Artificial hearts are an essential solution for patients suffering from end-stage heart failure. The precise control of these devices is critical for replicating the natural heart’s behavior and ensuring optimal patient health. This thesis presents the development and evaluation of control algorithms for a Total Artificial Heart (TAH). Our research initially considered the Proportional Integral Derivative (PID), Fuzzy-PID, and Artificial Neural Networks (ANN)-PID controllers. Through an iterative process of development and testing, two controllers emerged as the most effective: a Proportional (P) controller and a fuzzy Proportional Derivative (FPD) controller. These controllers were designed and simulated, followed by the generation of C code for implementation on an embedded system. An iterative approach was employed to design and test the controllers. First, the controllers were tested in a simulated environment, and then the validated designs were implemented and evaluated in a physical mock-loop system that mimicked the human circulatory system. The results demonstrated that both the P and FPD controllers were able to regulate the TAH operation. Notably, the FPD controller performs better based on the settling time, overshoot, and rise time in the simulation environment and stability in the physical environment. This thesis contributes to ongoing research in the field of TAH by providing a continuation that can advance the field’s development. These advancements could potentially improve the quality of life of patients awaiting heart transplants. Future work will include refining the FPD controller and conducting extensive physical testing and tuning.

Syntronic Research & Development AB both designs and performs tests of PCBs of differentkinds. At lower volumes, big parts of the test process are manual. This thesis examines oneway to automate the loading and unloading process with a cheap and simple solution so that itcan be profitable even at low volumes. If all parts of the process are automated manpower canbe freed, hence the cost of testing can be lowered, and tedious monotonous work can beavoided. Human error can also be removed from the equation, which could result in lessstochastic errors. The approach to automate the loading and unloading of test objects in this thesis is to design atwo-axis linear robot. This way the PCBs can be picked from incoming plates, placed in thetestbench, and then be placed in outgoing plates, as long as they are all in a straight line. To find weakness in the design a prototype was constructed on which tests were performed.These tests showed some areas on which improvements are needed before it can beconsidered a finished product. These improvements are discussed. The tests also showed that this approach can be made profitable, with some limitations. The cost savings are greatly dependable on how the other process are automated. There are both limitations on the setup of the test fixture used and, perhaps mainly, on how theother process are automated. This is also discussed. One challenge yet to overcome is how to make it easy to adapt and implement for a specifictest. When and if a robot based on this concept is implemented the setup time will by far be thelargest contributor to the cost.

Human Robot Collaboration (HRC) allows humans to operate more efficiently by reducing their human effort. Robots can do the majority of difficult and repetitive activities with or without human input. There is a risk of accidents and crashes when people and robots operate together closely. In this area, safety is extremely important. There are various techniques to increase worker safety, and one of the ways is to use Augmented Reality (AR). AR implementation in industries is still in its early stages. The goal of this study is to see how employees' safety may be enhanced when AR is used in an HRC setting. A literature review is carried out, as well as a case study in which managers and engineers from Swedish firms are questioned about their experiences with AR-assisted safety. This is a qualitative exploratory study with the goal of gathering extensive insight into the field, since the goal is to explore approaches for AR to improve safety. Inductive qualitative analysis was used to examine the data. Visualisation, awareness, ergonomics, and communication are the most critical areas where AR may improve safety, according to the studies. When doing a task, augmented reality aids the user in visualizing instructions and information, allowing them to complete the task more quickly and without mistakes. When working near robots, AR enhances awareness and predicts mishaps, as well as worker trust in a collaborative atmosphere. When AR is utilized to engage with collaborative robots, it causes less physical and psychological challenges than when traditional approaches are employed. AR allows operators to communicate with robots without having to touch them, as well as make adjustments. As a result, accidents are avoided and safety is ensured. There is a gap between theoretical study findings and data gathered from interviews in real time. Even though AR and HRC are not new topics, and many studies are being conducted on them, there are key aspects that influence their adoption in sectors. Due to considerations such as education, experience, suitability, system complexity, time, and technology, HRC and AR are employed less for assuring safety in industries by managers in various firms. In this study, possible future solutions to these challenges are also presented.

