Industrial Collaborative Robots Research Articles

Multi-scale progressive fusion-based depth image completion and enhancement for industrial collaborative robot applications

Multi-scale progressive fusion-based depth image completion and enhancement for industrial collaborative robot applications

A Hybrid Digital Twin Scheme for the Condition Monitoring of Industrial Collaborative Robots

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models’ information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots.

Pick the Right Co-Worker: Online Assessment of Cognitive Ergonomics in Human–Robot Collaborative Assembly

Human-robot collaborative assembly systems enhance the efficiency and productivity of the workplace but may increase the workers’ cognitive demand. This paper proposes an online and quantitative framework to assess the cognitive workload induced by the interaction with a co-worker, either a human operator or an industrial collaborative robot with different control strategies. The approach monitors the operator’s attention distribution and upper-body kinematics benefiting from the input images of a low-cost stereo camera and cutting-edge artificial intelligence algorithms (i.e. head pose estimation and skeleton tracking). Three experimental scenarios with variations in workstation features and interaction modalities were designed to test the performance of our online method against state-of-the-art offline measurements. Results proved that our vision-based cognitive load assessment has the potential to be integrated into the new generation of collaborative robotic technologies. The latter would enable human cognitive state monitoring and robot control strategy adaptation for improving human comfort, ergonomics, and trust in automation.

Analysis of quality standards for industrial collaborative robots based on user‐centered design framework

AbstractIndustrial collaborative robots have become increasingly important in recent years due to their ability to work safely and efficiently alongside humans. As a result, there is a growing need for evaluation standards to ensure the quality of collaborative robots. However, existing studies only consider system‐centered and technical aspects of collaborative robots, and there is a lack of research on user‐centered quality evaluation. In this study, we identified 21 user requirements based on a user‐centered design framework and confirmed the limitations of existing quality standards by reviewing the standard clauses for collaborative robots. It was found that user needs to be related to performance, safety, and even usability and enjoyment are already being expressed according to the user‐centered design framework, but the quality standards for these needs only present design principles or do not consider them at all. This study provides information on the quality attributes that need to be fulfilled to satisfy user requirements and suggests the need and direction for further research on the user‐centered evaluation of collaborative robots. Accordingly, the user's perception and experience of collaborative robots are expected to improve.

산업용 협동 로봇과 작업자연구 트랜드 쟁점 연구

산업용 협동 로봇과 작업자연구 트랜드 쟁점 연구

A Novel Strain Wave Gear Reducer with Double Flexsplines

Strain wave gear reducers, also known as harmonic drives, are widely used in industrial robots and collaborative robots. The zero-backlash feature is very important for these applications. However, this places extremely high demands on the machining accuracy of the strain wave gear reducer. Excessive manufacturing errors will lead to excessive backlash, affecting transmission accuracy or making installation difficult, and the flexible spline (or flexspline) is prone to wear, resulting in reduced accuracy with use. This study proposes a novel strain wave gear reducer with a double flexspline structure. The original “circular spline” which was rigid will be redesigned to be slightly flexible and deformable with an additional deformation adjustment structure, which reduces the requirements for machining accuracy, and realizes the same zero-backlash characteristic of traditional structure. The experimental results show that the new strain wave gear reducer has extremely low lost motion, hysteresis loss, and high torsional rigidity. The new strain wave gear reducer provides a more economical way to realize the zero-backlash reducer and helps to solve the problem of the accuracy decline of the strain wave gear reducer due to the wear of the flexspline.

