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Navigation and orientation in an indoor environment are a challenging task for visually impaired people. This paper proposes a portable vision-based system to provide support for visually impaired persons in their daily activities. Here, machine learning algorithms are used for obstacle avoidance and object recognition. The system is intended to be used independently, easily and comfortably without taking human help. The system assists in obstacle avoidance using cameras and gives voice message feedback by using a pre-trained YOLO Neural Network for object recognition. In other parts of the system, a floor plane estimation algorithm is proposed for obstacle avoidance and fuzzy logic is used to prioritize the detected objects in a frame and generate alert to the user about possible risks. The system is implemented using the Robot Operating System (ROS) for communication on a Nvidia Jetson TX2 with a ZED stereo camera for depth calculations and headphones for user feedback, with the capability to accommodate different setup of hardware components. The parts of the system give varying results when evaluated and thus in future a large-scale evaluation is needed to implement the system and get it as a commercialized product in this area.

Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

This research aims to develop an autonomous social robot for elderly individuals. The robot will learn from the interaction and change its behaviors in order to enhance the interaction and improve the user experience. For this purpose, we aim to use Deep Reinforcement Learning. The robot will observe the user’s verbal and nonverbal social cues by using its camera and microphone, the reward will be positive valence and engagement of the user.

Over the past few decades the use of industrial robots has increased the efficiency as well as competitiveness of many companies. Despite this fact, in many cases, robot automation investments are considered to be technically challenging. In addition, for most small and medium sized enterprises (SME) this process is associated with high costs. Due to their continuously changing product lines, reprogramming costs are likely to exceed installation costs by a large margin. Furthermore, traditional programming methods for industrial robots are too complex for an inexperienced robot programmer, thus assistance from a robot programming expert is often needed.  We hypothesize that in order to make industrial robots more common within the SME sector, the robots should be reprogrammable by technicians or manufacturing engineers rather than robot programming experts. In this thesis we propose a high-level natural language framework for interacting with industrial robots through an instructional programming environment for the user.  The ultimate goal of this thesis is to bring robot programming to a stage where it is as easy as working together with a colleague.In this thesis we mainly address two issues. The first issue is to make interaction with a robot easier and more natural through a multimodal framework. The proposed language architecture makes it possible to manipulate, pick or place objects in a scene through high level commands. Interaction with simple voice commands and gestures enables the manufacturing engineer to focus on the task itself, rather than programming issues of the robot. This approach shifts the focus of industrial robot programming from the coordinate based programming paradigm, which currently dominates the field, to an object based programming scheme.The second issue addressed is a general framework for implementing multimodal interfaces. There have been numerous efforts to implement multimodal interfaces for computers and robots, but there is no general standard framework for developing them. The general framework proposed in this thesis is designed to perform natural language understanding, multimodal integration and semantic analysis with an incremental pipeline and includes a novel multimodal grammar language, which is used for multimodal presentation and semantic meaning generation.

This paper proposes a novel system for task-level programming of industrial robots. The user interacts with an industrial robot by giving instructions in a structured natural language and by selecting objects through an augmented reality interface. The proposed system consists of two parts. First, a multimodal framework that provides a natural language interface to the user. This framework performs modality fusion, semantic analysis and helps the user to interact with the system easier and more naturally. The proposed language architecture makes it possible to manipulate, pick or place objects in a scene through high-level commands. The second component is the POPStar planner, which is based on partial order planner (POP), that takes landmarks extracted from user instructions as input, and creates a sequence of actions to operate the robotic cell with minimal makespan. The proposed planner takes advantage of partial order capabilities of POP to plan execution of actions in parallel and employs a best-first search algorithm to seek a series of actions that lead to a minimal makespan. The proposed planner can as well handle robots with multiple grippers, and  parallel machines. Using different topologies for the landmark graphs, we show that it is possible to create schedules for changing object types, which are processed in different stages in the robot cell. Results show that the proposed system can create and adapt schedules for robot cells with changing product types in low volume production based on the user's instructions.

In this paper, scheduling of robot cells that produce multiple object types in low volumes are considered. The challenge is to maximize the number of objects produced in a given time window as well as to adopt the  schedule for changing object types. Proposed algorithm, POPStar, is based on a partial order planner which is guided by best-first search algorithm and landmarks. The best-first search, uses heuristics to help the planner to create complete plans while minimizing the makespan. The algorithm takes landmarks, which are extracted from user's instructions given in structured English as input. Using different topologies for the landmark graphs, we show that it is possible to create schedules for changing object types, which will be processed in different stages in the robot cell. Results show that the POPStar algorithm can create and adapt schedules for robot cells with changing product types in low volume production.

In this paper we present a new simplified natural language that makes use of spatial relations between the objects in scene to navigate an industrial robot for simple pick and place applications. Developing easy to use, intuitive interfaces is crucial to introduce robotic automation to many small medium sized enterprises (SMEs). Due to their continuously changing product lines, reprogramming costs are far higher than installation costs. In order to hide the complexities of robot programming we propose a natural language where the use can control and jog the robot based on reference objects in the scene. We used Gaussian kernels to represent spatial regions, such as left or above. Finally we present some dialogues between the user and robot to demonstrate the usefulness of the proposed system.

Human hands and fingers have been widely studied in different fields, such as animation, biomechanics, ergonomics, rehabilitation, medicine, and robotics. However, since the hands are a highly complex part of the human body capable of developing precise tasks, replicating human hand mechanisms remains challenging and, thus, continues to be an active area. This study focuses on a bioinspired mechanically equivalent finger model. A parametric model was proposed based on the typical architecture of a human finger, allowing adaptation to different anthropometries. A forward kinematic model assesses each phalanx's range of motion (ROM) during flexion-extension and abduction-adduction. A CAD modeling technique based on fused filament fabrication (FFF) is used for easy fabrication, requiring no assembly. The resulting model achieves the desired ROM, offering a simple solution for hand modeling. This bioinspired model aids in training hand exoskeleton robots, accurately mimicking human finger mechanics for various applications, including rehabilitation and prosthetics. This model helps understand complex hand mechanisms and holds potential for robotics and related fields.

