freedom.

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. Refereed/Peer-reviewed

Cloud Manufacturing Service Composition (CMSC), as one of the key issues of Cloud Manufacturing (CMfg), has already attracted much attention. Existing researches on CMSC mainly focus on the optimization efficiency in ideal conditions, while scarcely focus on how to efficiently reconfigure CMSC when service exceptions occur. Uncertain service exceptions often occur during CMSC's execution in real-life CMfg. Thus, it is an urgent issue to perform an adjustment for CMSC to continue to complete the processing task. Besides, some practical constraints are non-negligible in real-world CMfg. Thus, it is necessary to consider them when reconfiguring CMSC. To bridge these gaps, this paper proposes a dynamic service composition reconfiguration model when service exceptions occur under practical constraints (DSCRWECPC). This model redefines optimization objectives, including machining quality, service quality, and cost. Besides, DSCRWECPC considers service exceptions, the cloud manufacturing service occupancy time constraint, the strict time constraint of original CMSC, and dynamic service quality change as its practical constraints. To solve this model, this paper proposes a service composition reconfiguration algorithm (SCRIHHO) based on the strengthened Harris Hawks Optimizer (HHO). Finally, to certify SCRIHHO's performance, this paper conducts numerical experiments and the case application to perform comparisons between SCRIHHO and other algorithms (Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO)). Results showed SCRIHHO in this paper is superior to PSO, GWO when tackling the practical DSCRWECPC in CMfg.

Kinematic error model plays an important role in the kinematic calibration of robot manipulators. In order to guarantee the best calibration result, the error model should meet the requirements of completeness, minimality and continuity. For the conventional serial robots, these issues have been intensively studied and a consensus has been reached during the past decades. However, for parallel manipulators, the problem of minimality, namely the determination of identifiable parameters mainly relies on numerical studies. There is a lack of effective methods to analyze the parameters’ identifiability in the error modeling process. Besides, there is still no agreement on the formula of the maximum number of identifiable parameters. In our previous work, a complete, minimal and continuous error modeling approach for parallel manipulators has been proposed based on product of exponential (POE) formula and the equation that the maximal number of identifiable parameters 4 r + 2 p + 6 was proved. However, the correctness and effectiveness of this method in kinematic calibration have not been verified on any parallel manipulator or robot. Therefore, in this paper a kinematic calibration is conducted on a prototype of a 3- P RRU parallel manipulator based on the proposed error model. The method of validating the completeness and minimality are respectively given by numerical simulations. Furthermore, to illustrate the advantage of the proposed error model, the kinematic parameters of the prototype are also calibrated based on the conventional error model which is derived by means of differentiating the limbs’ inverse kinematics. By comparing the calibration results, it can be concluded that the error model satisfying the three criteria can better improve the positioning accuracy. In addition, since the calibration method is described uniformly, it can be applied to most parallel manipulators by referring to the calibration procedure this paper presented.

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 0.1256 mm when using the proposed method, and the reprojection error is superior to those using traditional hand-eye calibration methods.

The robotic grasping task persists as a modern industry problem that seeks autonomous, fast implementation, and efficient techniques. Domestic robots are also a reality demanding a delicate and accurate human–machine interaction, with precise robotic grasping and handling. From decades ago, with analytical heuristics, to recent days, with the new deep learning policies, grasping in complex scenarios is still the aim of several works’ that propose distinctive approaches. In this context, this paper aims to cover recent methodologies’ development and discuss them, showing state-of-the-art challenges and the gap to industrial applications deployment. Given the complexity of the related issue associated with the elaborated proposed methods, this paper formulates some fair and transparent definitions for results’ assessment to provide researchers with a clear and standardised idea of the comparison between the new proposals.

Grasping and manipulating objects with robotic hands depend largely on the features of the object to be used. Especially, features such as softness and deformability are crucial to take into account during the manipulation tasks. Indeed, positions of the fingers and forces to be applied by the robot hand when manipulating an object must be adapted to the caused deformation. For unknown objects, a previous recognition stage is usually needed to get the features of the object, and the manipulation strategies must be adapted depending on that recognition stage. To obtain a precise control in the manipulation task, a complex object model is usually needed and performed, for example using the Finite Element Method. However, these models require a complete discretization of the object and they are time-consuming for the performance of the manipulation tasks. For that reason, in this paper a new control strategy, based on a minimal spring model of the objects, is presented and used for the control of the robot hand. This paper also presents an adaptable tactile-servo control scheme that can be used in in-hand manipulation tasks of deformable objects. Tactile control is based on achieving and maintaining a force value at the contact points which changes according to the object softness, a feature estimated in an initial recognition stage. Research supported by Spanish Ministry of Economy, European FEDER funds, the Valencia Regional Government and University of Alicante, through projects DPI2012-32390, DPI2015-68087-R, PROMETEO/2013/085 and GRE 15-05.

