With the simulations tools available today, simulation can be utilised as a platform for more advanced software testing. By introducing simulations to software testing of robot controllers, the motion performance testing phase can begin at an earlier stage of development. This would benefit all parties involved with the robot controller. Testers at ABB would be able to include more motion performance tests to the regression tests. Also, ABB could save money by adapting to simulated robot tests and customers would be provided with more reliable software updates. In this thesis, a method is developed utilising simulations to create a test set for detecting motion anomalies in new robot controller versions. With auto-generated test cases and a similarity analysis that calculates the Hausdorff distance for a test case executed on controller versions with an induced artificial bug. A test set has been created with the ability to detect anomalies in a robot controller with a bug.

This diploma thesis deals with the design and virtual commissioning of a robotic workplace designed for glueing and assembling a car headlight. The thesis contains a summary of available information on the matter of virtual commissioning and industrial robotics. It also contains a system analysis of the necessary equipment, followed by a design of several workplace variants. The final solution for which the 3D model is made is selected from them. With it, in the RobotStudio software, a simulation of all processes taking place at the workplace is created. On its base, a control program is created and debugged, and virtual commissioning of the workplace is performed.

This report studies how a digital twin can be utilized through offlinerobot programming and simulation for a robot packaging line;additionally, the advantages and challenges of a robot digital twin arereported. This thesis project and the robot simulation is done incollaboration with Ikea Industry.The obtained result was in form of a digital twin which is a digital copyof a physical robot cell at Ikea Industry’s packaging line. The resultsshow that a digital twin can indeed be utilized for layout planning androbot optimization. An energy consumption chart which depends on thetime taken to package ten boards was created. This chart can be used forfurther optimization of the robot cell.The study also shows that a digital twin can save time and money,especially in the design phase, for medium to small companies that donot have the resources to create a dedicated physical robot cell fortesting.

Detta projekt åsyftade att möjliggöra för en industrirobot att illustrera godtyckliga digitalabilder på en plan yta. Detta uppnåddes genom att utrusta en manipulator med ett ritverktyg. Genom digital bildbehandling så kunde rörelsemönster genereras vilka matades till industriroboten för att den skulle kunna återskapa den digitala versionen. Roboten ritar med en teknik benämnd pointillism som innebär att endast punkter plottas. Resultatet blir en konkret svartvit representation av originalbilden. Projektet genomfördes på institutionen Industriell Produktion på Kungliga Tekniska Högskolan. Projektet är i sin natur väldigt inriktat på forskning och utveckling eftersom det går ut på skapandet av en teknik för att uppnå ett tydligt mål. Kontinuerlig utveckling var kopplat till målet för att förbättra resultatet från olika aspekter. / This project strived to enable an industrial robot to illustrate arbitrary digitized images on a planar surface. This was accomplished by equipping a robotic manipulator with a drawing utensil. Motion patterns were generated based on digital image processing and fed to the robot for it to imitate the digital version. The robot prints with a technique called pointillism, which implies that solely points are plotted. The result is a tangible black and white representation of the original image. The project was carried out in the Production Engineering facilities at the Royal Institute of Technology. The nature of the project is very research and development oriented as it deals with the creation of a technology to achieve an explicit goal. Continuous development was related to the goal to improve the result from different aspects.