A Ground Penetrating Radar (GPR) is a non-invasive measurement tool to locate objects in the subsurface. The GPR transmits electromagnetic waves into the ground and records the waves reflected from surface interfaces of different materials. To accurately find these surfaces after measuring, it is important to record the precise location of the GPR and minimize reflected noise. Since a GPR cannot distinguish the direction from which the waves were reflected, this can result in a misinterpretation of the data if waves are reflected from surrounding objects. This problem can be reduced by also mapping objects in the surroundings. The work of this thesis is aimed at implementing a system that uses a Real-Time Kinematics (RTK) GNSS (Global Navigation Satellite System) receiver for precise positioning together with a 2D-LiDAR (Light Detection And Ranging) to record a 3D map of the surroundings. We used the 3D-LiDAR system to record vertical planes (cross-sections) that were processed into a 3D volume map. We found that the RTK GNSS receiver performed well and delivered the position within centimeters when provided with corrections, while it was about 2.5 m off without corrections. The performance was compared with a professional-grade Leica RTK receiver and the difference in latitude and longitude ranged from 0.001-0.002 m and 0.002-0.004 m, respectively. By fusing the RTK position with the LiDAR data using the software Robot Operating System (ROS), we created 3D maps that represented the surroundings along the traveled path. Our developed system, consisting of an RTK GNSS receiver and the 2D LiDAR, gave promising results and we are optimistic that combining the system with a GPR can improve the interpretation of the subsurface. Thus, the proposed method seems promising to be used during GPR mapping.

Underground mining machines play a critical role in the mining industry, enablingexcavation of valuable minerals from subterranean deposits. The efficiency of mining op-erations relies heavily on the effectiveness of these machines. Path and trajectory opti-mization techniques are important for improving the effectiveness by reducing the timeand resources required to excavate minerals while ensuring the safety of miners.This thesis explores the application of Model Predictive Control (MPC) in optimizingLoad-Haul-Dump (LHD) routes in underground mining operations. The research ques-tions focus on the utilization of MPC, considerations of dynamic vehicle behaviors, in-tegration of constraints and comparison of optimized routes in terms of cycle times andoverall mining process efficiency.The thesis adopts a two-controller approach, comprising longitudinal and lateral con-trollers, to effectively control the steering angle and optimize the vehicles path. The er-ror dynamics model accurately describes the vehicles position and orientation, enablingprecise route planning and execution. By developing and implementing an MPC-basedalgorithm, the routes are optimized, resulting in improvements in travel time.Dynamic vehicle behaviors, including position, orientation, longitudinal speed andsteering rate, are considered through the kinematic model of the articulated vehicle. Thisensures accurate representation and control of the vehicles movements.Constraints on the admissible state and control input, such as speed limitations, accel-eration limitations and steering angle limitations, are integrated into the MPC-based pathplanning algorithm. Collision avoidance with mine walls is also addressed through sim-ulation in MATLAB, incorporating the mine map. This analysis ensures the safety of thevehicles trajectory and highlights the importance of balancing optimization objectives withoperational constraints and safety requirements.Comparing the optimized LHD routes generated by the MPC-based algorithm revealsdifferences in travel time, with reductions ranging from 1-3 seconds. However, the specificmine conditions and constraints considered in this study may limit significant improve-ments in travel time.The findings of this research have implications for the mining industry, researchersand practitioners in autonomous vehicle control and optimization. The proposed methodenhances the efficiency and productivity of underground mining operations.Future work could explore alternative optimization approaches, refine the existingMPC-based algorithm and consider a more comprehensive vehicle dynamics model. Addi-tionally, investigating different mine environments and incorporating realistic sensor data,uncertainties and noise would improve the reliability and applicability of the method inreal-world mining scenarios.In conclusion, this study contributes to the understanding of applying MPC in LHDroute optimization, paving the way for further research. The utilization of a two-controllerapproach, error dynamics model and careful consideration of weighting parameters en-ables more effective route planning and execution. Continued advancements in au-tonomous vehicle control and optimization in the mining industry will lead to increasedproductivity, efficiency and indirectly safety in underground mining operations.

This thesis presents the research and development behind the integration of an autonomous propulsion system for a four-wheeled Ground Penetrating Radar (GPR) measurement unit, previously requiring manual operation. In order to ease the administration of the complex new system, Robot Operating System (ROS) 2 was used as middleware, where an implementation of Light Detection And Ranging (LiDAR) 3D-SLAM (Simultaneous Localization And Mapping) served to secure precise localization of the Unmanned Ground Vehicle (UGV) and mapping of its environment. This, with the ultimate goal of enabling accurate survey execution along paths optimized for various dynamic indoor- and outdoor environments. From a more general point of view, this work can also act as a hardware- and software selection guide for similar projects, especially if stricter physical limitations apply and the autonomous system is not considered the primary system, but the majority of the internal enclosed space of the UGV is reserved for higher purpose equipment or storage requirements. In this first prototype iteration, the mapping accuracy of the autonomous system reached centimeter precision and the execution of surveys in grid- and spiral patterns yielded position accuracies of 5(2) cm and 6(4) cm, respectively. These results are indeed very promising and show the proof of concept needed to enter the next development phase.