Safety-related functions are important in autonomous industrial applications and are featured in an extensive body of work contained within the standards. The implementation of safety-related systems is commonly done by an external company at a great cost and with limited flexibility. Thus, the objective of this thesis was to develop and implement a safety-related system using o-the-shelf products and to analyse how well it can comply with the established standards of safety-related functions. This work has sought to review the current standards for safety-functions, the eectsof harsh radio environments on safety-related systems, and how to validate the safety-function.The system development process was used to gain knowledge by rst building the concept based on pre-study. After the pre-study was nished, the process moved to the development of software, designed to maintain a wireless heartbeat as well as to prevent collisions between the autonomous and manual-driven vehicles at a quarry, and implementation of the system in real hardware. Finally, a set of software (simulations) and hardware (measurements in an open-pit mine) tests were performed to test the functionality of the system. The wireless tests showed that the system adhered to the functional requirements set by the company, however, the evaluated performance level according to ISO 13849-1 resulted in performance level B which is insucient for a safety-related function. This work demonstrates that it is not possible to develop a safety-related system using the off-the-shelf products chosen, without hardware redundancy.

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.

The industrial collaborative robot (ICR) application is a promising automation technology that combines human abilities with the repeatability and accuracy of an industrial robot. Yet, industrial challenges have prevented ICR applications from being implemented extensively in industry. Therefore, the purpose of the presented work is to deepen the knowledge of the key challenges that manufacturers experience during the implementation of ICR applications. In this study, a case study approach was used with eight companies to identify those challenges. The analysis of the qualitative data was conducted based on thirteen interviews with respondents from the industry to identify their challenges when implementing ICR applications. In this paper, a defined implementation process is presented that is combined with three significant areas of challenges relevant for the implementation of ICR applications, i.e., safety, knowledge, and functionality. Then, these areas are used as a basis to identify the corresponding challenges during the early implementation phases. The findings of the study point to an insufficient understanding of safety assessment and a lack of operator involvement in the pre-study phase that was propagated into the later implementation phases. The application design phase was identified to have several ad-hoc approaches due to a lack of knowledge concerning the application of ICR. In the factory installation phase, the challenges included increasing flexibility and ensuring standardised ways of working. This paper makes three distinct contributions to the research community. First, it provides rich data to the research concerning the implementation of applications of ICR, and it focuses on three areas, i.e., safety, knowledge, and functionality, and the challenges associated with their respective implementations. Second, contributions are made to the literature on implementing new technology, and they are focused on the early phases. Third, the results of this paper suggest that the role of system integrators might change in ICR application implementation projects. This paper contributes to practitioners a list of challenges that they might face during the implementation of ICR.

This project consists on developing an automatic night driving system in a simulation environment. The simulator I have used is TORCS. TORCS is an Open Source car racing simulator written in C++. It is used as an ordinary car racing game, as a IA racing game and as a research platform. The goal of this thesis is to implement an automatic driving system to control the car under night conditions using computer vision. A camera is implemented inside the vehicle and it will detect the reflective light-markers placed along the roadside. The car is oriented only with the lights the camera can see.

Wheel loaders are under the influence of low frequency vibrations that may be harmful for the health of the operator, and for the productivity of the machine. The strong vibrations can significantly impact the operation of the machine since they are not equipped with wheel suspension systems and due to their work environment in rough terrains with uneven surfaces. The risk of spilling or dropping the load is also increased and they can introduce higher mechanical stress that can wear out parts faster. The focus of this thesis work is to develop an approach for damping these vibrations dynamically by improving the functionality of the electrohydraulic system in Volvo's prototype wheel loader LX1. The dynamic damping system controls the cylinder of the lift framework to make it behave as a damper. The system utilizes the lift cylinder pressure and piston position as feedback to adjust the oil flow in the cylinder using the proposed control system. Results indicate that the proposed technique is capable of attenuation that is comparable with the existing boom suspension system based on accumulators through simulations and experimental tests.

As manufacturing companies are becoming more global, dynamic, and competitive, contradictory demands intensify. Flexibility is a key enabler for meeting the challenges of a global market if offered at mass production price and quality. Many companies have adopted Flexible Manufacturing System (FMS) together with new technologies. Nevertheless, despite the drastic increase in industrial robots adoption, industrial robot applications continue today as they were designed 50 years ago. To obtain a flexible and reliable production system, it takes more than technology as quality depends on equipment and manufacturing processes. Non-adaptive industrial robots autonomy may be disrupted by the geometrical deformations of the fixtures. This paper presents a comprehensive case study of adopting a robotic in-line quality inspection in an automotive Original Equipment Manufacturer (OEM) to aid the robot-fixture collision problem. The purpose is to examine errors that occur in production processes and how quality inspection can mitigate such errors. Empirical data collection was carried out in the form of (i) interviews, (ii) participant observations, (iii) documents, and (iv) video recording of robot cells. Results show that contrary to the case company beliefs, the manufacturing system does not follow the FMS standards; thus, to harmonise resources design and manufacturing processes, adding a robotic in-line quality inspection station is not enough. First, the robotic in-line quality inspection should follow a “preventive” control strategy to avoid deviated fixturing from entering the robot line. Second, the managers should address the beliefs of operators and their activities in solving the robot-fixture collision problem. Moreover third, the robot gripper design needs to be updated to an appropriate one.

