Många förändringar sker idag inom industrin och särskilt med tanke på omställning till industri 4.0. Dana Incorporated har genom Högskolan Väst efterfrågat en studie om automatiserade transporter i deras planerade fabrik i Åmål. Fabriken ska ligga i yttersta framkant gällande automation och effektivitet med industri 4.0 som mål. För att hitta rätt system behandlar denna rapport flera viktiga aspekter som behöver tas i beaktning vid övergång eller nyetablering av AGV/AMR truckar och robotar. Studier, intervjuer och fakta används i rapporten för att hitta rätt system i en föränderlig miljö där flexibilitet efterfrågas. Förstudierna om godsets förpackning, AGV/AMR-systemets modell och flexibilitet visar sig vara ytterst viktiga faktorer i studien. Det är stor skillnad mellan olika AGV/AMR-system i både pris och utförande, vilket gör att stora kostnader går att spara vid grundlig undersökning. Ju mer arbete och tid som läggs i förstudierna desto mer underlättar implementering och sparar på investeringskostnaden. Studien visar att ju längre tid det tar att implementera, blir kostnaden högre på grund av att företaget samtidigt belastas med operatörskostnader. Längre implementeringstid ger högre kostnad men innebär ett lägre risktagande. Att hitta den perfekta balansen mellan dessa två faktorer är svårt och tidskrävande. Det är ändå viktigt att ha vetskap om att både en för djärv och en för långsam satsning resulterar i onödiga kostnader. Strukturen och flexibilitet är andra delar som påverkar val av system. Inget AGV/AMR-system kan idag ha samma flexibilitet som en mänsklig truckförare. Organisationen måste därför struktureras till den flexibilitetsgrad som det tänkta systemet kan hantera. Vilken grad av vinst och resultat som ges genom det tänkta AGV/AMR-systemet grundas mycket genom kunskap om AGV/AMR-systemen som skulle passa företaget innan val av produkt. / Many changes are taking place today in the industry and especially with a view to conversion to industry 4.0. Through University West, Dana Incorporated has requested a study on automated transports in their planned factory in Åmål. The factory will be at the forefront of automation and efficiency with industry 4.0 as its goal. To find the right system, this report addresses several important aspects that need to be taken into account when transitioning or establishing new AGV / AMR trucks and robots. Studies, interviews and facts are used in the report to find the right system in a changing environment where flexibility is required. The feasibility studies on the packaging of the goods, the model and flexibility of the AGV / AMR system prove to be extremely important factors in the study. There is a big difference between different AGV / AMR systems in both price and design, which means that large costs can be saved during a thorough examination. The more work and time that is put into the feasibility studies, the easier it is to implement and save on the investment cost. The study shows that the longer the time implementation requires, the higher the cost due to contemporary operator costs. Longer implementation time results in higher costs but means lower risk-taking. Finding the perfect balance between these two factors is hard and time consuming. It´s still a good knowledge to know that both a too bold and a too slow investment results in unnecessary costs. The structure and flexibility are other parts that influence the choice of system. No AGV / AMR system today can have the same flexibility as a human truck driver. The organization must therefore be structured to the flexibility that is possible to handle for the selected system. The degree of profit and results given through the intended AGV / AMR system is largely based on knowledge before choosing a product.

