This report describes the development of an application which allows a user to program a robot in a virtual environment by the use of hand motions and gestures. The application is inspired by the use of robot lead-through programming which is an easy and hands-on approach for programming robots, but instead of performing it online which creates loss in productivity the strength from offline programming where the user operates in a virtual environment is used as well. Thus, this is a method which saves on the economy and prevents contamination of the environment. To convey hand gesture information into the application which will be implemented for RobotStudio, a Kinect sensor is used for entering the data into the virtual environment. Similar work has been performed before where, by using hand movements, a physical robot’s movement can be manipulated, but for virtual robots not so much. The results could simplify the process of programming robots and supports the work towards Human-Robot Collaboration as it allows people to interact and communicate with robots, a major focus of this work. The application was developed in the programming language C# and has two different functions that interact with each other, one for the Kinect and its tracking and the other for installing the application in RobotStudio and implementing the calculated data into the robot. The Kinect’s functionality is utilized through three simple hand gestures to jog and create targets for the robot: open, closed and “lasso”. A prototype of this application was completed which through motions allowed the user to teach a virtual robot desired tasks by moving it to different positions and saving them by doing hand gestures. The prototype could be applied to both one-armed robots as well as to a two-armed robot such as ABB’s YuMi. The robot's orientation while running was too complicated to be developed and implemented in time and became the application's main bottleneck, but remained as one of several other suggestions for further work in this project.

Eurofins is one of the world's largest laboratories which, among other things, offer chemical and microbiological analyses in agriculture, food and environment. Several 100.000 tests of various foods are executed each year at Eurofins’ facility in Jönköping and the current processes include much repeated manual tasks which could cause ergonomic problems. The company therefore wants to investigate the possibilities of utilizing Human-Robot Collaboration (HRC) at their facility. Human-Robot Collaboration is a growing concept that has made a big impression in both robot development and Industry 4.0. A HRC approach allow humans and robots to share their workspaces and work side by side, without being separated by a protective fence which is common among traditional industrial robots. Human-Robot Collaboration is therefore believed to be able to optimize the workflows and relieve human workers from unergonomic tasks. The overall aim of the research project presented is to help the company to gain a better understanding about the existing HRC technologies. To achieve this goal, the state-of-the-art of HRC had to be investigated and the needs, possibilities and limitations of HRC applications had to be identified at Eurofins’ facility. Once these have been addressed, a demonstrator could be built which could be used for evaluating the applicability and suitability of HRC at Eurofins. The research project presented used the design science research process. The state-of-the-art of HRC was studied in a comprehensive literature review, reviewing sterile robots and mobile robotics as well. The presented literature review could identify possible research gaps in both HRC in laboratory environments and mobile solutions for HRC applications. These areas studied in the literature review formed together the basis of the prepared observations and interviews, used to generate the necessary data to develop the design science research artefact, the demonstrator. ABB's software for robotic simulation and offline programming, RobotStudio, were used in the development of the demonstrator, with the collaborative robot YuMi chosen for the HRC implementation. The demonstrator presented in the research project has been built, tested and refined in accordance to the design science research process. When the demonstrator could illustrate an applicable solution, it was evaluated for its performance and quality using a mixed methods approach. Limitations were identified in both the performance and quality of the demonstrator's illustrated HRC implementation, including adaptability and sterility constraints. The research project presented could conclude that a HRC application would be possible at a station which were of interest by the company, but would however not be recommended due to the identified constraints. Instead, the company were recommended to look for stations which are more standardized and have less hygienic requirements. By the end of the research project, additional knowledge was contributed to the company, including how HRC can affect today's working methods at Eurofins and in laboratory environments in general.