This paper presents control of underactuated robot manipulators in task space utilizing novel algorithm called fuzzy modified transpose effective Jacobian (MTEJ) with integrator term which ensures tracking trajectory in task space with high-quality performance. Although non-model base MTEJ have been introduced before and evaluated for underactuated robots, this method has poor operation against non-linear factors in actuators and joints observed in practical tests like deadband, backlash or Coulomb damping. The contributions of this paper are in twofold. First, to introduce improved MTEJ algorithm with additional integrator term that is efficient to eliminate effects of mentioned factors or other source of steady state error (SSE). Second, using new fuzzy rules to manage its terms to stabilize system with better control properties, the controller is used to make the endeffector to both track a predefined trajectory or set on an exact point. Global stabilization of this control method is proved. Simulation and experimental results are offered which compared tracking performance of the improved fuzzy MTEJ with integrator to other methods for both tracking and point to point (P.T.P) control. Outcomes of these experiments reveal privileges of using fuzzy improved MTEJ in various areas like removing SSE with better control characteristics and low computational efforts.

In this paper, an X-Y nano-positioner driven by the shear piezoelectric actuators (SPAs) is proposed based on the friction-inertial theory. The nano-positioner can output a two-dimensional motion at a maximum speed of 0.187 mm/s within a 5 mm×5 mm workspace. In order to solve the contradiction between the motion velocity and the motion resolution, the coarse and the fine motion modes are first proposed on the SPAs application. The coarse motion and the fine motion are realized based on the power-function-shaped signal and the combined piecewise-curve signal respectively. The performance of the nano-positioner, e.g. the resolution, the velocity and the fluctuation, are analyzed and discussed based on different driving signals. Compared with other nano-positioners, the proposed system in this paper is superior in respect of compact size, high resolution, high velocity and two switchable motion modes. Furthermore, a prototype system of the proposed nano-positioner has been developed, which is evaluated by using a capacitive sensor based measurement system. Experiment results validate the effectiveness of the proposed methodology that can be employed and extended to a variety of SPA involved systems.

This paper describes a flexible, intelligent, high bandwidth, signal conditioning reference design and implementation, which is suitable for a wide range of force and displacement transducers in manufacturing applications. The flexibility inherent in the design has allowed more than 10 specialised transducer conditioning boards to be replaced by this single design, in a range of bespoke mechanical test equipment manufactured by the authors. The board is able to automatically reconfigure itself for a wide range of transducers and calibrate and balance the transducer. The range of transducers includes LVDT, AC/DC strain gauge and inductive bridges, and a range of standard industrial voltage current interface transducers. Further, with a minor low-cost addition to the transducer connector, the board is able to recognise the type of transducer, reconfigure itself and store the calibration data within the transducer, thereafter allowing a plug-and-play operation as transducers are changed. The paper provides an example of the operation in typical manufacturing test application and illustrates the stability and noise performance of the design.

Wireless Sensor Nodes (motes) have witnessed rapid development in the last two decades. Though the design considerations for Wireless Sensor Networks (WSNs) have been widely discussed in the literature, limited investigation has been done for their application in pipeline surveillance. Given the increasing number of pipeline incidents across the globe, there is an urgent need for innovative and effective solutions for deterring the incessant pipeline incidents and attacks. WSN pose as a suitable candidate for such solutions, since they can be used to measure, detect and provide actionable information on pipeline physical characteristics such as temperature, pressure, video, oil and gas motion and environmental parameters. This paper presents specifications of motes for pipeline surveillance based on integrated systems architecture. The proposed architecture utilizes a Multi-Agent System (MAS) for the realization of an Integrated Oil Pipeline Monitoring and Incident Mitigation System (IOPMIMS) that can effectively monitor and provide actionable information for pipelines. The requirements and components of motes, different threats to pipelines and ways of detecting such threats presented in this paper will enable better deployment of pipeline surveillance systems for incident mitigation. It was identified that the shortcomings of the existing wireless sensor nodes as regards their application to pipeline surveillance are not effective for surveillance systems. The resulting specifications provide a framework for designing a cost-effective system, cognizant of the design considerations for wireless sensor motes used in pipeline surveillance.

In the polishing process, contact force has obvious influence on material removal rate, thus realizing stability and effectiveness of force control is a significant issue for automatic polishing. This paper designs a pneumatic polishing force control system to achieve reliability force control. The system is based on a high speed on/off valve which is controlled by Pulse-Width Modulation (PWM) signals. PWM signals are generated by a Programmable Logic Controller (PLC). Proportion-Integration-Differentiation (PID) control algorithm with moving average filter is conducted in the PLC, thus dynamic property of the system has been improved. The pneumatic system is assembled on a 3-TPS hybrid robot with 5 degrees of freedom (DOF) movement to equip a polishing experimental platform. Polishing force control experiments are conducted on the experimental platform to research the performances and characteristics of the pneumatic polishing force system. According to the experimental results, the pneumatic system presents excellent effectiveness and response speed to track a certain desired polishing force value with small force tracking error. Percentage of absolute average errors are descending as the desired force values is increasing, and settling time is small enough for the polishing manufacture. Therefore, based on the high speed on/off valve, the pneumatic polishing force system proposed in this paper can be conducted in various polishing processes and satisfy special requirements of different workpieces. The polishing force control system can be adjusted to work at an adaptive status due to the excellent effectiveness, stabilization and response speed of force tracking.