Wearable robotics and exoskeletons have been explored for their efficacy in physical rehabilitation and for assistance in daily activities for people with motor disorders. The overall objective of this thesis is to design a powered soft exoskeleton for people with dropfoot and excessive inversion, commonly after a stroke, and to optimize the control parameters for each individual while considering different dimensions. This compilation thesis is based on two papers that focus on the design and verification of the ankle joint exoskeleton prototype, and control parameters optimization using human-in-the-loop optimization, respectively. In the first paper, we presented the design of the powered soft ankle exoskeleton, mainly consisting of the actuation system, Bowden cables, and textile components, to assist two degrees of freedom (DoF), dorsiflexion and eversion, simultaneously.A proof-of-concept study was performed to verify the functionality of the exoskeleton in two aspects: assisting/controlling two DoFs simultaneously and compensating for the resistance during ankle plantarflexion. Our results suggested that two-DoF assistance can be delivered with the structure, and the proposed force-free controller can counteract the inherent resistance in the system. In the second paper, a multi-objective-based human-in-the-loop optimization method was proposed, aiming at optimizing gait quality in different aspects simultaneously.In this case study, the multi-objective optimization method, Non-dominated Sorting Genetic Algorithm II, was implemented in the human-in-the-loop optimization. Four generations, comprising ten sets of control parameters in each generation, were tested on one non-disabled subject wearing the exoskeleton described in paper I. The results indicated that this novel method can identify the control laws that optimize both gait quality metrics. In the set of solutions, control laws with different focuses can be selected for different purposes or individual uses. / Bärbara robotar och exoskelett har utforskats för deras effektivitet inom fysisk rehabilitering och som stöd i dagliga aktiviteter för personer med motoriska störningar. Det övergripande målet med denna avhandling är att designa en kraftdriven mjuk exoskelett för personer med fallfot och överdriven inversion, vanligt efter en stroke, och att optimera kontrollparametrarna för varje individ med hänsyn till olika dimensioner. Denna sammanställda avhandling bygger på två artiklar som fokuserar på design och verifiering av prototypen för fotledsexoskelett samt optimering av kontrollparametrar med mänsklig medverkan. I den första artikeln presenterade vi designen av det kraftdrivna mjuka fotledsexoskelettet, som huvudsakligen består av aktiveringssystem, Bowden-kablar och textilkomponenter, för att assistera i två grader av frihet (DoF), dorsiflexion och eversion, samtidigt. En konceptbevisstudie genomfördes för att verifiera funktionen hos exoskelettet avseende två aspekter: assistera/styra två DoFs samtidigt och kompensera för motståndet under plantarflektion i fotleden. Våra resultat antydde att två-DoF-assistans kan levereras med strukturen och att den föreslagna kraftfria styrenheten kan motverka det inneboende motståndet i systemet. I den andra artikeln föreslogs en mänsklig medverkande optimeringsmetod baserad på flera mål, med syfte att optimera gångkvaliteten i olika aspekter samtidigt. I denna fallstudie implementerades den flerobjektiva optimeringsmetoden Non-dominated Sorting Genetic Algorithm II i den mänskliga medverkansoptimeringen. Fyra generationer, med tio uppsättningar av kontrollparametrar i varje generation, testades på en icke-funktionshindrad försöksperson som bar det exoskelett som beskrivs i den första artikeln. Resultaten indikerade att denna nya metod kan identifiera styrregler som optimerar både gångkvalitetsmått. I uppsättningen lösningar kan styrregler med olika fokus väljas för olika ändamål eller individuella användningsområden. / <p>QC 231115</p>

This bachelor thesis aims to integrate and evaluate a 3D printing robotic cell at the SmartIndustry Group – SIG lab at Linnaeus University (LNU).A sustainable structure consisting of wood fiber polymer composites was 3D printed withan industrial robot. Sustainable 3D printing material can be recycled or burned for energyafterwards. The 3D printing material used in this thesis stems from certificated forests. The objective is to utilise this technology in manufacturing courses and research projectsat the SIG lab at LNU. This objective is achieved by creating an operation manual and avideo tutorial in this thesis.The integration and evaluation process will involve offline robot programming,simulation, and practical experiments on the 3D printing robotic cell.

Two crucial functionalities of a fully autonomous robotic agent are localization and navigation. The problem of enabling an agent to localize itself in an unknown environment is an extensive and widely studied topic. One of the main areas of this topic focuses on Simultaneous Localization and Mapping (SLAM). Many advancements in this field have been made over the years resulting in robust and accurate localization systems. Navigation progress has also improved substantially throughout the years resulting in efficient path planning algorithms and effective exploration strategies. Although an abundance of research exists on these two topics, less so exists about the combination of the two and their effect on each other. Therefore, the aim of this thesis was to integrate two state-of-the-art components from each respective area of research into a functioning system. This was done with the aim of studying the interconnectivity between these components while also documenting the integration process and identifying important considerations for similar future endeavours. Evaluations of the system showed that it performed with surprisingly good accuracy although it was severely lacking in robustness. Integration efforts showed good promise; however, it is clear that the two fields are heavily linked and need to be considered in a mutual context when it comes to a complete integrated system. / Förmågor som lokalisering och navigering är inom robotik förutsättande för att kunna möjliggöra en fullt autonom agent. Att för en agent kunna lokalisera sig i en okänd miljö är ett omfattande och brett studerat ämne, och ett huvudfokus inom ämnet är Simultaneous Localization and Mapping (SLAM) som avser lokalisering som sker parallellt med en aktiv kartläggning av omgivningen. Stora framsteg har gjorts inom detta område genom åren, vilket har resulterat i robusta och exakta system för robotlokalisering. Motsvarande framsteg inom robotnavigering har dessutom möjliggjort effektiva algoritmer och strategier för path planning och autonom utforskning. Trots den stora mängd forskning som existerar inom ämnena lokalisering och navigation var för sig, är samspelet mellan de två områdena samt möjligheten att sammankoppla de två aspekterna mindre studerat. I syfte att undersöka detta var målet med detta examensarbete således att integrera två toppmoderna system från de respektive områdena till ett sammankopplat system. Utöver att förmågorna och prestandan hos det integrerade systemet kunde studeras, genomfördes studien med avsikten att möjliggöra dokumentering av integrationsprocessen samt att viktiga insikter kring integrationen kunde identifieras i syfte att främja framtida studier inom samspelet mellan områdena lokalisering och navigation. Utvärderingar av det integrerade systemet påvisade en högre nivå av noggrannhet än förväntat, men fann en markant avsaknad av robusthet. Resultaten från integrationsarbetet anses lovande, och belyser framförallt att finns ett starkt samband mellan de två områdena samt att de bör beaktas i ett gemensamt kontext när de avses användas i ett komplett integrerat system.

