Industrial Collaborative Robots Research Articles

Projekt oraz oprogramowanie stanowiska z robotem kolaboracyjnym z wykorzystaniem wirtualnej rzeczywistości

Collaborative industrial robots meet the modern approach to robotization. They enable cooperation with a human being in the robot’s work area. As part of the article, it was decided to design and build a robotic station enabling the implementation of human–machine cooperation. CAD models of the station were created using the Autodesk Inventor software. The station design was made in the RobotStudio environment with the use of the IRB 14000 YuMi robot. The software is based on smart components and Rapid language code. A graphical user interface was created, interactive with a human in virtual reality. The software was verified with the participation of the operator in virtual reality.

Facing with Collaborative Robots: The Subjective Experience in Senior and Younger Workers.

In the past few years, collaborative robots (i.e., cobots) have been largely adopted within industrial manufacturing. Although robots can support companies and workers in carrying out complex activities and improving productivity, human factors related to cobot operators have not yet been thoroughly investigated. The present study aims to understand the subjective experience of younger and senior workers interacting with an industrial collaborative robot. Results show that workers' acceptance of cobots is high, regardless of age and control modality used. Interesting differences between seniors and younger adults emerged in the evaluations of user experience, usability, and perceived workload of participants and are detailed and commented in the last part of the work.

Acceptance of Industrial Collaborative Robots by People With Disabilities in Sheltered Workshops.

The integration of people with disabilities into the working world is an important, yet challenging field of research. While different inclusion efforts exist, people with disabilities are still under-represented in the open labor market. This paper investigates the approach of using a collaborative robot arm to support people with disabilities with their reintegration into the workplace. However, there is currently little literature about the acceptance of an industrial robot by people with disabilities and in cases where a robot leads to stress, fear, or any other form of discomfort, this approach is not feasible. For this reason, a first user study was performed in a sheltered workshop to investigate the acceptance of a robot arm by workers with disabilities. As a first step in this underdeveloped field, two main aspects were covered. Firstly, the reaction and familiarization to the robot arm within a study situation was closely examined in order to separate any effects that were not caused by the moving robot. Secondly, the reaction toward the robot arm during collaboration was investigated. In doing so, five different distances between the robot arm and the participants were considered to make collaboration in the workplace as pleasant as possible. The results revealed that it took the participants about 20 min to get used to the situation, while the robot was immediately accepted very well and did not cause fear or discomfort at any time. Surprisingly, in some cases, short distances were accepted even better than the larger distances. For these reasons, the presented approach showed to promise for future investigations.

Wireless communication based on Raspberry pi and Codesys for mobile robots using IoT technology

The industrial environment is going through exponential changes, due to the diversity of technological solutions that appear more and more frequently and the increase of productivity at increasing capacities. Due to this fact in the industrial area the number of devices, processing systems, collaborative robots, mobile robots and industrial equipment is increasing more and more. Consequently, there is a need for communication and connectivity of entities, common physical or virtual functioning and decision making, all of which are fundamental to the transition to the new concept of Industry 4.0. This study presents how wireless communication can be achieved in a mobile robotic platform that serves an industrial sector with other equipment in the production area, such as industrial equipment, collaborative industrial robots or other mobile robots. Also in this paper is presented how to create an HMI interface for the mobile platform that can be accessed from a touchscreen display mounted on the robot or from any mobile device connected to the internet.

Preliminary Evaluation of Composite Learning Tracking Control on 7-DoF Collaborative Robots

Abstract With the increase of computational power, adaptive control becomes more and more attractive for industrial robots due to the ability to handle parametric uncertainty and the potential to achieve higher tracking accuracy. This paper implements a recently developed adaptive control scheme termed composite learning robot control (CLRC) for tracking control of a 7-degree-of-freedom industrial collaborative robot named Franka Emika Panda. The desired trajectory is specifically designed to evaluate the performance of the CLRC compared with the standard adaptive robot control and composite adaptive robot control via both simulations and experiments. Comparative results demonstrate that the CLRC meets the requirement of real-time computation and outperforms the other two control schemes with respect to tracking accuracy and parameter convergence.

Passivity Guaranteed Dynamic Friction Model With Temperature and Load Correction: Modeling and Compensation for Collaborative Industrial Robot

In this paper, a new comprehensive dynamic friction model for a collaborative industrial robot joint that considers the velocity, temperature, and load torque is proposed. The variation of load-dependent friction among the four-quadrant operation depending on the sign of load-torque and speed is studied. The new model’s passivity property is analyzed to obtain a physically meaningful and experimentally identified friction model. A sufficient condition is presented in terms of a simple algebraic inequality involving the parameters of the model. The model parameter identification procedure and validation of model effectiveness are demonstrated experimentally on a commercial collaborative robot manipulator. Moreover, the proposed friction model’s benefits are demonstrated in two different robot applications: friction compensation and direct teaching (smooth lead-through programming) applications. Significant tracking performance improvement in the root-mean-square errors up to 76% was achieved with the proposed friction model compared to the uncompensated cases in the friction compensation application. In the direct teaching application, the new model, which precisely estimates joint friction, results in a significant decrease in interaction forces up to 66%. These experimental results validate the performance of the proposed friction model.

