The industrial robot is more precisely an “automatically controlled, reprogrammable, multipurpose manipulator, programmable in three or more axes, which can be either fixed in place or mobile” (ISO 8373:2012). According to the International Federation of Robotics, by 2018, more than 400,000 new units were being installed annually, and the global average robot density in the manufacturing industry was 99 robots per 10,000 employees. More than 30% of all installed robots were in the automotive industry, the biggest customer for robots.

Research on measuring and calibrating, modeling, programming and controlling, and integrating systems has been conducted to give robotic manipulators a wider variety of industrial applications. This special issue covers technical and academic efforts related to new technologies that improve the accuracy and facilitate the implementation of robotic manipulators in industrial applications.

The first paper, by Ibaraki et al., outlines technical issues and future research directions for the implementation of model-based numerical compensation schemes for industrial robots. The random forest method is used by Kato et al. to construct a calibration model for positioning errors and identify industrial robots’ positioning errors. A procedure for the quasi-static compliance calibration of serial articulated industrial manipulators is proposed by Theissen et al. A review of the kinematic modeling theory and a derived algorithm to identify error sources for a six-axis industrial robot are presented by Alam et al. Nagao et al. derive a forward kinematics model and identify the kinematics parameters for the calibration of a robot-type machine tool. A novel trajectory generation algorithm, including a corner smoothing method for high-speed and high-accuracy machining by industrial robots, is proposed by Tajima et al. Sato et al. study the vibration characteristics of an industrial robot and derive a mathematical model that represents the dynamic behavior of the system. In the context of smart manufacturing, a multilayer quality inspection framework including a measurement instrument and a robot manipulator is introduced by Azamfirei et al. To support mass customization and the development of reconfigurable manufacturing systems, Inoue et al. propose an autonomous mobile robotic manipulator. Yonemoto and Suwa present an adaptive manipulation procedure to establish an automated scheduling technique that flexibly responds to unforeseen events, such as machine failures. Sasatake et al. introduce a learning system that is based on a method for calculating the similarity between tools, and they test it on a robot system for doing housework. Finally, for better knowledge of the key challenges that manufacturers experience in implementing collaborative industrial robots, an industrial survey is conducted by Andersson et al.

The editors sincerely appreciate the contributions of all the authors as well as the work of the reviewers. We are confident that this special issue will further encourage research and engineering work to increase our understanding and knowledge of robotic manipulators and their industrial applications.

As long as industrial robots are programmed by teach programming, their positioning accuracy is unimportant. With a wider implementation of offline programming and new applications such as machining, ensuring a higher positioning accuracy of industrial robots over the entire working space has become very important. In this paper, we first review the measurement schemes of end effector poses. We then outline kinematic models of serial articulated industrial manipulators to quantify the positioning accuracy with a focus on the extension of the classical Denavit-Hartenberg (DH) models to include rotary axis error motions. Subsequently, we expand the discussion on kinematic models to compliant robot models. The review highlights compliance models that are applied to calculate the elastic deformation produced by forces, namely gravity and external loads. Model-based numerical compensation plays an important role in machine tool control. This paper aims to present state-of-the-art technical issues and future research directions for the implementation of model-based numerical compensation schemes for industrial robots.

Because most industrial robots are taught using the direct teaching and playback method, they are unsuitable for variable production systems. Alternatively, the offline teaching method has limited applications because of the low accuracy of the position and posture of the end-effector. Therefore, many studies have been conducted to calibrate the position and posture. Positioning errors of robots can be divided into kinematic and non-kinematic errors. In some studies, kinematic errors are calibrated by kinematic models, and non-kinematic errors are calibrated by neural networks. However, the factor of the positioning errors has not been identified because the neural network is a black box. In another machine learning method, a random forest is constructed from decision trees, and its structure can be visualized. Therefore, we used a random forest method to construct a calibration model for the positioning errors and to identify the positioning error factors. The proposed calibration method is based on a simulation of many candidate points centered on the target point. A large industrial robot was used, and the 3D coordinates of the end-effector were obtained using a laser tracker. The model predicted the positioning error from end-effector coordinates, joint angles, and joint torques using the random forest method. As a result, the positioning error was predicted with a high accuracy. The random forest analysis showed that joint 2 was the primary factor of the X- and Z-axis errors. This suggests that the air cylinder used as an auxiliary to the servo motor of joint 2, which is unique to large industrial robots, is the error factor. With the proposed calibration, the positioning error norm was reduced at all points.