During the years 2004-2014, the manufacturing sector within the EU countries witnessed an increase in the utilization of industrial robots, where robot density per worker approximately doubled. Considering that this is a rather recent event, studies investigating how much industrial robots impact labour productivity are still rare. At the same time, one of EU’s outspoken goals is that of working to foster productivity and economic convergence between the member states. Given the above premises, we have investigated the relation between the adoption rate of industrial robotics within the EU and its effect on labour productivity. Secondly, we have made a predictive convergence model, in terms of labour productivity. We have collected data from several sources, including the Industrial Federation of Robotics and EU KLMS, in order to build a dataset for our quantitative analysis. We have then used statistical methods such as multiple regressions and 3 stage least square analysis (3sls) to estimate our system of interdependent equations model. The results show that implementation of industrial robotics in the manufacturing sector is a driver of labour productivity. The model finally predicts upward labour productivity divergence between the member states in the years 2015-2025, assuming that the determining factors of labour productivity grow at the same pace in our forecast period as in our data sample. / Under åren 2004-2014 fördubblades i genomsnitt antalet industrirobotar per arbetare i tillverkningsindustrin inom EU. Eftersom detta fenomen är relativt nytt, är studier som undersöker industrirobotars påverkan på arbetarproduktivitet fortfarande sällsynta. Samtidigt är ett av EU:s uttalade mål att arbeta för att främja konvergens mellan medlemsländerna inom produktivitet och andra ekonomiska mått. Med ovanstående förutsättningar har vi undersökt förhållandet mellan ökad användning av industriell robotik inom EU och dess effekt på arbetskraftsproduktiviteten. För att bygga en model för vår kvantitativa analys har vi samlat in data från flera källor, inklusive Industrial Federation of Robotics och EU KLMS. Vi har sedan använt statistiska metoder såsom multipel regression och 3-stegs minsta kvadratanalys (3sls) för att estimera vårt system av ekvationer. Resultaten visar att ökad användning av industriell robotik i tillverkningssektorn driver ökad arbetskraftsproduktivitet. Därefter analyserar vi även den aktuella konvergensriktningen för arbetarproduktivitet, och vår modell förutspår uppåtgående arbetsproduktivitetsdivergens, under förutsättning att de ingående faktorerna för arbetskraftsproduktivitet växer i samma takt under vår prognosperiod som under dataunderlagsperioden. / Durante gli anni che vanno dal 2004 al 2014 il settore manifatturiero degli stati appartenenti all’Unione europea è stato testimone di un aumento dell’utilizzo dei robot industriali: la densità di robot utilizzati per ciascun lavoratore è raddoppiata. Considerato che questo è un fenomeno abbastanza recente, gli studi che investigano quanto i robot industriali influiscono sulla produttività lavorativa sono ancora rari. Allo stesso tempo, uno degli obiettivi dichiarati dall’Unione europea è quello di stimolare la convergenza economica tra gli stati membri. Date queste premesse, abbiamo studiato la relazione tra il tasso di adozione dei robot industrali nell’Unione europea e il suo effetto sulla produttività del lavoro. Inoltre, abbiamo sviluppato un modello di previsione della convergenza in termini di produttività lavorativa. Abbiamo raccolto i dati da diverse fonti, tra cui la federazione industriale della robotica ed EU KLEMS, in modo da costruire un dataset per la nostra analisi quantitativa. In seguito abbiamo usato dei metodi statistici come la regressione multipla e la l’analisi dei minimi quadrati a tre stadi (3sls) per testare il nostro sistema di equazioni indipendenti. I risultati mostrano che l’implementazione dei robot industriali nel settore manifatturiero è un elemento motore della produttività lavorativa. Infine, il modello prevede una divergenza della produttività tra i Paesi membri negli anni 2015-2025, assumendo che i fattori determinanti della produttività crescano allo stesso modo nel periodo della previsione rispetto al periodo del nostro campione.

The interest in breaking the historical dependence on fossil energy and begin moving towards more renewable energy sources is rising worldwide. This is largely due to uncertainties in the future supply of fossil fuels and the rising concerns about humanity’s role in the currently ongoing climate changes. One renewable energy source is ocean waves and Uppsala University has since the early 2000s been performing active research in this area. The Uppsala wave energy concept is centered on developing linear generators coupled to point absorbing buoys, with the generator situated on the seabed and connected to the buoy on the sea surface via a steel wire. The motion of the buoy then transfers energy to the generator, where it is converted into electricity and sent to shore for delivery into the electrical grid. This thesis will mainly focus on the development and evaluation of technologies used to automate the manufacturing of the translator, a central part of the linear generator, using industrial robotics. The translator is a 3 m high and 0.8 m wide three sided structure with an aluminum pipe at its center. The structure consists of alternating layers of steel plates (pole-shoes) and ferrite magnets, with a total of 72 layers per side. To perform experiments on translator assembly and production, a robot cell (centered on an IRB6650S industrial robot) complimented with relevant tools, equipment and security measures, has been designed and constructed. The mounting of the pole-shoes on the central pipe, using the industrial robot, proved to be the most challenging task to solve. However, by implementing a precise work-piece orientation calibration system, combined with selective compliance robot tools, the task could be performed with mounting speeds of up to 50 mm/s. Although progress has been made, much work still remains before fully automated translator assembly is a reality. A secondary topic of this thesis is the development of stand-alone measurement systems to be used in the linear generator, once it has been deployed on the seabed. The main requirements of such a measurement system is robustness, resistance to electrical noise, and power efficiency. If possible the system should also be portable and easy to use. This was solved by developing a custom measurement circuit, based on industry standard 4–20 mA current signals, combined with a portable submersible logging unit. The latest iteration of the system is small enough to be deployed and retrieved by one person, and can collect data for 10 weeks before running out of batteries. Future work in this area should focus on increasing the usability of the system. The third and final topic of this thesis is a short discussion of an engineering approach to kinetic energy storage, in the form of high-speed composite flywheels, and the design of two different prototypes of such flywheels. Both designs gave important insights to the research group, but a few crucial design faults unfortunately made it impossible to evaluate the full potential of the two designs.