Defining SMEs’ 4.0 Readiness Indicators

Industry 4.0 revolution offers smart manufacturing; it systematically incorporates production technology and advanced operation management. Adopting these high-state strategies can increase production efficiency, reduce energy consumption, and decrease manufacturer costs. Simultaneously, small and medium-sized enterprises (SMEs) were the backbone of economic growth and development. They still lack both the knowledge and decision-making to verify this high-stage technology’s performance and implementation. Therefore, the research aims to define the readiness indicators to assess and support SMEs toward Industry 4.0. The research begins with found aspects that influence the SME 4.0 readiness by using Bibliometric techniques. The result shows the aspects which were the most occurrences such as the Industrial Internet, Cloud Manufacturing, Collaborative Robot, Business Model, and Digital Transformation. They were then grouped into five dimensions by using the visualization of similarities (VOS) techniques: (1) Organizational Resilience, (2) Infrastructure System, (3) Manufacturing System, (4) Data Transformation, and (5) Digital Technology. Cronbach’s alpha then validated the composite dimensions at a 0.926 level of reliability and a significant positive correlation. After that, the indicators were defined from the dimension and aspects approach. Finally, the indicators were pilot tested by small enterprises. It appeared that 23 indicators could support SMEs 4.0 readiness indication and decision-making in the context of Industry 4.0.

Success factors for introducing industrial human-robot interaction in practice: an empirically driven framework

Human-robot interaction (HRI) promises to be a means whereby manufacturing companies will be able to address current challenges like a higher demand for customization. However, despite comparably low costs, there are only few applications in practice. To date, it remains unclear which factors facilitate or hinder the successful introduction of industrial collaborative robots (cobots). In a three-step approach, we first developed a comprehensive two-dimensional framework covering three separate phases and four essential components for human-robot working systems. Secondly, we reviewed related literature to identify relevant success factors. Thirdly, in an online survey we asked leading representatives of German manufacturing companies (n = 81) to assess the importance of these factors from a practical point of view. The results reveal that besides technology-related factors like occupational safety and appropriate cobot configuration, employee-centered factors like the fear of job loss and ensuring an appropriate level of trust in the robot are considered important. However, company representatives seem to underestimate the impact of subtle measures to increase employee acceptance which could be incorporated into internal communication strategies prior to and during the introduction of cobots. Comparative analysis based on three distinct application scenarios suggests that most success factors’ practical importance is independent of the motivation for implementing HRI. Furthermore, answers from practitioners in free-text fields reveal that success factors which intuitively come to their mind such as financial factors are not necessarily perceived most important. Finally, we argue for more application-oriented research that focuses on practically relevant factors to guide HRI research, inform cobot development, and support companies in overcoming apparent barriers.

The Effect of Anthropomorphism and Failure Comprehensibility on Human-Robot Trust

The application of anthropomorphic features to robots is generally considered to be beneficial for human- robot interaction. Although previous research has mainly focused on social robots, the phenomenon gains increasing attention in industrial human-robot interaction, as well. In this study, the impact of anthropomorphic design of a collaborative industrial robot on the dynamics of trust is examined. Participants interacted with a robot, which was either anthropomorphically or technically designed and experienced either a comprehensible or an incomprehensible fault of the robot. Unexpectedly, the robot was perceived as less reliable in the anthropomorphic condition. Additionally, trust increased after faultless experience and decreased after failure experience independently of the type of error. Even though the manipulation of the design did not result in a different perception of the robot’s anthropomorphism, it still influenced the formation of trust. The results emphasize that anthropomorphism is no universal remedy to increase trust, but highly context dependent.

An adaptive framework for robotic polishing based on impedance control

Precise finishing operations such as chamfering and filleting are characterized by relatively low contact forces and low material removal. For such processes, conventional automation approaches like pre-programmed position or force control without adaptations are not suitable to obtain fine surface finishing with high profile accuracy. As a result, polishing tasks are still mainly carried out manually by skilled operators. In this paper, we propose an adaptive framework capable of polishing a wide range of materials including hard metals like titanium using a collaborative robot. We propose an iterative learning controller based on impedance control that adapts both position and forces simultaneously in each iteration to regulate the polishing process. The proposed controller can track the desired profile without any a priori knowledge of the forces required to polish different materials. In addition, we introduce a novel mathematical model to generate the complex filleting toolpath based on Lissajous curves. Trials are carried out in finishing tasks such as chamfering and filleting using a collaborative industrial robot to validate the novel framework. Surface roughness and profile measurements show that our adaptive controller can obtain fine polishing output in various materials such as titanium, aluminum, and wood.