This article presents a procedure for the quasi-static compliance calibration of serial articulated industrial manipulators. Quasi-static compliance refers to the apparent stiffness displayed by manipulators at low-velocity movements, i.e., from 50 to 250 mm/s. The novelty of the quasi-static compliance calibration procedure lies in the measurement phase, in which the quasi-static deflections of the manipulator’s end effector are measured under movement along a circular trajectory. The quasi-static stiffness might be a more applicable model parameter, i.e., representing the actual manipulator more accurately, for manipulators at low-velocity movements. This indicates that the quasi-static robot model may yield more accurate estimates for the trajectory optimization compared with static stiffness in the implementation phase. This study compares the static and apparent quasi-static compliance. The static deflections were measured at discretized static configurations along circular trajectories, whereas the quasi-static deflections were measured under circular motion along the same trajectories. Loads of different magnitudes were induced using the Loaded Double Ball Bar. The static and quasi-static displacements were measured using a linear variable differential transformer embedded in the Loaded Double Ball Bar and a Leica AT901 laser tracker. These measurement procedures are implemented in a case study on a large serial articulated industrial manipulator in five different positions of its workspace. This study shows that the measured quasi-static deflections are bigger than the measured static deflections. This, in turn, indicates a significant difference between the static and apparent quasi-static compliance. Finally, the implementation of the model parameters to improve the accuracy of robots and the challenges in realizing cost-efficient compliance calibration are discussed.

In advanced industrial applications, like machining, the absolute positioning accuracy of a six-axis robot is indispensable. To improve the absolute positioning accuracy of an industrial robot, numerical compensation based on positioning error prediction by the Denavit and Hartenberg (D-H) model has been investigated extensively. The main objective of this study is to review the kinematic modeling theory for a six-axis industrial robot. In the form of a tutorial, this paper defines a local coordinate system based on the position and orientation of the rotary axis average lines, as well as the derivation of the kinematic model based on the coordinate transformation theory. Although the present model is equivalent to the classical D-H model, this study shows that a different kinematic model can be derived using a different definition of the local coordinate systems. Subsequently, an algorithm is presented to identify the error sources included in the kinematic model based on a set of measured end-effector positions. The identification of the classical D-H parameters indicates a practical engineering application of the kinematic model for improving a robot’s positioning accuracy. Furthermore, this paper presents an extension of the present model, including the angular positioning deviation of each rotary axis. The angular positioning deviation of each rotary axis is formed as a function of the axis’ command angles and the direction of its rotation to model the effect of the rotary axis backlash. The identification of the angular positioning deviation of each rotary axis and its numerical compensation are presented, along with their experimental demonstration. This paper provides an essential theoretical basis for the error source diagnosis and error compensation of a six-axis robot.

This study aims to calibrate the posture of a robot-type machine tool comprising parallel and serial links using a kinematics error model and verify the machining performance based on the measurement results of a machined workpiece calibrated with kinematics parameters. A robot-type machine tool (XMINI, Exechon Enterprises LLC) is used in this study. Typically, the performance required of a robot-type machine tool is not only dimensional accuracy but also the contour accuracy of the machined workpiece. Therefore, in this study, we first construct a forward kinematics model of a robot-type machine tool and identify the kinematics parameters used in it via spatial positioning experiments using a coordinate measuring machine. Based on the parameter identification results, we calibrate this robot-type machine tool and evaluate its machining performance in terms of the dimensional accuracy and contour accuracy of the machined workpiece.