Classification of Collaborative Industrial Robots in the Digital Transformation of High-Tech Enterprises

Classification of Collaborative Industrial Robots in the Digital Transformation of High-Tech Enterprises

Mixed Reality for Safe and Reliable Human-Robot Collaboration in Timber Frame Construction

In the field of construction, human-robot collaboration and mixed reality (MR) open new possibilities. However, safety and reliability issues persist. The lack of flexibility and adaptability in current preprogrammed systems hampers real-time human-robot collaboration. A key gap in this area lies in the ability of the robot to interpret and accurately execute operations based on the real-time visual instructions and restrictions provided by the human collaborator and the working environment. This paper focuses on an MR-based human-robot collaboration method through visual feedback from a vision-based collaborative industrial robot system for use in wood stereotomy which we are developing. This method is applied to an alternating workflow in which a skilled carpenter lays out the joinery on the workpiece, and the robot cuts it. Cutting operations are instructed to the robot only through lines and conventional “carpenter’s marks”, which are drawn on the timbers by the carpenter. The robot system’s accuracy in locating and interpreting marks as cutting operations is evaluated by automatically constructing a 3D model of the cut shape from the vision system data. A digital twin of the robot allows the carpenter to previsualize all motions that are required by the robot for task validation and to know when to enter the collaborative workspace. Our experimental results offer some insights into human-robot communication requirements for collaborative robot system applications in timber frame construction.

A robust and simple collision avoidance approach for pick and place robot applications

Purpose The aim of this study is to create a robust and simple collision avoidance approach based on quaternion algebra for vision-based pick and place applications in manufacturing industries, specifically for use with industrial robots and collaborative robots (cobots). Design/methodology/approach In this study, an approach based on quaternion algebra is developed to prevent any collision or breakdown during the movements of industrial robots or cobots in vision system included pick and place applications. The algorithm, integrated into the control system, checks for collisions before the robot moves its end effector to the target position during the process flow. In addition, a hand–eye calibration method is presented to easily calibrate the camera and define the geometric relationships between the camera and the robot coordinate systems. Findings This approach, specifically designed for vision-based robot/cobot applications, can be used by developers and robot integrator companies to significantly reduce application costs and the project timeline of the pick and place robotics system installation. Furthermore, the approach ensures a safe, robust and highly efficient application for robotics vision applications across all industries, making it an ideal solution for various industries. Originality/value The algorithm for this approach, which can be operated in a robot controller or a programmable logic controller, has been tested as real-time in vision-based robotics applications. It can be applied to both existing and new vision-based pick and place projects with industrial robots or collaborative robots with minimal effort, making it a cost-effective and efficient solution for various industries.

Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot

Industrial collaborative robots (cobots) are known for their ability to operate in dynamic environments to perform many different tasks (since they can be easily reprogrammed). Due to their features, they are largely used in flexible manufacturing processes. Since fault diagnosis methods are generally applied to systems where the working conditions are bounded, problems arise when defining condition monitoring architecture, in terms of setting absolute criteria for fault analysis and interpreting the meanings of detected values since working conditions may vary. The same cobot can be easily programmed to accomplish more than three or four tasks in a single working day. The extreme versatility of their use complicates the definition of strategies for detecting abnormal behavior. This is because any variation in working conditions can result in a different distribution of the acquired data stream. This phenomenon can be viewed as concept drift (CD). CD is defined as the change in data distribution that occurs in dynamically changing and nonstationary systems. Therefore, in this work, we propose an unsupervised anomaly detection (UAD) method that is capable of operating under CD. This solution aims to identify data changes coming from different working conditions (the concept drift) or a system degradation (failure) and, at the same time, can distinguish between the two cases. Additionally, once a concept drift is detected, the model can be adapted to the new conditions, thereby avoiding misinterpretation of the data. This paper concludes with a proof of concept (POC) that tests the proposed method on an industrial collaborative robot.