Learning-Based Real-Time Detection of Robot Collisions Without Joint Torque Sensors

Robots operating in close proximity to humans require fast and reliable detection of collisions, which can range from sharp impacts (hard collisions) to pulling-pushing-catching motions (soft collisions). Because joint torque sensors can be costly, the external joint torques caused by collisions are typically estimated from motor current measurements at the joint actuators. For reliable detection, however, these methods require accurate models of dissipative friction at the joints. In this letter we design two learning-based detection methods – a support vector machine (SVM), and a convolutional neural network (CNN) – that require only motor current measurements together with a robot dynamics model and a momentum observer; no friction model is needed, and manual tuning of collision detection thresholds for each of the joints can be avoided. Extensive experiments with a six-dof industrial collaborative robot trained with stacks of four- and six-dimensional time series data validate our algorithms for a wide range of hard and soft collisions. The CNN-based method shows better performance if more training data is available, whereas the SVM-based method performs better with less data.

Adaptive feedforward control of a collaborative industrial robot manipulator using a novel extension of the Generalized Maxwell-Slip friction model

Collaborative industrial robots often use strain-wave transmissions which display a highly nonlinear behavior. In particular, the friction torque depend on the load torque and the hysteresis characteristics were recently found to depend on the joint angular position.This paper presents a novel extension of the Generalized Maxwell-Slip friction model to describe said phenomena in a combined framework. The method overcomes the discontinuity around zero velocity of existing models. Experiments on the Universal Robots UR5e manipulator show superior performance in terms of torque prediction accuracy and tracking performance of the proposed method.An adaptive feedforward friction compensator is proposed based on the extended Generalized Maxwell-Slip friction model to compensate the time-variations of the Coulomb and viscous friction due to, respectively, wear and mispredictions of the lubricant temperature. The adaptive estimator relies on the sensing hardware readily available in the joints of the Universal Robots manipulators, i.e. two absolute rotary encoders; one at each side of the transmission, current sensing for the electric actuator, and a temperature sensor. Results show a considerable reduction of the torque prediction error and tracking error.

Dynamics Parametrization and Calibration of Flexible-Joint Collaborative Industrial Robot Manipulators

Many collaborative robots use strain-wave-type transmissions due to their desirable characteristics of high torque capacity and low weight. However, their inherent complex and nonlinear behavior introduces significant errors and uncertainties in the robot dynamics calibration, resulting in decreased performance for motion and force control tasks and lead-through programming applications. This paper presents a new method for calibrating the dynamic model of collaborative robots. The method combines the known inverse dynamics identification model with the weighted least squares (IDIM-WLS) method for rigid robot dynamics with complex nonlinear expressions for the rotor-side dynamics to obtain increased calibration accuracy by reducing the modeling errors. The method relies on two angular position measurements per robot joint, one at each side of the strain-wave transmission, to effectively compensate the rotor inertial torques and nonlinear dynamic friction that were identified in our previous works. The calibrated dynamic model is cross-validated and its accuracy is compared to a model with parameters obtained from a CAD model. Relative improvements are in the range of 16.5% to 28.5% depending on the trajectory.

A systematic review of Augmented Reality interfaces for collaborative industrial robots

Industry 4.0 is moving factories towards a high level of collaboration between human workers and industrial robots, with the aim of improving efficiency and productivity. Among other technologies, Augmented Reality (AR) is one of the most researched in recent years to provide novel user interfaces that could easily blend the real world with additional information. This literature review aims at identifying the main strengths and weaknesses of AR with industrial robots in human–robot collaborative scenarios. The term industrial robot is meant according to the ISO 8373:2012 definition. To this end, starting from 3734 initial works, 63 papers have been selected and analysed. The results suggest that AR technology shows its effectiveness also in this particular domain. With respect to traditional approaches, AR systems are faster and more appreciated by users. Nonetheless, the use of AR in human–robot collaborative scenarios is so cutting edge that not all the considered works have properly evaluated the proposed user interfaces. Future research should improve the qualitative evaluation in order to clearly point out both limitations and strengths of the proposed systems, involving also expert users in tests.