This study presents a procedure for the estimation of stopping behavior of industrial robots with a trained neural network. This trained network is presented as a single channel in a redundant architecture for safety control applications, where its potential for future integration with an analytical model of robot stopping is discussed. Basic physical relations for simplified articulated manipulators are derived, which motivate a choice of quantities to predict robot stopping behavior and inform the training and testing of a network for prediction of stopping distances and times. Robot stopping behavior is considered in the context of relevant standards ISO 10218-1, ISO/TS 15066 and IS0 13849-1, which inform the definitions for safety related stopping distances and times used in this study. Prior work on the estimation of robot stopping behavior is discussed alongside applications of machine learning to the broader field of industrial robotics, and particularly to the cases of prediction of forward and inverse kinematics with trained networks. A state-driven data collection program is developed to perform repeated stopping experiments for a controlled stop on path within a specified sampling domain. This program is used to collect data for a simulated and real robot system. Special attention is given to the identification of meaningful stopping times, which includes the separation of stopping into pre-deceleration and post-deceleration phases. A definition is provided for stopping of a robot in a safety context, based on the observation that residual motion over short distances (less than 1 mm) and at very low velocities (less than 1 mm/s) is not relevant to robot safety. A network architecture and hyperparameters are developed for the prediction of stopping distances and times for the first three joints of the manipulator without the inclusion of payloads. The result is a dual-network structure, where stopping distance predictions from the distance prediction network serve as inputs to the stopping time prediction network. The networks are validated on their capacity to interpolate and extrapolate predictions of robot stopping behavior in the presence of initial conditions not included in the training and testing data. A method is devised for the calculation of prediction errors for training training, testing and validation data. This method is applied both to interpolation and extrapolation to new initial velocity and positional conditions of the manipulator. In prediction of stopping distances and times, the network is highly successful at interpolation, resulting in comparable or nominally higher errors for the validation data set when compared to the errors for training and testing data. In extrapolation to new initial velocity and positional conditions, notably higher errors in the validation data predictions are observed for the networks considered. Future work in the areas of predictions of stopping behavior with payloads and tooling, further applications to collaborative robotics, analytical models of stopping behavior, inclusion of additional stopping functions, use of explainable AI methods and physics-informed networks are discussed. / Master of Science / As the uses for industrial robots continue to grow and expand, so do the need for robust safety measures to avoid, control, or limit the risks posed to human operators and collaborators. This is exemplified by Isaac Asimov's famous first law of robotics - "A robot may not injure a human being, or, through inaction, allow a human being to come to harm." As applications for industrial robots continue to expand, it is beneficial for robots and human operators to collaborate in work environments without fences. In order to ethically implement such increasingly complex and collaborative industrial robotic systems, the ability to limit robot motion with safety functions in a predictable and reliable way (as outlined by international standards) is paramount. In the event of either a technical failure (due to malfunction of sensors or mechanical hardware) or change in environmental conditions, it is important to be able to stop an industrial robot from any position in a safe and controlled manner. This requires real-time knowledge of the stopping distance and time for the manipulator. To understand stopping distances and times reliability, multiple independent methods can be used and compared to predict stopping behavior. The use of machine learning methods is of particular interest in this context due to their speed of processing and the potential for basis on real recorded data. In this study, we will attempt to evaluate the efficacy of machine learning algorithms to predict stopping behavior and assess their potential for implementation alongside analytical models. A reliable, multi-method approach for estimating stopping distances and times could also enable further methods for safety in collaborative robotics such as Speed and Separation Monitoring (SSM), which monitors both human and robot positions to ensure that a safe stop is always possible. A program for testing and recording the stopping distances and times for the robot is developed. As stopping behavior varies based on the positions and speeds of the robot at the time of stopping, a variety of these criteria are tested with the robot stopping program. This data is then used to train an artificial neural network, a machine learning method that mimics the structure of human and animal brains to learn relationships between data inputs and outputs. This network is used to predict both the stopping distance and time of the robot. The network is shown to produce reasonable predictions, especially for positions and speeds that are intermediate to those used to train the network. Future improvements are suggested and a method is suggested for use of stopping distance and time quantities in robot safety applications.