Industrial automation is critical in today's industry. The majority of new scientific and technological advancements are either enabling technologies or industrial automation application areas. In the past, the two main forms of control systems were distributed control systems (DCS) and programmable logic controllers (PLCs). PLCs have been referred as the "brain" of production systems because they provide the capacity to meet interoperability, reconfigurability, and portability criteria. Today's industrial automation systems rely heavily on control software to ensure that the automation process runs smoothly and efficiently. Furthermore, requirements like flexibility, adaptability, and robustness add to the control software's complexity. As a result, new approaches to building control software are required. The International Electrotechnical Commission attempted to meet these new and impending demands with the new IEC 61499 family of standards for distributed automation systems. The IEC 61499 standard specifies a high-level system design language for distributed data and control. With the advancement of these technologies like edge/fog computing and IIoT, how the control software in future smart factory managed is discussed here. This study aims to do a systematic literature review on PLC, software containers, edge/fog computing and IIoT for future industrial use. The objective is to identify the correspondence between the functional block (IEC 61499) and the container technology such as Docker. The impact of edge computing and the internet of things in industrial automation is also analysed. Since the aim is to do a comparative study, a qualitative explorative study is done, with the purpose to gather rich insight about the field. The analysis of the study mainly focused on four major areas such as deployment, run time, performance and security of these technologies. The result shows that containerisation or container based solutions is the basis for future automation as it outperforms virtual machines in terms of deployment, run time, performance and security.

While autonomous robot systems are becoming increasingly common, their usage is still mostly limited to rather simple tasks. This primarily results from the need for manually programming the execution plans of the robots. Instead, as shown in this thesis, their behavior can be automatically generated from a given goal specification. This forms the basis for providing formal guarantees regarding optimality and satisfaction of the mission goal specification and creates the opportunity to deploy these robots in increasingly sophisticated scenarios. Well-defined robot capabilities of comparably low complexity can be developed independently from a specific high-level goal and then, using a behavior planner, be automatically composed to achieve complex goals in a verifiably correct way. Considering multiple robots introduces significant additional planning complexity. Not only actions need to be planned, but also allocation of parts of the mission to the individual robots needs to be considered. Classically, either are planning and allocation seen as two independent problems which requires to solve an exponential number of planning problems, or the formulation of a joint team model leads to a product state space between the robots. The resulting exponential complexity prevents most existing approaches from being practically useful in more complex and realistic scenarios. In this thesis, an approach is presented to utilize the interplay of allocation and planning, which avoids the exponential complexity for independently executable parts of the mission specification. Furthermore, an approach is presented to identify these independent parts automatically when only being given a single goal specification for the team. This bears the potential of improving the efficiency to find an optimal solution and is a significant step towards the application of formal multi-robot behavior planning to real-world problems. The effectiveness of the proposed methods is therefore illustrated in experiments based on an existing office environment and in realistic scenarios. / Även om autonoma robotsystem blir allt vanligare är deras användning fortfarande mestadels begränsad till ganska enkla uppgifter. Detta beror främst på att manuell programmering av robotarnas exekveringsplaner behövs. Istället, som det visas i denna avhandling, kan deras beteende genereras automatiskt från en given målspecifikation. Detta utgör fundamentet för att ge en formell garanti att det resulterande beteendet är optimalt och uppdragsmålspecifikationen är uppfylld. Därför skapar det möjlighet att använda dessa robotar i alltmer sofistikerade scenarier. Väldefinierade robotkompetenser med relativt låg komplexitet kan utvecklas oberoende av ett specifikt mål på hög nivå och sedan sammansättas automatiskt med hjälp av en beteendeplanerare för att uppnå komplexa mål på ett verifierbar korrekt sätt. Om det handlar om flera robotar så introduceras ytterligare planeringskomplexitet som är betydande. Inte bara åtgärder behöver planeras, men även fördelning av uppdragets olika delar till de enskilda robotarna måste hanteras. Traditionellt anses planering och allokering som två oberoende problem som kräver att man löser ett exponentiellt antal planeringsproblem, eller så leder formuleringen av en gemensam modell för hela gruppen till ett produkttillståndsutrymme mellan robotarna. Den resulterande exponentiella komplexiteten förhindrar att de flesta befintliga metoderna är praktiskt användbara i mer komplexa och realistiska scenarier. I denna avhandling presenteras ett tillvägagångssätt för att utnyttja samspelet mellan allokering och planering, som undviker exponentiell komplexitet för oberoende exekverbara delar av uppdragsspecifikationen. Dessutom presenteras ett tillvägagångssätt för att automatiskt identifiera dessa oberoende delar när endast en enda målspecifikation ges för arbetslaget. Detta har potential att förbättra effektiviteten för att hitta en optimal lösning och är ett viktigt steg mot tillämpningen av formell multi-robot-beteendeplanering för realistiska problem. Effektiviteten av de föreslagna metoderna illustreras därför i experiment baserade på en befintlig kontorsmiljö och i realistiska scenarier. / <p>QC 20170928</p>