Environment perception within autonomous driving aims to provide a comprehensive and accurate model of the surrounding environment based on information from sensors. For the model to be comprehensive it must provide the kinematic state of surrounding objects. The existing approaches of object detection and tracking (estimation of kinematic state) are developed for dense 3D LiDAR data from a sensor mounted on a car. However, it is a challenge to design a robust detection and tracking algorithm for sparse 3D LiDAR data. Therefore, in this thesis we propose a framework for detection and tracking of unknown objects using sparse VLP-16 LiDAR data which is mounted on a heavy duty vehicle. Experiments reveal that the proposed framework performs well detecting trucks, buses, cars, pedestrians and even smaller objects of a size bigger than 61x41x40 cm. The detection distance range depends on the size of an object such that large objects (trucks and buses) are detected within 25 m while cars and pedestrians within 18 m and 15 m correspondingly. The overall multiple objecttracking accuracy of the framework is 79%. / Miljöperception inom autonom körning syftar till att ge en heltäckande och korrekt modell av den omgivande miljön baserat på information från sensorer. För att modellen ska vara heltäckande måste den ge information om tillstånden hos omgivande objekt. Den befintliga metoden för objektidentifiering och spårning (uppskattning av kinematiskt tillstånd) utvecklas för täta 3D-LIDAR-data från en sensor monterad på en bil. Det är dock en utmaning att designa en robust detektions och spårningsalgoritm för glesa 3D-LIDAR-data. Därför föreslår vi ett ramverk för upptäckt och spårning av okända objekt med hjälp av gles VLP-16-LIDAR-data som är monterat på ett tungt fordon. Experiment visar att det föreslagna ramverket upptäcker lastbilar, bussar, bilar, fotgängare och även mindre objekt om de är större än 61x41x40 cm. Detekteringsavståndet varierar beroende på storleken på ett objekt så att stora objekt (lastbilar och bussar) detekteras inom 25 m medan bilar och fotgängare detekteras inom 18 m respektive 15 m på motsvarande sätt. Ramverkets totala precision för objektspårning är 79%.

Today automotive companies across the world strive to create vehicles with fully autonomous capabilities. There are many benefits of developing autonomous vehicles, such as reduced traffic congestion, increased safety and reduced pollution, etc. To be able to achieve that goal there are many challenges ahead, one of them is visual perception. Being able to estimate depth from a 2D image has been shown to be a key component for 3D recognition, reconstruction and segmentation. Being able to estimate depth in an image from a monocular camera is an ill-posed problem since there is ambiguity between the mapping from colour intensity and depth value. Depth estimation from stereo images has come far compared to monocular depth estimation and was initially what depth estimation relied on. However, being able to exploit monocular cues is necessary for scenarios when stereo depth estimation is not possible. We have presented a novel CNN network, BiNet which is inspired by ENet, to tackle depth estimation of moving objects using only a monocular camera in real-time. It performs better than ENet in the Cityscapes dataset while adding only a small overhead to the complexity. / I dag strävar bilföretag över hela världen för att skapa fordon med helt autonoma möjligheter. Det finns många fördelar med att utveckla autonoma fordon, såsom minskad trafikstockning, ökad säkerhet och minskad förorening, etc. För att kunna uppnå det målet finns det många utmaningar framåt, en av dem är visuell uppfattning. Att kunna uppskatta djupet från en 2D-bild har visat sig vara en nyckelkomponent för 3D-igenkännande, rekonstruktion och segmentering. Att kunna uppskatta djupet i en bild från en monokulär kamera är ett svårt problem eftersom det finns tvetydighet mellan kartläggningen från färgintensitet och djupvärde. Djupestimering från stereobilder har kommit långt jämfört med monokulär djupestimering och var ursprungligen den metod som man har förlitat sig på. Att kunna utnyttja monokulära bilder är dock nödvändig för scenarier när stereodjupuppskattning inte är möjligt. Vi har presenterat ett nytt nätverk, BiNet som är inspirerat av ENet, för att ta itu med djupestimering av rörliga objekt med endast en monokulär kamera i realtid. Det fungerar bättre än ENet med datasetet Cityscapes och lägger bara till en liten kostnad på komplexiteten.