The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

A collaborative robot, or cobot, enables users to work closely with it through direct communication without the use of traditional barricades. Cobots eliminate the gap that has historically existed between industrial robots and humans while they work within fences. Cobots can be used for a variety of tasks, from communication robots in public areas and logistic or supply chain robots that move materials inside a building, to articulated or industrial robots that assist in automating tasks which are not ergonomically sound, such as assisting individuals in carrying large parts, or assembly lines. Human faith in collaboration has increased through human–robot collaboration applications built with dependability and safety in mind, which also enhances employee performance and working circumstances. Artificial intelligence and cobots are becoming more accessible due to advanced technology and new processor generations. Cobots are now being changed from science fiction to science through machine learning. They can quickly respond to change, decrease expenses, and enhance user experience. In order to identify the existing and potential expanding role of artificial intelligence in cobots for industrial applications, this paper provides a systematic literature review of the latest research publications between 2018 and 2022. It concludes by discussing various difficulties in current industrial collaborative robots and provides direction for future research.

Safe warehouse in collaborative robot technologies

Industrial robots have gained significant application and popularity in modern industries worldwide due to their productivity, reliability, efficiency, and ability to perform hazardous operations that pose or potentially pose a risk to people. However, there are cases when classic industrial work cannot be used, and human work becomes the only alternative in production. With the continuous rapid development of technology, a new type of industrial robot has emerged, namely collaborative industrial robots. The peculiarity of the latter lies in the functional and hardware capabilities of interacting with a person and mutually complementing him, performing joint technological operations. These capabilities are achieved with the help of advanced technologies such as technical vision, artificial intelligence, signal processing technologies from various sensors, etc. The use of collaborative industrial robots in modern production, together with humans, poses many problematic issues related to human safety in production processes. A brief analysis of the available information sources indicates the relevance of the above question and the variety of approaches to its solution and research. However, there are currently no comprehensive solutions, and there is fragmentation regarding human safety in collaborative robotic technologies. Existing gaps in ISO safety standards are highlighted, which do not fully address the known safety components of human collaboration and collaborative industrial robots. The essence of the definition of the safety component in collaborative robotic technologies is highlighted as a complex concept that involves the use of hardware, software, ergonomics, and other components to support and ensure human safety in the conditions of technological collaboration. Attention is focused on improving the process of training personnel and updating technological equipment for safety efficiency in collaborative robotic technologies. Recommendations that would complement the existing ISO standards are proposed. The above is defined as a promising direction of research in the field of collaborative robotics.

Mapping socio-technical dependencies to enable the successful adoption of collaborative robots in industry

Collaborative robots (cobots) allow for combining complementary strengths of humans and robots. However, this close human-cobot interaction creates a highly complex socio-technical system. This is a key adoption and integration challenge as it complicates an understanding of dependencies between cobots and their surrounding workplace: while a cobot needs to fit to the product to be manipulated, the human operator and the workplace, those three domains also need to be adjusted to allow for a successful cobot use. This paper presents a matrix-based approach to analyze dependencies between the four domains; (1) cobot, (2) product, (3) human/operator, and (4) workplace.

Review of Current Status and Future Directions for Collaborative and Semi-Automated Automotive Wire Harnesses Assembly

Wire harnesses are vital for any modern automotive vehicle. They control the basic functions in a vehicle, for example, windshield wipers and critical functions such as sensors, cameras, and autopilot functions. Thus, the quality of wire harness assembly is highly important. Today, wire harnesses are usually assembled manually, which creates unergonomic and tedious working conditions for operators. Traditional and collaborative industrial robots have been identified as possible solutions to overcome challenges faced by operators in this type of assembly. The international research community has proposed many solutions for automating the assembly of wire harnesses in automotive vehicles but despite these solutions, the industry has not been able to adopt a method to automate this assembly process fully or partially. This paper presents a review of findings on robot-assisted wire harness assembly processes based on a systematic literature review. Specifically, the assembly of wire harnesses in Electric Vehicles (EVs). The state-of-the-art review focuses on solutions to improve unergonomic work situations and ensure the quality of assembly operations. Best practices and reasons for the lack of extensive implementation in automotive final assembly systems are described. Further, the paper presents suggestions based on success stories where the automation of the wire harness assembly in automotive vehicles has been realised by leveraging human-centred automation solutions. Based on the findings, this paper identifies the research for future study. The findings also indicate that there is already technology that can support the automation of wire harness assembly processes in EVs but it is crucial to identify the human aspects and the role of humans in the assembly of wire harness assembly process.

ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

The Condition Monitoring (CM) of industrial collaborative robots (cobots) has the potential to decrease downtimes in highly automated production systems. However, in such complex systems, defining a strategy for effective CM and automatically detecting failures is not straightforward. In this paper, common issues related to the application of CM to collaborative manipulators are first introduced, discussed, and then, a solution based on the Robot Operating System (ROS) is proposed. The content of this document is highly oriented towards applied research and the novelty of this work mainly lies in the proposed CM architecture, while the methodology chosen to assess the manipulator’s health is based on previous research content. The CM architecture developed and the relative strategy used to process data are useful for the definition of algorithms for the automatic detection of failures. The approach is based on data labeling and indexing and aims to extract comparable data units to easily detect possible failure. The end of this paper is provided with a proof of concept (PoC) applied to an industrial collaborative manipulator where the proposed CM strategy has been implemented and tested in a real application scenario. Finally, it is shown how the proposed methodology enables the possibility of defining standard Health Indicators (HIs) to detect joint anomalies using torque information even under a highly dynamic and non-stationary environmental conditions.

Human-Like Movements of Industrial Robots Positively Impact Observer Perception.

The number of industrial robots and collaborative robots on manufacturing shopfloors has been rapidly increasing over the past decades. However, research on industrial robot perception and attributions toward them is scarce as related work has predominantly explored the effect of robot appearance, movement patterns, or human-likeness of humanoid robots. The current research specifically examines attributions and perceptions of industrial robots-specifically, articulated collaborative robots-and how the type of movements of such robots impact human perception and preference. We developed and empirically tested a novel model of robot movement behavior and demonstrate how altering the movement behavior of a robotic arm leads to differing attributions of the robot's human-likeness. These findings have important implications for emerging research on the impact of robot movement on worker perception, preferences, and behavior in industrial settings.

Multi-actor perspectives on human robotic collaboration implementation in the heavy automotive manufacturing industry - A Swedish case study

Implementing an industrial collaborative robot for Human-Robot Collaboration (HRC) in the automotive manufacturing industry is an emerging technology-driven solution aiming to increase production efficiency and reduce the human operator's ergonomic load. Successful implementation of innovative technology depends on technical feasibility and on the acceptance by the affected actors. Many studies exist that focus on the technical aspects of HRC, however, research that focuses on understanding the multi-actor concerns of HRC adoption is rare. In an effort to support the successful adoption of industrial collaborative robots, this study aims to understand the concerns of the various actors who work at the operational and management levels influencing future HRC adoption in the heavy automotive manufacturing industry.A literature review was conducted to understand the HRC implementation challenges and the methods used to investigate multi-actor involvement in advance of, and during, the implementation stage. After reviewing existing studies, the actor analysis method was selected to present the actors' perceptions using the action, factor, and goal (AFG) list to understand different actors’ opinions of HRC adoption, using a Swedish heavy vehicle manufacturing company case study.The case study results showed that the actors from the same organization had different concerns but mostly positive expectations for future HRC adoption. The actors’ perception map shows the details pertaining to Actions, Concerns, and Goals as well as the logical flow between these elements in regards to HRC future adoption. The involvement of different actor groups prior to new solution implementation contributes to a holistic view of potential implementation influences and challenges in the organization. Actor analysis can provide a set of analysis processes that comply with multi-actor perceptions to understand future adoption challenges from different perspectives. In the next step, safety-related issues and under-development standardization are the key challenges of HRC implementation.