The current manufacturing climate is characterised by the offering of customisable or one-of-a-kind products at mass production prices while complying with sustainability demands. Consequently, modern manufacturing companies are employing more and more industrial robots to increase flexibility and productivity. Nonetheless, for manufacturing companies to achieve true sustainability, a Zero-Defect Manufacturing (ZDM) approach is needed. Such an approach implies applying emerging technologies for quality inspection and improvement, as no system is perfect or free from deviations. Science and Technology (S&T) roadmaps are employed as decision aids to improve the coordination of activities and resources in increasingly complex and uncertain environments. This paper presents a S&T roadmap to guide all-size companies towards ZDM through advanced robotics for in-line quality inspection. The Key Performance Indicators (KPIs) and progress metrics for robotic in-line quality inspection towards ZDM are outlined.

Being able to safely control autonomous heavy machinery is of uttermost importance for the conversion of traditional machines to autonomous machines. With the continuous growth of autonomous vehicles around the globe, an increasing effort has been put into certifying autonomous vehicles in terms of reliability and safety. In this thesis, we will investigate the problem with a deviation from the planned path for an autonomous hauler from Volvo Construction Equipment. The autonomous hauler has an error within the kinematic model, the feed-forward curvature-steering controller, due to a slip-effect that comes with the third wheel-axle. The deviation can especially be seen in sharp curves, where the deviation needs to be decreased in order to make the autonomous hauler more dependable and achieve an increased accuracy when following any given path. The aim of the thesis is to develop a fully functional Artificial Neural Network that has a new steering angle as output. The hypothesis for this thesis is to use an ANN to mimic the steering of a human driver, since a real driver compensates for the slipping behavior; both because the operator knows where on the road the machine is and also in the way that a human thinks many steps ahead whilst driving. This proposed ANN will have a monitor function which ensures that the steering angle command operates within its boundaries. Hence this thesis implies that it is indeed possible to ensure that the ANN performs reliably with the help of a monitor function in a simulated environment and can thus be used in dependable systems.

Industrial robot calibration is a very important tool when extra precision and repeata- bility is required, also to make it possible to change robot without reprogramming. The difference between the virtual robot and the real robot can be as much as 8-15mm [1]. Presently used methods for calibration are both expensive and time-consuming. This the- sis will test a method of calibration that is simple and cheap, using kinematic coupling. The repeatability of the calibration and improvements to the method have been investi- gated, as well as the impact of lost-space motion at different positions of the robot. The thesis shows that the precision of the robot when coupling is better than ±0.05mm. A problem with backlash in a gearbox was solved using statistical method and by adding an external force affecting the coupling. Also a simulation of the coupling is developed to more easily identify errors in any axis.

It is not reliable to depend on a persons inference on dense data of high dimensionality on a daily basis. A person will grow tired or become distracted and make mistakes over time. Therefore it is desirable to study the feasibility of replacing a persons inference with that of Machine Learning in order to improve reliability. One-Class Support Vector Machines (SVM) with three different kernels (linear, Gaussian and polynomial) are implemented and tested for Anomaly Detection. Principal Component Analysis is used for dimensionality reduction and autoencoders are used with the intention to increase performance. Standard soft-margin SVMs were used for multi-class classification by utilizing the 1vsAll and 1vs1 approaches with the same kernels as for the one-class SVMs. The results for the one-class SVMs and the multi-class SVM methods are compared against each other within their respective applications but also against the performance of Back-Propagation Neural Networks of varying sizes. One-Class SVMs proved very effective in detecting anomalous samples once both Principal Component Analysis and autoencoders had been applied. Standard SVMs with Principal Component Analysis produced promising classification results. Twin SVMs were researched as an alternative to standard SVMs.

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.

As the power distribution system grows and more sensors are added, more data is created every day. This data can be crucial for finding faults, but there is now so much data that it ends up being unused. This presents a valuable opportunity to gain crucial insights into the continuously expanding and increasingly complex power distribution system. 

This thesis aims to utilize this valuable resource by finding a feature extraction method that can find valuable features in real-world data, use these features to cluster the data, separate different faults into different clusters, and develop a method for how these clusters can be classified, making it possible for an expert to classify large amounts of data quickly. 

In the end, an autoencoder was used for the feature extraction. The features could be used to cluster both labeled and unlabeled real-world data. The clustering also made it possible to find errors in the labeled data, as the data from one class were clustered into two clusters. A method was developed that allowed the clusters of 32454 unlabeled datapoints to be accurately classified in approximately 30 minutes. 

This thesis has successfully developed a method that can be used to get insights from large amounts of data, helping experts within the field of power engineering build the power distribution system of the future.

In this thesis we have designed and implemented components for an autonomoussystem for material re handling in the construction business, an autonomouswheel loader. Our achievements was at the end of this thesis demonstratedon a prototype machine. Our main contribution to the autonomous wheelloader is a new software platform design, with this new platform future expansionsis made easier. We have also developed several software components forthis platform that solves tasks required for autonomous operation with the wheelloader the most important listed below.ˆ Mission state machineˆ Vision algorithm, detect and parametrizes a load receiver (in this thesis ahauler) from LiDAR scanner dataˆ Translation algorithm, translate the wheel loader to a dynamic target andthere empty the payloadLow level development on the vision system of the autonomous wheel loaderwas also required to bee able to reach the target of the thesis. We have implementeda communication driver and a API for a LiDAR scanner and a API for aservo controller. A mechanical xture have been designed in CAD, manufacturingof this xture was achieved using a CNC mill.

The manufacturing industry faces new challenges after the emersion of the fourth industrial revolution having brought with it a new knowledge-based and digital era. Among the most important factors for survival of today´s manufacturing companies are efficiency, flexibility, and responsiveness to the changing market. The technological development certainly raises the bar as far as demands go, but it also introduces new solutions to deal with the increase in demand. Opportunities for a higher degree of automation in manufacturing is one such thing.