Concensus-Based ALADIN Method to Faster the Decentralized Estimation of Laplacian Spectrum

With the upcoming fifth Industrial Revolution, humans and collaborative robots will dance together in production. They themselves act as an agent in a connected world, understood as a multi-agent system, in which the Laplacian spectrum plays an important role since it can define the connection of the complex networks as well as depict the robustness. In addition, the Laplacian spectrum can locally check the controllability and observability of a dynamic controlled network, etc. This paper presents a new method, which is based on the Augmented Lagrange based Alternating Direction Inexact Newton (ALADIN) method, to faster the convergence rate of the Laplacian Spectrum Estimation via factorization of the average consensus matrices, that are expressed as Laplacian-based matrices problems. Herein, the non-zero distinct Laplacian eigenvalues are the inverse of the stepsizes {αt,t=1,2,…} of those matrices. Therefore, the problem now is to carry out the agreement on the stepsize values for all agents in the given network while ensuring the factorization of average consensus matrices to be accomplished. Furthermore, in order to obtain the entire Laplacian spectrum, it is necessary to estimate the relevant multiplicities of these distinct eigenvalues. Consequently, a non-convex optimization problem is formed and solved using ALADIN method. The effectiveness of the proposed method is evaluated through the simulation results and the comparison with the Lagrange-based method in advance.

Comprehensive modeling and identification of nonlinear joint dynamics for collaborative industrial robot manipulators

For collaborative robots, the ability to accurately predict the actuator torques required to realize the desired task is highly important. This will improve and guarantee the safety, motion and force control performance, and smooth lead-through programming experience. Thus, this paper presents the investigation towards comprehensive modeling and identification of nonlinear joint dynamics for collaborative robots. The proposed joint dynamics model and identification describes the most dominant dynamic characteristics of robot joints that comprise strain-wave transmissions, such as nonlinear friction, nonlinear stiffness, hysteresis, and kinematic error. Position-dependent backlash characteristics is observed and quantified using our proposed identification method and the Generalized Maxwell-Slip friction model is extended to describe the observed phenomena. The developed dynamic modeling and identification procedures provides insightful guidance for the design and model-based control of collaborative robots.

Design, Modelling and Control of Novel Series-Elastic Actuators for Industrial Robots

This paper describes data-driven modelling methods and their use for the control of a novel set of series-elastic actuators (SEAs). A set of elastic actuators was developed in order to fulfill the end-user needs for tailored industrial collaborative robot manipulators of different morphologies and payloads. Three different types of elastic actuation were investigated, namely, disc springs, coil springs and torsion bars. The developed algorithms were validated both on single actuators and on a 6-DOF robotic arm composed of such actuators.

On Inferring Intentions in Shared Tasks for Industrial Collaborative Robots

Inferring human operators’ actions in shared collaborative tasks plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space, but also forces and the execution of a task. In this article, we present a robotic system that is able to identify different human’s intentions and to adapt its behavior consequently, only employing force data. In order to accomplish this aim, three major contributions are presented: (a) a force based operator’s intention recognition system based on data from only two users; (b) a force based dataset of physical human–robot interaction; and (c) validation of the whole system with 15 people in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human–robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.

A case study in human–robot collaboration in the disassembly of press-fitted components

Human–robot collaborative disassembly is an approach designed to mitigate the effects of uncertainties associated with the condition of end-of-life products returned for remanufacturing. This flexible semi-autonomous approach can also handle unpredictability in the frequency and numbers of such returns as well as variance in the remanufacturing process. This article focusses on disassembly, which is the first and arguably the most critical step in remanufacturing. The article presents a new method for disassembling press-fitted components using human–robot collaboration based on the active compliance provided by a collaborative robot. The article first introduces the concepts of human–robot collaborative disassembly and outlines the method of active compliance control. It then details a case study designed to demonstrate the proposed method. The study involved the disassembly of an automotive water pump by a collaborative industrial robot working with a human operator to take apart components that had been press-fitted together. The results show the feasibility of the proposed method.

A learning-based multiscale modelling approach to real-time serial manipulator kinematics simulation

Kinematics simulation is central to the control of serial manipulators in human machine interaction, which highly depends upon computational efficiency and accuracy for the sake of enabling real-time analysis and interaction. In this paper, a novel learning-based multiscale modelling approach is proposed to effectively address the efficiency and accuracy trade-offs through a combination of models with different levels of fidelity. Specifically, low-fidelity models are formulated using Kernel Ridge Regression (KRR) to achieve much lower computational cost as its kinematic approximation. Additionally, high-fidelity models are created based on the Long Short Term Memory (LSTM) neural network, which can calibrate fidelity by training the significant samples and eliminate position singularity of a serial manupulator. The proposed approach is evaluated both by using numerical tests and by applying it to a collaborative industrial robot arm. The experimental results obtained demonstrate that it can achieve better performance in terms of both accuracy and efficiency compared with the other popular methods.