Design of experiment (DOE) is a statistical method for testing effects of input factors into a process based on its responses or outputs. Since the influence of these factors and their interactions are studied from the process outputs, then quality of these outputs or the measurements play a significant role in a correct statistical conclusion about the significance of factors and their interactions. Linear regression is a method, which can be applied for the DOE purpose, the parameters of such a regression model are estimated by the ordinary least-squares (OLS) method. This method is sensitive to the presence of any blunder in measurements, meaning that blunders significantly affect the result of a regression using OLS method. This research aims to perform a robustness analysis for some full factorial DOEs by different robust estimators as well as the Taguchi methodology. A full factorial DOE with three factors at three levels, two replicants, and three replicants are performed is studied. Taguchi's approach is conducted by computing the signal-to-noise ratio (S/N) from three replicants, where the lower noise factor means the stronger signal. Robust estimators of Andrews, Cauchy, Fair, Huber, Logistic, Talwar, and Welsch are applied to the DOE in different setups and adding different types and percentages of blunders or gross errors to the data to assess the success rate of each. Number and size of the blunders in the measurements are two important factors influencing the success rate of a robust estimator. For evaluation, our measurements are infected by blunders up to different percentages of data. Our study showed the Talwar robust estimator is the best amongst the rest of estimators and resists well against up to 80% of presence of blunders. Consequently, the use of this estimator instated of the OLS is recommended for DOE purposes. The comparison between Taguchi’s method and robust estimators showed that blunders affect the signal-to-noise ratio as the signal is significantly changed by them, whilst robust estimators suppress the blunders well and the same conclusion as that with the OLS with no blunder can be drawn from them.

The purpose of this thesis is to investigate the possibilities of automating a gluing process in the optical manufacturing industry. Aimpoint, a Swedish company that manufactures red-dot sights was the industrial partner in this thesis. Since the optical quality of their products is of the highest importance and the lenses and protective glass are for the most part glued using traditional, manual methods, the company aims to find improvements and methods to ensure the qualityof their products by automating this process. A number of requests were submitted, including the ability of such an improved process to be able to glue lenses of more complex geometries than the mostly round lenses currently used. To allow precise adhesive dispensing around the lens of a sight with a complex geometry, Aimpoint has two Cartesian tabletop dispensing robots. This kind of robot servesas a baseline for the comparisons in this thesis. A Franka Emika articulating robot arm was used to test the possibilities of au-tomating the process using such a robot system. Firstly, a motion was generatedbased on the CAD model of the product. The motion generation and manipulation was performed in MATLAB where the Peter Corke robotics toolbox was used for initial simulations. Unfortunately, the robot model in the toolbox was not able to take joint limitations into account and a migration to ROS was in order. Since there was no implementation for Cartesian control in the simulation software for Franka Emika, a decision was made to start physical tests using the robot pre-maturely. As for the adhesive dispensing, tests were conducted to investigate the parameters that affect the termination of a glue joint such as the retract setup and timings. A repeatability test was also conducted to test the performance of dis-pensing using a direct pressure fed adhesives syringe versus using a dispensingvalve. With the knowledge and data from these tests, the experimental setup was built at the robot lab in Lund where a computer ran the controller implementation, controlling the robot and an Arduino was utilized to actuate the dispenser. Several details were glued on this setup, both with vertical and angled nozzle. Asa reference for gluing, a detail was glued using the Cartesian dispensing robotat Aimpoint with the same limited path as previously described. To analyse the performance of the glued joint, a push-out test was conducted at Aimpoint usinga tensile strength machine. The results from this test showed that a glue joint glued with an angled nozzle was stronger and a higher force was required to deform the joint compared to one glued with a vertical nozzle. On average, the joint glued with an angled nozzle required 721 N and the vertical required 395 N. It is concluded that keeping the nozzle angled during the dispensing operation yield a stronger glue joint and makes the system less sensitive to positionand orientation errors. As for the impedance controller used in this thesis, it is concluded to not be suitable for the considered application and its requirements. Regarding the robotic solution investigated in this thesis, it was observed to bebeneficial to utilize a high number of axes when generating the motion for the gluing operation. However, case-consistent inverse kinematics is a requirementto further investigate this claim.