ABB Electrification Sweden AB are interested in introducing a higher degree of automation in an assembly cell where contactors are manufactured. Currently there is no sure way to assess to what extent it is possible to increase the level of automation within the assembly system. The purpose of this study is to explore opportunities for streamlining the assembly process through automation, to increase productivity. The research question to be answered in this study reads as follows:

RQ: How can automation be used to increase productivity in an existing assembly cell where contactors are manufactured?

This study was conducted in the form of a case study based on a product development process in two stages. Interactive research was also conducted during three workshops where practitioners from the company joined the researcher in analysing data and contributing with prior experiences. Through analysing the data collected from the assembly process, a common ground for knowledge could be achieved which contributed to introducing decision criteria regarding actions for automation implementation.

The study resulted in a proposition of an updated assembly process with a higher degree of automation than previously. This proposed solution consisted of several specifications ranging from an estimation of cycle time for each assembly process to the cost of acquiring new equipment. Furthermore, a layout was suggested as well as an estimation of labour costs and resources. A 25 % increase in productivity could also be estimated based on the reduced cycle time.

Automation can be used as a measure to streamline the manufacturing process of contactors in an assembly cell. To achieve this, knowledge and understanding of the specific assembly unit is required to identify available opportunities. This knowledge can be acquired based on a combination of prior experiences and evaluation of the current assembly system.

Other conclusions from the study were that previous research regarding automation of assembly cells could be applied to the case of an assembly cell where contactors are manufactured as well. Furthermore, actions for automation are easier to implement for products that are adapted for automated assembly. Finally, automation is most useful when regarded as a tool for optimization and should not be thought of as a framework to be applied on a larger scale.

Key words: contactor manufacturing, assembly cell, automation, streamlining, productivity

The estimated position of an industrial robot’s end-effector is crucial for its performance. Contemporary methods for doing the estimation is limited in certain aspects, and alternative methods are in high demand. This work builds on a method previously introduced where an Inertial Measurement Unit (IMU) device is combined with the robot’s system via sensor fusion. The IMU must be calibrated before its signal is used in sensor fusion and this work implements and builds on current cutting-edge methods for calibration. Sensor fusion is a crucial part of the method and here a complementary filter is used. The finished estimation is then used with Iterative Learning Control (ILC) to investigate if the accuracy can be further improved and also test its viability. Results from ILC show that the IMU can indeed be used to estimate the end-effector's trajectory but that sensor fusion is mandatory. Further research could be done to allow the estimation to be done online instead of offline and ILC could be tested more extensively.

Smart portable devices is increasing in popularity in many fields. In motion tracking many devices have been created in the last years as a help in motivation and observation for training. Most of them is for tracking distance moved, heart-rate and some more basic functions. For deeper analyses in motion tracking a more advanced system is needed. The system needs to be small and light to not influence the movement of the subject in a negative way. It should preferably be cheap. Two other factors is that the system needs to be easy to use, both in the interface and deployment.

Symmetry in motion is an key-element to effective use of energy. Measuring the symmetry in gait should then help to improve motion. This could be used as a tool for more efficient training or to faster recover from an injury. For a stroke-patient this could perhaps decrease the time of rehabilitation and remind the patient to move one leg. To create this, a reliable communication between a data sink and sensor nodes has been developed. The sensor nodes is gathering nine dimensions IMU data, accelerometer, gyroscopes and magnetometer, each in three dimensions. The data is saved to a database where it can be extracted for further analyses.

Testing of the script language for Bluetooth devices, BGScript for time synchronisation has been done to see if it is able to use for frequencies high enough for a system to measure movement.

Due to the globalization today the competition in the market has in- creased and it requires flexibility and produce according to customer demand. In order to reduce the cost of wages industrial companies are now considering moving the manufacturing to low-cost countries. To keep up with the competition in the market without moving the manu- facturing abroad, Lean Automation was developed. The concept of Lean Automation is to reduce the perceived complexity with automa- tion and make automation available for new users and in new applica- tions.

Haldex Brake Products are situated in Sweden and want to maintain the manufacturing of the automatic brake adjusters in the country. It is a product well-known and highly exposed to the competition in the mar- ket. Due to this situation Haldex are collaborating with Mälardalen University to develop a robotic cell based on the concept of Lean Au- tomation. It will be installed on their assembly line in 2012.

The goal with this master thesis is to create a development process for automation, which can enable the company to be more competitive in the market and this by using their own knowledge to develop automat- ed solutions for higher efficiency in the production line. In addition to the development process for automation, programming of the Lean Au- tomation robotic cell is to be conducted.

The conducted work in the master thesis was the programming of the robotic cell and the development process for automation. The pro- gramming was made Lean by reducing waste of time when working with the programmed code. This by a clear structure in the program- ming which makes it easy to handle. A development process for auto- mation was made and used for developing a robotic cell for the assem- bly line. The robotic cell was then tested at Haldex, an important stage before implementing the cell on the assembly line. Testing the robotic cell will give important information about how the equipment actually runs and failures can be observed and adjusted. Testing of the robotic cell will continue after the end of this master thesis. 

In this thesis, a kinematic model for controlling an Omni-directional 3 DOF DDR with an external navigation point is presented. Two different dynamic models for investigating the resulting torque on the three active motors on the robot are also developed and validated. The focus of the thesis is on the design of kinematic and dynamic models in an ideal environment and the kinematic model in a high fidelity environment. The kinematic model uses inverse kinematics to translate the controlling motion reference from the external navigation point to the three active motors on the DDR. The thesis also includes a comparison of the two different dynamic models based on Kane's method and Newton's second law of motion, respectively. The models presented in this thesis could aid autonomous robots with attached payloads such as hospital beds move with the centre of geometry as a focal point and thereby perform movements. The results show that such a kinematic model for controlling the specific robot is shown to be feasible in an ideal environment. However, due to PID controllers for the active wheel motors not being exact enough, the model in the high fidelity environment does not perform correctly in all cases. Furthermore, the dynamic model results provide an understanding of the difference of torque dependant on the distance to the navigation point. 