The demands for machining by industrial robots have been increasing owing to their low installation cost and high flexibility. A novel trajectory generation algorithm for high-speed and high-accuracy machining by industrial robots is proposed in this paper. Linear interpolation in the workspace and smooth trajectory generation at the corners are important in industrial machining robots. Because industrial robots are composed of rotational joints, the joint space has a nonlinear relationship with the workspace. Therefore, linear interpolation in the joint space, which has been widely used in conventional machine tools, does not guarantee linear interpolation in the actual machining workspace. This results in the degradation of the machining surface. The proposed trajectory generation algorithm based on the decoupled approach can achieve linear interpolation in the workspace by separating the position commands into Cartesian coordinates and the orientation commands into spherical coordinates. In addition, a novel corner smoothing method that generates a smooth and continuous trajectory from discrete commands is proposed in this paper. The proposed kinematic local corner smoothing generates a smooth trajectory by using a 3-segmented constant jerk profile at the corners in the joint space. The sharp corners can thereby be replaced by smooth curves. The resulting cornering error is controlled by varying the cornering duration. The simulation results demonstrate the effectiveness of the proposed kinematic smoothing algorithm in achieving linear tool motion in straight sections and in generating smooth trajectories at corner sections within the user-defined tolerance.

Articulated robots are widely used in industries because they can perform manufacturing tasks with complicated movements. Higher speed and accuracy of motions are always required to improve the quality and productivity of products. The vibration characteristics of the robots are an important factor to achieve higher speed and accuracy motions. Robots are increasingly being used for machining. The vibration characteristics must also be considered when designing proper cutting conditions for the machining. To design control and cutting strategies for higher speed and accuracy motions or higher productivity of the machining process, it is effective to investigate the vibration characteristics of the robot and develop a mathematical model which can represents the vibration characteristics. The aim of this study is to investigate the vibration characteristics of an architectural robot and develop a mathematical model which can represent the dynamic behavior of the robot. To achieve this, vibration mode of an industrial architectural robot is analyzed based on measured frequency characteristics. According to the results of the modal analysis, it was clarified that the axial and angular stiffness of bearings of each joint of the robot has a significant impact on the vibration characteristics. Therefore, in this study, a mathematical model of the robot is developed considering the joint bearing stiffness. The mathematical model that also considers the kinematics of the robot, stiffness of reduction gears, control system for motors, and disturbance, such as friction and gravity, is introduced into the model. The control system is precisely modeled based on actual control algorithm in accordance with the implemented source codes. Although mass and inertia of the links are obtained from the 3D-CAD model, stiffness and damping parameters of the bearings and reduction gears are identified by matching the measured and simulated frequency responses. It has been confirmed that the model can adequately represents the vibration mode of the actual robot. Circular motion tests were performed to verify the model. Motion trajectories of the end effector were measured and simulated. As a result, it has been confirmed that the developed model is effective to analyze the dynamic behaviors.

In the era of market globalisation, the quality of products has become a key factor for success in the manufacturing industry. The growing demand for customised products requires a corresponding adjustment of processes, leading to frequent and necessary changes in production control. Quality inspection has been historically used by the manufacturing industry to detect defects before customer delivery of the end product. However, traditional quality methods, such as quality inspection, suffer from large limitations in highly customised small batch production. Frameworks for quality inspection have been proposed in the current literature. Nevertheless, full exploitation of the Industry 4.0 context for quality inspection purpose remains an open field. Vice-versa, for quality inspection to be suitable for Industry 4.0, it needs to become fast, accurate, reliable, flexible, and holistic. This paper addresses these challenges by developing a multi-layer quality inspection framework built on previous research on quality inspection in the realm of Industry 4.0. In the proposed framework, the quality inspection system consists of (a) the work-piece to be inspected, (b) the measurement instrument, (c) the actuator that manipulates the measurement instrument and possibly the work-piece, (d) an intelligent control system, and (e) a cloud-connected database to the previous resources; that interact with each other in five different layers, i.e., resources, actions, and data in both the cyber and physical world. The framework is built on the assumption that data (used and collected) need to be validated, holistic and on-line, i.e., when needed, for the system to effectively decide upon conformity to surpass the presented challenges. Future research will focus on implementing and validating the proposed framework in an industrial case study.