This paper presents an approach for the trajectory planning of a hybrid machine tool based on vibration error, which aims at determining the optimal location and machining time of a given machining path. Firstly, an elastodynamic model of the hybrid robot is proposed by taking compliance of joints and limbs into account. Then, in order to evaluate vibration error in a typical machining path, two indices, i.e., the mean value and fluctuation of vibration error throughout the whole trajectory are proposed. Based on the Isight platform, sensitivity analysis is conducted. In addition, optimal trajectories are derived when adopting 3–4–5 polynomial and B-splines, respectively. Results show that the machining time and machining angle have important impacts on the mean value and fluctuation of vibration error. Especially, effects of the machining angle on the vibration error cannot be neglected. Comparison shows that adopting B-splines is conductive to decreasing the required torque and power. This paper provides a method on determining optimal machining location quickly for a given path throughout the whole workspace. The proposed method can be applied to trajectory planning of other hybrid robots or parallel kinematic machines.

In this paper an overview of recent augmented reality (AR) solutions for manufacturing execution systems (MES) is presented. The first part of the paper describes the challenges of integrating AR into MES, while the second part focuses on custom AR solutions. The last part of the paper highlights the advantages of the proposed approaches, as well as real life experimental results. Experiments are described in detail while the code for these applications is made public on author’s website.

The linear motion command (G01) is a widely used command format in CNC machining. However, the tangential direction discontinuity at the corner junction will cause velocity fluctuations and excite the structural vibration of machine tools. Corner smoothing methods with controlled tolerance are used to obtain continuous smooth motion. Typically, most methods generate a symmetrical cornering trajectory around the angle bisector, and the trajectory generally decelerates first and then accelerates, which limits the velocity increase. In this paper, a novel local asymmetrical corner trajectory smoothing method is proposed, which can realize accelerated/decelerated cornering transition. The proposed method can obtain the analytical solution in one step, which is different from two-step geometric-based corner path smoothing, and can generate the same cornering trajectory in back-and-forth parallel toolpath. In addition, this paper proposes a bidirectional planning and adjusting algorithm for the situation where smoothed cornering paths are close to each other or even overlap. The algorithm can generate a jerk-limited trajectory by coordinating the cornering error of adjacent corners, while respecting the user-specified tolerance. Experimental results demonstrate the effectiveness of the proposed method in contour accuracy and cycle time for CNC machining along short-segmented toolpath.

The optimization of the energy consumption of Industrial Robots (IRs) has been widely investigated. Unfortunately, on the field, the prediction and optimization strategies of IRs energy consumption still lack robustness and accuracy, due to the elevated number of parameters involved and their sensitivity to environmental working conditions. The purpose of this paper is to present, and share with the research community, an extensive experimental campaign that can be useful to validate virtual prototypes computing the energy consumption of robotic cells. The test cell, comprising a high payload IR equipped with multiple sensors and different payloads, is firstly described. The testing procedures are then presented. Experimental results are analyzed providing novel qualitative and quantitative evaluations on the contribution and relevance of different power losses and system operating conditions, clearly depicting the nonlinear relation between the energy consumption and various freely programmable parameters, thus paving the way to optimization strategies. Finally, all the experimental tests data are provided in the form of an open research dataset, along with custom Matlab scripts for plotting graphs and maps presented in this paper. These tests, which are verifiable via the shared dataset, consider the overall measured IR energy consumption (as drawn from the electric network) and highlight that, in some industrially interesting case scenarios, optimization potentials for energy savings of more than 50% are possible.

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.

With the closed-loop structure, parallel manipulators possess some inherent advantages, such as high stiffness, enhanced dynamics, and compact structure. As a result, parallel manipulators gradually gain wide application. In the great variety of parallel manipulators, a kind of high-speed parallel robot, the limb of which is composed of active pendulum and passive parallelogram, gets popular and has realized industrial application firstly. Dynamic performance is the core of the high-speed parallel manipulator, which is usually illustrated through dynamic performance analysis with the aid of the index. Thus, developing reasonable dynamic performance indices is of great theoretical and practical significance for the high-speed parallel manipulator. In this paper, the Coefficient of Variation of joint-space Inertia (CVI) index is proposed to illustrate the acceleration consistency of each limb of the parallel manipulator. By taking the X4 high-speed pick-and-place parallel manipulator as object, the dynamic model and joint-space inertia matrix is established, and the dynamic performance analysis is carried out with the proposed CVI index and the existing Joint-Reflected Inertia (JRI) index. Simulation results illustrate changes of the dynamic performance of the X4 parallel manipulator, and reveal that the JRI index is well complemented with the CVI index. Finally, the workspace with good dynamic performance is discussed. This paper provides a new approach for dynamic analysis and optimal design of high-speed parallel manipulator. Limb Jacobian matrices of the X4 manipulator are given.Dynamic model and inertia matrix of the X4 manipulator are deduced.The CVI index is proposed to evaluate the dynamic performance.Dynamic performance of the X4 mahipulator is analyzed with indices.The workspace with good dynamic performance is disscussed.