This report is based on a thesis that was carried out during spring 2012. The report covers the development of a preliminary study to Performance Sk8. The purpose of the study is to facilitate and contribute inspiration to Performance Sk8 to run an automation project to bring home their production to Sweden from China. The reason is to gain a greater ability to control quality, produce smaller batches and reduce the cycle time to reduce costs.

The goal is that the preliminary study shall meet the requirements and wishes Performance Sk8 and other partners defined. The pre study will contain different cost proposals for the automation of selected process and serve as a basis for procurement of the equipment.

The pre study has resulted in some proposed solutions to the various steps required to automate Performance Sk8 ́s production. In addition to the proposed solutions are also concept proposal which has developed with suggestions for flows, prices and other information that can help Performance Sk8 in its ongoing automation project.

Evolving technology in wireless telecommunication, such as 5G, provides opportunities to utilize wireless communication more in an industrial setting where reliability and predictability are of great concern. More capable Industrial Internet of Things devices (IIoT) are, indeed, a catalyst for Industry 4.0. Still, before the IIoT devices can be deemed capable enough, a method to evaluate the IIoT systems unobtrusively—so that the evaluation does not affect the performance of the systems—must be established. This thesis aims to answer how the performance of a distributed control system can be unobtrusively evaluated, and also determine what the state-of-the-art is in latency measurements in distributed control systems. To answer the question, a novel diagnostics method for time-critical control systems in cloud-fog automation is proposed and extensively evaluated on real-life testbeds that use 5G, WiFi 6, and Ethernet in an edge-computing topology with real control systems. The feasibility of the proposed method was verified by experiments conducted with a diagnostics framework prototype developed in this thesis. In the proposed diagnostics framework, the controller application is monitored by a computing probe based on an extended Berkeley Packet Filter program. Network communication between the controller and control target is evaluated with a multi-channel Ethernet probe and custom-made software that computes several metrics related to the performance of the distributed system. The data from the unobtrusive probes are sent to a time-series database that is used for further analysis and real-time visualization in a graphical interface created with Grafana. The proposed diagnostics method together with the developed prototype can be used as a research infrastructure for future evaluations of distributed control systems.

Due to safety criticality, distributed robotic systems, such as Robot Operating System 2 (ROS 2) based applications, often have strict timing requirements. In this thesis, we attempt to simplify formal verification of the timing behaviour of ROS 2 based applications. Therefore, (i) we conduct experiments to determine and define patterns and semantics of ROS 2 task scheduling and execution, (ii) we propose a pattern-based formal approach of modeling and verifying ROS 2 applications via model checking in UPPAAL, and (iii) we propose a methodology for model-based development and verification of ROS 2 application designs. The thesis starts with a comprehensive evaluation of timing behavior, including the internal scheduling of ROS 2 applications, to define evaluation metrics and timing correctness. Based on the evaluation, buffer overflow and callback latency are defined as measures for timing errors. Furthermore, we identify application design patterns and parameters that can influence potential timing errors. To introduce and facilitate the use of formal methods, we propose pattern-based verification, using UPPAAL, creating reusable templates of important ROS 2 application components. Furthermore, we show how to apply the templates to model ROS 2 applications and verify potential buffer overflow and callback latencies. Finally, we propose a novel methodology for automation of verification in the context of ROS 2 that uses generated tracing information of ROS 2 executions to build structural models as class diagrams and, ultimately, formal models in the form of networks of UPPAAL timed automata for model checking. In our approach, one can apply the methodology as a framework that includes model checking as a back-end and, therefore, helping designers to bridge the gap between the ROS 2 code and formal analysis.

To simplify the formal verification of ROS 2-based applications, in this paper, we propose a novel approach to the automation of their model-based verification using model-driven engineering techniques. We propose a methodology starting with ROS 2 execution traces, generated by ROS2_tracing and using models and model transformations in Eclipse to automatically initialize pre-defined formal model templates in UPPAAL, with system parameters. While the methodology targets the simplification of formal verification for robotics developers as users, the implementation is at an early stage and the toolchain is not fully implemented and evaluated. Hence, this paper targets tool developers and researchers to give a first overview of the underlying idea of automating ROS 2 verification.

Hence, we propose a toolchain that supports verification of implemented and conceptual ROS 2 systems, as well as iterative verification of timing and scheduling parameters. We propose using four different model representations, based on the ROS2_tracing output and self-designed Eclipse Ecore metamodels to model the system from a structural and verification perspective. The different model representations allow traceability throughout the modeling and verification process.Last, an initial proof of concept is implemented containing the core elements of the proposed toolchain and validated given a small ROS 2 system. 

In this paper, we propose a formal modeling approach in uppaal to simulate and verify multi-threaded robotics middleware execution based on ROS 2. In the modeling process, we consider middleware-specific scheduling by creating formal models that simulate the execution behavior of a ROS 2-based system. Furthermore, we show how to model potential underlying operating system's influences on execution by modeling reservation servers. We propose timed automata templates to model the multi-threaded execution of ROS 2 systems and the reservations of the underlying operating system in uppaal.We show how to utilize the created templates to simulate a ROS 2 application. We demonstrate the application of the formal models and model checking in various ROS 2 experiments. Furthermore, we validate the created models by comparing the observed execution traces in experiments on ROS 2 systems and the simulated traces of our models.Overall, this paper showcases the application and usability of model-based verification of distributed middleware applications, including internal scheduling and influences of underlying operating system actions.