The manufacturing industry has identified a new megatrend of mass customization, which is one of the essential goals of Industry 4.0. This megatrend requires the realization of manufacturing that can respond quickly and flexibly to various changing production requirements and ensure the achievement of various quality criteria. However, the manufacturing cannot be realized by conventional manufacturing systems in which reconfigurations need to be performed by skilled engineers. This paper proposes a new reconfigurable manufacturing system concept based on an ultra-flexible transfer system. Particularly, an autonomous mobile robotic manipulator, consisting of a high-performance automated guided vehicle module and a collaborative robotic manipulator module, represents a key component of the system concept. In this context, the focus is on the cooperative control between the modules of the autonomous mobile manipulator, which is essential for high-precision processes (e.g., machining, assembly, measurement, inspection), and its wide operating area. The experimental results confirm that the proposed cooperative control improves the positioning performance of the autonomous mobile manipulator, including the time required for positioning and the positioning accuracy.

Manufacturing systems are affected by uncertainties, such as machine failure or tool breakage, which result in system downtime and productivity deterioration. In machining processes, system downtime must be reduces. This study aims to establish an automated scheduling technique that flexibly responds to unforeseen events, such as machine failure, based on adaptive operations of the handling manipulator instead of an operation schedule for the machine tools. We propose an “adaptive manipulation” procedure for establishing a reactive revision policy. The reactive revision policy modifies a portion of the manipulator operation sequence, followed by the machine operation sequence. We conduct a physical scheduling simulation on a material-handling manipulator system imitating a job-shop manufacturing system. Through simulations involving machine breakdown scenarios, the applicability of the reactive revision policy based on adaptive manipulation is demonstrated.

Population aging has become a major problem in developed countries. As the labor force declines, robot arms are expected to replace human labor for simple tasks. A robotic arm attaches a tool specialized for a task and acquires the movement through teaching by an engineer with expert knowledge. However, the number of such engineers is limited; therefore, a teaching method that can be used by non-technical personnel is necessitated. As a teaching method, deep learning can be used to imitate human behavior and tool usage. However, deep learning requires a large amount of training data for learning. In this study, the target task of the robot is to sweep multiple pieces of dirt using a broom. The proposed learning system can estimate the initial parameters for deep learning based on experience, as well as the shape and physical properties of the tools. It can reduce the number of training data points when learning a new tool. A virtual reality system is used to move the robot arm easily and safely, as well as to create training data for imitation. In this study, cleaning experiments are conducted to evaluate the effectiveness of the proposed method. The experimental results confirm that the proposed method can accelerate the learning speed of deep learning and acquire cleaning ability using a small amount of training data.

The industrial collaborative robot (ICR) application is a promising automation technology that combines human abilities with the repeatability and accuracy of an industrial robot. Yet, industrial challenges have prevented ICR applications from being implemented extensively in industry. Therefore, the purpose of the presented work is to deepen the knowledge of the key challenges that manufacturers experience during the implementation of ICR applications. In this study, a case study approach was used with eight companies to identify those challenges. The analysis of the qualitative data was conducted based on thirteen interviews with respondents from the industry to identify their challenges when implementing ICR applications. In this paper, a defined implementation process is presented that is combined with three significant areas of challenges relevant for the implementation of ICR applications, i.e., safety, knowledge, and functionality. Then, these areas are used as a basis to identify the corresponding challenges during the early implementation phases. The findings of the study point to an insufficient understanding of safety assessment and a lack of operator involvement in the pre-study phase that was propagated into the later implementation phases. The application design phase was identified to have several ad-hoc approaches due to a lack of knowledge concerning the application of ICR. In the factory installation phase, the challenges included increasing flexibility and ensuring standardised ways of working. This paper makes three distinct contributions to the research community. First, it provides rich data to the research concerning the implementation of applications of ICR, and it focuses on three areas, i.e., safety, knowledge, and functionality, and the challenges associated with their respective implementations. Second, contributions are made to the literature on implementing new technology, and they are focused on the early phases. Third, the results of this paper suggest that the role of system integrators might change in ICR application implementation projects. This paper contributes to practitioners a list of challenges that they might face during the implementation of ICR.