Conventional medical imaging technologies for cancer diagnosis utilize fixed geometric configuration of the source and the detector to image the target. In this dissertation, we hypothesize that dynamic utilization of source and detector geometry will lead to better performance of medical imaging devices. Interrogating a target in a three dimensional space requires cooperation and coordination between the source and detector positions. The goal of this dissertation is to develop a dynamic imaging method, which will improve the tumor characterization performance, and provide a control scheme appropriate for the dynamic interrogation. This dissertation proposes a bimodal dynamic imaging (BDI) method for improving tumor characterization and a hybrid hierarchical statistical control scheme for the autonomous control of the sources and detectors. The tactile imaging sensor has high specificity but low sensitivity in tumor characterization. The spectral sensor has high sensitivity but low specificity. The BDI system integrates the tactile sensing and the spectral sensing modalities with the capability of dynamic positioning of the source and detector to determine the mechanical and spectral properties of a tumor. The tactile sensing can estimate the mechanical properties of the tumor, such as size, depth, and elastic modulus, while the spectral sensing can determine the absorption coefficient of the tumor through diffuse optical imaging. These properties help us characterize the tumor, and differentiate cancerous tissues from healthy tissues. We designed and experimentally evaluated the BDI system for estimating the size, depth, elastic modulus, and absorption coefficient of embedded inclusions. The system performance in characterizing mechanical properties was then compared to that of the tactile imaging sensor. The proposed BDI method was experimentally validated using fabricated bimodal phantom. The experimental results showed that the tactile imaging system (TIS) estimated the tumor phantom size with 7.23% error; BDI measured the size with 0.8% error. The TIS depth estimation error was 41.83%; BDI reduced the depth measurement error to 20.00%. The TIS elastic modulus estimation error was 96.80%; the BDI method showed 74.79% error. Additionally, BDI estimated the absorption coefficient with 14%-25% estimation error. For further improvement the system performance, this bimodal imaging system is implemented on a dual-arm robot, Baxter, where the laser source and the tactile imaging sensors were mounted on the end-effectors. Each arm of Baxter robot has seven Degree-of- Freedom. This provides more flexibility in terms of interrogating the target compared to the fixed geometric configuration. We devised a hybrid statistical controller for maneuvering the source and the detector of the system. In this control architecture, a high-level supervisory controller was used for the functions at a higher level for coordinating two arms. At lower level, a full-state feedback statistical controller was used to facilitate the minimum position variation. A linear model for the dual-arm Baxter robot was derived for testing the proposed architecture. We performed the simulations of hybrid hierarchical statistical controller on the Baxter model for trajectory tracking. The simulation studies demonstrated accurate sequential task execution for the bimodal dynamic imaging system using a hybrid hierarchical statistical control. / Electrical and Computer Engineering

Nowadays, assembly robots are increasingly used in the manufacturing industry to replace or collaborate with human labors. This is the goal of the dual arm assembly robot developed by ABB. With the rapid upgrading in consumer electronics products, the lifetime of an assembly line could be only a few months. However, even for experienced programmers, to manually construct a good enough assembly sequence is time consuming, and the quality of the generated assembly sequence is not guaranteed. Moreover, a good robot assembly sequence is important to the throughput of an assembly line. For dual arm robots, it is also important to obtain a balance between the two arms, as well as handling scheduling conflicts and avoiding collisions in a crowded environment. In this master thesis, a program is produced to automatically generate the optimal assembly sequence for a class of real-world assembly cases. The solution also takes the layout of the assembly cell into account, thus constructing the best combination of cell layout, workload balancing, task sequence and task scheduling. The program is implemented using Google OR-Tools – an open-source support library for combinatorial optimization. A customized search strategy is proposed and a comparison between this strategy and the built-in search strategy of Google OR-Tools is done. The result shows that the used approach is effective for the problem study case. It takes about 4 minutes to find the optimal solution and 32 minutes to prove its optimality. In addition, the result also shows that the customized search strategy works consistently with good performance for different problem cases. Moreover, the customized strategy is more efficient than built-in search strategy in many cases. / Numera används monteringsrobotar alltmer inom tillverkningsindustrin för att ersätta eller samarbeta med människor. Detta är måluppgiften för den tvåarmiga monteringsroboten, YuMi, som utvecklats av ABB. Med den korta produktlivslängden för hemelektronikprodukter kan livslängden för en monteringslinje vara ett fåtal månader. Även för erfarna robotprogrammerare är det svårt och tidsödande att manuellt konstruera en tillräckligt bra monteringsordning, och dessutom kan resultatets kvalitet inte garanteras. En bra monteringsordning är nödvändig för genomströmningen i en monteringslinje. För tvåarmiga robotar, är det också viktigt att få en balans mellan de två armarna, samt hantering av schemakrockar och undvika kollisioner i en trång miljö. I detta examensarbete har ett program skrivits, som automatiskt genererar optimala lösningar för en klass av verkliga monteringsfall. Lösningen tar hänsyn till utformningen av monteringscellen och arrangerar cellen på bästa sätt, balanserar arbetsbelastningen, ordnar och tidsbestämmer uppgifter. Programmet använder sig av Google OR-Tools – ett öppet kodbibliotek för kombinatorisk optimering. Dessutom föreslås en skräddarsydd sökstrategi, som jämförs med Google OR-Tools inbyggda sökstrategi. Resultatet visar att den använda metoden är effektiv för problemtypen. Det tar ungefär 4 minuter att hitta den optimala lösningen och 32 minuter för att bevisa optimalitet. Dessutom visar resultatet att den anpassade sökstrategin konsekvent har en bra prestanda för olika problemfall. Dessutom är den anpassade strategin effektivare än den inbyggda sökstrategin i många fall.