Multi-agent robot systems, especially for mobile robots in dynamic environments interacting with humans, have seen an increased interest over the past years. Many vehicle manufactures (e.g.Volvo GTO) have been following the trend and has started investigating a possible implementation of an autonomous-transport robot system for material delivery in production environments.First implementations of a system have been built using ROS2 and initialising static amounts of participating robots.Throughout this thesis, scalability is emphasised to enhance and add new use cases to the system.This thesis investigates possible improvements for the system by adding a dynamic connection handling, which allows robots to connect and disconnect under the system's run time.Furthermore, the performance of the connection handling in the system is evaluated in simulation for increasing system complexity in terms of the amount of connected robots.The first part of the thesis presents an approach for the dynamic connection and disconnection of robots to the network using service client communication approaches.An implementation is tested in a simulation based on an excerpt from the legacy system.Furthermore, two methods are proposed for detecting possible communication losses. The thesis work simulates the increase of the number of robots in the system at different publishing rates. It compares a many to one communication approach, where multiple robots communicate to a central node over one topic, to the one to one communication approach, where multiple robots communicate over particular topics to a central node.The simulations have shown that with an increase of nodes, the average data age and the data miss ratio in the one to one approach were significantly lower than in the multi to one approach.

Autonomous Unmanned Aerial Vehicles (UAVs) in agriculture are increasingly in demand to reduce cost, labour and increase effectiveness and quality in farming. However, it is necessary to improve reliability for this technology to perform its full potential without harming humans, animals or the environment. The reliability increases by identifying the hazards and mitigating them. Therefore the risks are identified, analyzed and mitigated using analysis tools. Two different methods are used to analyze and reduce hazards, and each method utilizes various analysis tools. In addition, redundancy and preventive action are proposed to eliminate or minimize the danger. This thesis identifies risks by studying and reviewing a generic use-case from the AFarCloud project and compares the two hazard analysis methods to determine which method provides the most reliable result.

There is a need for compact, high-speed, and low-power vision systems for enabling real-time mobile autonomous applications. The best approach to achieve this is to implement the bulk of the application in hardware. Reconfigurable hardware meet these requirements without the limitation of fixed functionality that accompanies application-specific circuits. Resource constraints of reconfigurable hardware calls for optimized implementations i terms of resource usage with maintained performance.

The research group in Robotics at Mälardalen University is moving toward the completion of a reconfigurable hardware-platform for stereo vision, coupled with a compact embedded computer. This system will incorporate hardware-based preprocessing components enabling visual perception for autonomous machines. This thesis covers the reconfigurable hardware section of the vision system concerning the realization of scene depth extraction. It shows the advantages of image preprocessing in hardware and propose a resource optimized approach to stereo matching. The work quantifies the impact of reduced resource utilization and a desire for increased accuracy in disparity estimation. The implemented stereo matching approach performs on par with recent similar implementations in terms of accuracy, but excels in terms of resource utilization and resource sharing, as the external memory requirement is removed for larger images.

Future work aims to further include processes for navigation, and structure and object recognition. Furthermore, the system will be adapted to real world scenarios, both indoors and outdoors.

This paper proposes a limited implementation of areabasedstereo matching for minimal resource utilization. It shows that it is possible to achieve an acceptable disparity map without the use of expensive resources. The matching accuracy for the single-row SAD can even outperform that of its full-row counterpart. Additionally, it excels in terms of frame rate and resource utilization, and is highly suitable for real-time stereo-vision systems.

This paper presents a set of approaches for increasing the accuracy of basic area-based stereo matching methods. It is targeting real-time FPGA systems for dense disparity map estimation. The methods are focused on low resource usage and maximized improvement per cost unit to enable the inclusion of an autonomous system in an FPGA. The approach performs on par with other area-matching implementations, but at substantially lower resource usage. Additionally, the solution removes the requirement for external memory for reconfigurable hardware together with the limitation in image size accompanying standard methods. As a fully piped complete on-chip solution, it is highly suitable for real-time stereo-vision systems, with a frame rate over 100 fps for Megapixel images.

The concept Industry 4.0 brings several technologies that could be useful for the factories and manufacturers to become more competitive. One of these technologies is the robots in collaborative environments which operate fenceless together with the operators. The interest of these robots in the manufacturing sector has been getting more attention in recent years. These implementation types require the organizations to determine their readiness levels to have success with the robot. Currently, the successful implantations of the robots in collaborative environments in industry are few, and the success factors must be mapped. Therefore, investigating the potential barriers, enablers and establishing a road map for a case company could be useful to fill this gap and assist the organizations with what is necessary to accomplish a successful implementation.

A case study was conducted at a manufacturing company in Sweden, which allowed for insights into how an actual organization approaches the situation. The case company organization was in the process of acquiring a robot in a collaborative environment, hence, the data from the case company was relevant to answer the research questions. Furthermore, the data was collected using interviews, observations, and documents at the case company. A literature review was performed to provide information regarding the common factors within a RCE implementation, which also was a base for the interview guide. The data was analysed by comparing the theoretical framework and with the empirical findings to bring forth a conclusion and to establish a road map for the case company. A road map was created with sets of questions to address the identified barriers and enablers found in both literature and at the case company.

The constructed road map contains questions concerning the areas of knowledge about the robot in collaborative environments, communication & information, organizational aspects, and resistance towards the change. By addressing the questions developed in the framework, the organization could get an advantage when considering the implementation of the robot. For the case company, as they are further along in the process, the road map could indicate how well they have performed in current projects. Furthermore, it could generate a more successful project the next time.

Industry 4.0 brings several technologies that might be useful for manufacturers to become more competitive. One of these technologies is robots in collaborative environments (RCE), which operate fenceless together with the operators. The interest of these robots in the manufacturing sector has been getting more attention recently. Nevertheless, manufacturers should determine their organization's readiness for implementing RCE successfully. Therefore, this paper presents results from a case study conducted at a manufacturing company in Sweden. The case study approach allowed for insights into how manufacturers can approach RCE organizational readiness. The investigated case company was in the process of acquiring a robot in a collaborative environment. The resulting list of critical factors contains aspects concerning four areas to support organizational readiness for RCE, namely, communication, knowledge, organizational, and resistance. By addressing the critical factors presented in the list, manufacturers can identify critical organizational readiness aspects when implementing RCE. 