Based on the rapid advancement of IoT technology, it has become pervasive in various industries for promoting effective production, including the plastic injection molding industry. In this study, a fundamental investigation of mold deformation was conducted to develop a monitoring system. Mahalanobis distance (MD), which is calculated from mold strain data, was adopted in this monitoring system. We determined that the simple MD index is helpful for judging between normal and abnormal mold states. This index is expected to be a key component of future IoT applications.

Soft continuum manipulators are comprised of flexible materials in a serpentine shape. Such manipulators can be controlled mechanically through tendons or pneumatic muscles. Continuum manipulators utilizing tendons are traditionally formed in a thick cross section, which presents limitations in achieving a high bending range as well as difficulties for storage and transportation. This study introduces a continuum manipulator comprised of two thin plastic bands and driven by a tendon to provide a bending action. The manipulator’s thin body form enables it to be rolled up for storage and transportation. Experimental results on different section lengths show the possibility of achieving a horizontal displacement of up to 34% of the bending-segment’s length, and a full closed-loop curvature for most segments. However, the results also indicated an elongation of the tip paths owing to gravity. These results, in addition to the manipulator’s flexibility and light weight features, confirm its suitability for applications in space and underwater environments.

Magnetic levitation technology is expected to provide a solution for achieving nanometer-scale positioning accuracy. However, magnetic leakage limits the application of the magnetic levitation stage. To reduce magnetic density, motors should be installed at an appropriate distance from the table. This increases the axis interference between the horizontal thrust and the pitching, making it difficult to achieve stable levitation. In this study, a magnetic levitation stage system that has a unique motor structure fusing a gravity compensation function and pitching moment compensation is proposed. This compensation mechanism operates automatically using the passive magnetic circuit structure, ensuring that noises from the coil current and the timing gaps do not affect the driving characteristics and that neither wiring nor sensors are required. The basic characteristics were evaluated through the driving experiments, and the efficiency of the proposed gravity and pitching moment compensation system was demonstrated.

We propose the use of the line section method with crossed line beams for the process control of laser wire deposition. This method could be used to measure the height displacement in front of a laser spot when the processing direction changes. In laser processing, especially laser deposition of metal additive manufacturing, the laser process control technique that controls the processing parameters based on the measured height displacement in front of a laser processing spot is indispensable for high-accuracy processing. However, it was impossible to measure the height displacement in front of a processing laser spot in a processing route in which the processing direction changes as the measurement direction of the conventional light-section method comprising the use of a straight-line beam is restricted although the configuration is simple. In this paper, we present an in-process height displacement measurement system of the light-section method using two crossed line beams. This method could be used to measure the height displacement in a ±90° direction by projecting two crossed line beams from the side of a laser processing head with a simple configuration comprising the addition of one line laser to the conventional light-section method. The height displacement can be calculated from the projected position shift of the line beams irrespective of the measurement direction by changing the longitudinal position on the crossed line beams according to the measurement direction. In addition, the configuration of our proposed system is compact because the imaging system is integrated into the processing head. We could measure the height displacement at 2.8–4 mm in front of a laser processing spot according to the measurement direction by reducing the influence of intense thermal radiation. Moreover, we experimentally evaluated the height displacement measurement accuracy for various measurement directions. Finally, we evaluated continuous deposition in an “L” shape wherein the deposition direction was changed while using a laser wire direct energy deposition machine for the laser process control based on the in-process height displacement measurement result. We achieved highly accurate continuous deposition at the position wherein the processing direction changes despite the acceleration and deceleration of the stage by laser process control.

At urban production sites, laser cutting is an essential technology for high-speed flexible sheet-metal processing. This study aims to detect defective cuts by sensing laser-cutting-induced light emission and elucidate meaningful features for processing-based detection. The proposed method comprises three steps. In the first step, the sensors installed in the laser head acquire the spectra of light generated during processing, and data analysis software converts the spectral data into spectrograms and stacked-graph images. In the second step, image processing software extracts the edges of both images and emphasizes the periodic features in normal laser cutting. In the final step, a one-class support vector machine recognizes defective cuts from the extracted features. Verification tests using multiple normal and abnormal cut data confirmed that the proposed method accurately detected defective cuts.