Working with Bounded Collaboration: A Qualitative Study on How Collaboration is Co-constructed around Collaborative Robots in Industry

We investigate how collaboration is understood and configured in industrial workplaces with collaborative robots (cobots). Through a qualitative analysis of 115 case studies of companies using cobots and 14 semi-structured interviews with cobot manufacturers and users, we examine the usages of cobots in the manufacturing industry over the entire temporal spectrum from pre-introduction to completed implementation. By synthesizing diverse stakeholders' perspectives, we present a set of main findings; key roles of a few supportive production workers during the adoption of cobots; a fragmentation of work tasks and the resulting loss of job identity among workers; the disunified meaning of "collaboration" which is under constant development; and the collaborative space and the working rhythms between production workers and cobots. By reconsidering what collaboration means in the workplace with cobots, we propose the concept of bounded collaboration, which means that the anticipated collaboration is manifested in a partial and limited manner within a collaborative technology. Finally, we provide practical suggestions for examining and supporting organizations and users in their adoption of cobots.

A Framework of Robot Skill Learning From Complex and Long-Horizon Tasks

Robot Learning from humans is a promising paradigm for directly transferring human skills to robots. This learning allows robots to encapsulate task constraints and motion patterns from human demonstrations as well as acquire skills that can be adapted to unseen scenarios. Even though many state-of-the-art skill-learning successes have been achieved, simultaneously addressing variability from a complex and long-horizon manipulation task and generalizing it to external uncertainty remains a challenge. This efficient skill learning has to allow for handling large-scale, high-dimensional demonstrations, adapting to environmental changes (starting, via and end points and obstacles), generalizing to task constraints (trajectory precision, stiffness), and measuring uncertainty in the reproduction. To this effect, we present a novel robot skill-learning framework called SVGP-CoGP that will implement all the aforementioned properties by encoding task variability from multiple demonstrations using Sparse Variational Gaussian Processes (SVGP) and adapting to additional constraints via a coregionalized multi-output GP (CoGP) based on SVGP. The proposed method can significantly reduce the computational complexity of model fitting by making use of the variational inference of GP models, which makes it possible for robots to learn skills from complex and long-horizon tasks. We evaluated and compared the effectiveness and strengths of our framework with existing probabilistic methods on a Kinova robot that performed emergency button-pressing tasks. The results indicated that our framework allowed the robot to learn skills from complex and long-horizon manipulation tasks that outperformed baselines both in quantitative evaluation and in an online test. Note to Practitioners–The objective of this work is to address the problem of robot-learning from complex and long-horizon manipulation tasks to allow end-users to teach robots new tasks by having them learn from human demonstrations instead of being programmed. We start with a brief historical overview of widely used methods and summarize five prominent capabilities that a skill-learning approach should have: variability, uncertainty, correlation, extrapolation, and adaptability. We then propose an entirely GP-based skill-learning framework by simultaneously addressing all those capabilities by using a sparse variational Gaussian process (SVGP) in conjunction with a coregionalized multioutput GP model. The proposed framework incorporated variational inference and kernel treatments such that the robot learned skills from large-scale demonstrations and high-dimensional trajectories. Finally, experimental evaluation and performance comparisons were performed in a real robot button-pressing task, the results of which indicated that our proposed method enables robots to achieve complex and long-horizon manipulation tasks in dynamic and unstructured environments. With the rapid development of collaborative robots in service and industry, our findings have application scenarios as diverse as robot learning from demonstration, robot skill learning, human–robot collaboration, and other complex and long-horizon manipulation tasks.

Overload Clutch With Integrated Torque Sensing and Decoupling Detection for Collision Tolerant Hybrid High-Speed Industrial Cobots

A hybrid high-speed industrial collaborative robot can switch between collaborative mode and high-speed mode, combining the advantages of both. While promising, this concept comes with some challenges such as dealing with collisions at high speed. An overload clutch with integrated torque sensing and clutch decoupling detection is presented as enabling technology. Both joint torque sensing and clutch decoupling detection are realized with the same capacitive measurement hardware that consists of paired electrodes. A prototype device is experimentally validated through comparison with a reference torque sensor.