The research field of robotic prosthetic hands have expanded immensely in the last couple of decades and prostheses are in more commercial use than ever. Classification of hand gestures using sensory data from electromyographic signals in the forearm are primary for any advanced prosthetic hand. Improving classification accuracy could lead to more user friendly and more naturally controlled prostheses. In this thesis, features were extracted from wavelet transform coefficients of four channel electromyographic data and used for classifying ten different hand gestures. Extensive search for suitable combinations of wavelet transform, feature extraction, feature reduction, and classifier was performed and an in-depth comparison between classification results of selected groups of combinations was conducted. Classification results of combinations were carefully evaluated with extensive statistical analysis. It was shown in this study that logarithmic features outperforms non-logarithmic features in terms of classification accuracy. Then a subset of all combinations containing only suitable combinations based on the statistical analysis is presented and the novelty of these results can direct future work for hand gesture recognition in a promising direction.

Battery systems of today forces the design of applications to compromise between important cell parameters. Through the use of different cells, the suitability of power solutions are adapted closer to the needs of specific applications. This work outlines current research in heterogeneous battery technology. And investigates a system for testing and verifying cell mixing and current control strategies. In the process analysing the performance of two heterogeneous battery system architectures, one DC/DC based and the other a based on a diode module.The system properly performs the analysis and displays the correct characteristics for use. With the cell combination being tested displaying potential as partners for temperature regulation and discharge offloading.

Unmanned Aerial Vehicle (UAV), also called drones, are used for Search And Rescue (SAR) missions, mainly in the form of a pilot manoeuvring a single drone. However, the increase in labour to cover larger areas quickly would result in a very high cost and time spent per rescue operation. Therefore, there is a need for an easy to use, low-cost, and highly autonomous swarm of drones for SAR missions where the detection and rescue times are kept to a minimum. In this thesis, a Subsumption-based architecture is proposed, which combines multiple behaviours to create more complex behaviours. An investigation of (1) what are the critical aspects of controlling a swarm of drones, (2) how can a combination of different behavioural algorithms increase the performance of a swarm of drones, and (3) what benchmarks are necessary when evaluating the fitness of the behavioural algorithms. The proposed architecture was simulated in AirSim using the SimpleFlight flight controller through experiments that evaluated the individual layers and missions that simulated real-life scenarios. The results validate the modularity and reliability of the architecture, where the architecture has the potential for improvements in future iterations. For the search area of 400×400meters, the swarm consistently produced an average area coverage of at least 99.917% and found all the missing people in all missions, with the slowest average being 563 seconds. Compared to related work, the result produced similar or better times when scaled to the same proportions and higher area coverage. As comparisons of results in SAR missions can be difficult, the introduction of Active time can serve as a benchmark for others in future swarm performance measurements. 

Unmanned Aerial Vehicle (UAV) swarms, also known as drone swarms, have been a subject of extensive research due to their potential to enhance monitoring, surveillance, and search missions. Coordinating several drones flying simultaneously presents a challenge in increasing their level of automation and intelligence to improve strategic organization. To address this challenge, we propose a solution that uses hill climbing, potential fields, and search strategies in conjunction with a probability map to coordinate a UAV swarm. The UAVs are autonomous and equipped with distributed intelligence to facilitate a cooperative search application. Our results show the effectiveness of the swarm, indicating that this approach is a promising approach to addressing this problem.

Localization is a critical aspect of robots and their industrial applications, with its major impact on navigation and planning. The goal of this thesis is to improve multi-robot localization by utilizing scan matching algorithms to calculate a corrected pose estimate using the robots' shared laser scan data. The current pose estimate relative to the map is used as the initial guess for the scan matching. This corrected pose is fused using several different localization configurations, such as an Extended Kalman Filter in combination with the Adaptive Monte Carlo Localization algorithm. Simulations showed that localization improved by resetting the Monte Carlo particle filter with the pose estimate generated by the collaborative scan matching. Further, in simulated scenarios, the collaborative scan matching implementation improved the accuracy of typical Monte Carlo Localization configurations. Furthermore, when filtering based on the number of reciprocal correspondences between the scan match output and the target scan, one could extract highly accurate pose estimates. When resetting the Monte Carlo Localization algorithm with the pose estimates, the localization algorithm could successfully recover from severe errors in the global positioning. In future work, additional testing needs to be done using these extracted pose estimates in a dedicated map-based multi-robot localization algorithm.

Undersökningar och beräkningar av en rengöringsprocess med ibc-containers vid Allemballage har gjorts. En automatiserad lösning har tagits fram där vissa delar har tillhandahållits i förväg som rengöringsverktyg. Uppgiften blev att studera om verktyget klarade av de räckvidder som var avgörande samt att fastsälla om den robot som kunden ville använda sig av kunde utföra uppgiften genom att ta reda på vilket moment som uppstod för att sedan ta fram en simulering av processen i RS. Räckvidden undersöktes dels i RS men även ett presentationsunderlag togs fram. Momentet undersöktes dels praktiskt där man fastställde de krafter som verkade och teoretiskt där man sedan beräknade momentet. Det sista som gjordes var en simulering på hur processen kan se ut då en IRB4600-robot ifrån Abb och rengöringsverktyg utförde en rekonditionering av en IBC.

Resultaten har visat att momentet i kortare stunder överstiger det krav som ställs på önskad robot men att verktyget klarar de räckvidder som ställs. Räckvidden och momentet har visat sig vara beroende av varandra då en minskning av räckvidden leder till ett mindre moment, vise versa.

Med de undersökningar som gjorts och det resultat som tagits fram är rekommendationen i dagsläget att man bör lägga en beställning på det aktuella rengöringsverktyget. Vidare så anses robot IRB4600 kunna utföra uppgiften som tänkt, mindre justeringar bör då göras vid de ställen som visat sig utgöra det största momentet vid rengöring.

Volvo Group Truck Technology has the ambition to automate parts of their routine service. Therefore a project was launched to investigate which parts of the routine that could be automated. The idea of this project is to lower the time spent on the service and also improve the working environment for the personnel. The purpose of this thesis is to develop and build a conceptional prototype for a low-cost crack detecting sensor. This thesis is a part of a larger proof of concept project which Volvo GTT runs in cooperation with Robotdalen and Robot Application Center (RAC). The work done in this thesis has been based on literature studies, interviews and company visits. The gathered knowledge and observations was translated into what would be required to fit the needs. This thesis covers different techniques that could be used to detect flaws in braking discs. However, this thesis is mostly focused on one non-destructive method technique based on induced eddy currents. Several non-destructive techniques and conceptual designs has been tested and evaluated with varying results during this project. The result of this thesis was a design that reacts to discontinuities in conductive materials, such as the grey cast iron material used in the Volvo trucks braking discs. The results are represented as a voltage drop change and can be visualized by an oscilloscope. This study shows that the method of choice has the potential to be used as a crack detecting system and that the system can be built reliable with rather cheap components. Further development should aim towards making the design even cheaper and the components should be assembled on a PCB instead of a breadboard in order to make the system less sensitive to noise and easier to assemble alongside the trucks braking discs.

The majority of the UWB radars available on the market today are expensive and often closed forfurther development due to proprietary rights. Therefore it is difficult to fully understand and adaptthe functionality of an available UWB system to fit one’s needs. The consulting-firm Addiva purchasedan UWB radar to be used in a safety system. However, the radar had limitations and the functionalityof it was partly unknown. This master thesis was inspired from this issue to examine the possibilitiesof developing a low-cost UWB radar, with main focus on research of human detection. The systemshould be easy to understand and modify, as well as reporting reliable data from the scanning. Theresults indicate that such a system can be developed. However, further development to the UWB radarneeds to be made in order to have a complete system.

Unmanned aerial vehicle (UAV) is an expanding interest in numerous industries for various applications. Increasing development of UAVs is happening worldwide, where various sensor attachments and functions are being added. The multi-function UAV can be used within areas where they have not been managed before. Because of their accessibility, cheap purchase, and easy-to-use, they replace expensive systems such as helicopters- and airplane-surveillance. UAV are also being applied into surveillance, combing object detection to video-surveillance and mobility to finding an object from the air without interfering with vehicles or humans ground. In this thesis, we solve the problem of using UAV on autonomous sites, finding an object and critical situation, support autonomous site operators with an extra safety layer from UAVs camera. After finding an object on such a site, uses GPS-coordinates from the UAV to see and place the detected object on the site onto a gridmap, leaving a coordinate-map to the operator to see where the objects are and see if the critical situation can occur. Directly under the object detection, reporting critical situations can be done because of safety-distance-circle leaving warnings if objects come to close to each other. However, the system itself only supports the operator with extra safety and warnings, leaving the operator with the choice of pressing emergency stop or not. Object detection uses You only look once (YOLO) as main object detection Neural Network (NN), mixed with edge-detection for gaining accuracy during bird-eye-views and motion-detection for supporting finding all object that is moving on-site, even if UAV cannot find all the objects on site. Result proofs that the UAV-surveillance on autonomous site is an excellent way to add extra safety on-site if the operator is out of focus or finding objects on-site before startup since the operator can fly the UAV around the site, leaving an extra-safety-layer of finding humans on-site before startup. Also, moving the UAV to a specific position, where extra safety is needed, informing the operator to limit autonomous vehicles speed around that area because of humans operation on site. The use of single object detection limits the effects but gathered object detection methods lead to a promising result while printing those objects onto a global positions system (GPS) map has proposed a new field to study. It leaves the operator with a viewable interface outside of object detection libraries. 

In the truck industry, manual final assembly and material handling processes can be complex and crowded, making their automation difficult using traditional industrial robots. Collaborative robot systems, on the other hand, offer a flexible and user-friendly alternative that can free up human workers from repetitive and non-ergonomic tasks, allowing them to focus on more value-adding operations. Despite the considerable efforts made by researchers and within the industry to promote collaborative robots, they are often underused and their use is limited to handling simple automation tasks without perimeter fences.

The aim of this thesis is to enhance our understanding of human-robot collaboration and the challenges faced by complex industries when implementing intelligent and collaborative automation. The goal is to create a sustainable workplace where robots and humans can work together safely and efficiently in a flexible environment.

Through several industrial use cases, two demonstration setups were developed to identify a set of industrial challenges and requirements. These requirements include safe, efficient, and intuitive interactions, as well as deliberative and robust control, reliable communication, variant handling, and an efficient engineering process. However, the most critical requirement is ensuring the safety of both machines and humans. It was found that current safety standards trade safety for efficiency, flexibility, and cost, which limits the implementation of intelligent and adaptive collaborative systems in complex applications.

To address these issues, a new safety approach called deliberative safety is proposed, which allows for switching between different safety measures depending on whether flexibility or efficiency is required to attain production goals. A taxonomy is proposed to better support the design of deliberative safety, along with five safety measures ranging from currently existing measures like perimeter safety to planned and active safety. These measures can enable intelligent human-robot collaboration.

However, incorporating intelligence and using the deliberative safety concept may introduce new types of risks, which necessitates the development of new risk assessment and risk reduction methods. To address this, a risk assessment method based on reliability theory is combined with a novel method based on system theory to identify system requirements in the early stages of development and to identify risky scenarios and related risk reduction methods.

The findings of this research will be beneficial to manufacturing industries seeking to use intelligent and collaborative automation to increase flexibility when automating. Additionally, they will provide valuable inputs for the development of related safety standards and risk assessment procedures.