Postpartum Depression (PPD) affects 8-15 percent of new mothers in Sweden every year. The majority of PPD cases go undetected, and only a few percent receive adequate care. New ways to detect and diagnose PPD are required. In my previous work, PPD experts expressed willingness to integrate social assistive robots (SARs) into their medical team. Moreover, we disclosed that future work was needed to include patients' perspectives on the subject. This thesis aims to provide insights from mothers with experience of mental health issues in relation to their pregnancy to elicit perceptions, attitudes and opinions towards SARs in PPD healthcare. Semi-structured interviews with participants (n=10) and a generative design activity were conducted and analyzed using thematic analysis. The results suggest split opinions towards SARs in PPD healthcare. Participants expressed healthcare needs, and how SARs could be used to address these issues. Opinions towards the robot's appearance, including characteristics, gender and ethnicity were also discussed. Future work including midwives, child health nurses' and perspectives is needed, as well as a larger sample of women to validate the robot’s appearance, gender and characteristics.

In the field of robotics, Artificial Intelligence based on Machine Learning and Deep Learning is a key enabling technology for robot navigation, interaction and task execution. Despite significant advances in AI, there remain notable hurdles in deploying AI algorithms in real-time safety-critical systems such as robotic systems, to achieve high levels of safety assurance in the presence of stringent hardware resource constraints. For Deep Learning-based perception based on computer vision, adversarial patch attacks have emerged as a potent technique for fooling classifiers by placing a patch on the input image, and defence techniques against such attacks is an active research topic. In this thesis, we address two research questions: RQ1: How do adversarial patch defence algorithms perform on different hardware platforms with varying computing capabilities? RQ2: How do heuristics-based adversarial defence algorithms perform with increasing patch sizes? To address RQ1, this thesis measures and compares among six well-known adversarial patch defence algorithms, including 14 models, on three different hardware platforms. Their performance in accuracy and processing time are compared and trade-offs are presented. To address RQ2, this thesis measures and compares accuracy and timing performance of a representative heuristics-based algorithm with increasing patch sizes, and compares the performance of masking-alone mitigation and Generative Adversarial Network (GAN)-based mitigation. The research results of this thesis aim to serve as a useful reference for the practical deployment of adversarial patch defence algorithms in robotic systems.

In the realm of autonomous underwater exploration and surveillance, the capability of Autonomous Underwater Vehicles (AUV) to make informed decisions in dynamic and often unpredictable environments is crucial. This thesis investigates how a software architecture can be designed and implemented for autonomous decision making on the remotely operated vehicle BlueROV2. Using raw sensor data and external information inputs, situational awareness (SA) is achieved. This is used as a basis for real-time decision making during a search mission that is set up. The thesis aims for the AUV to base its actions on the SA. This is demonstrated through a search scenario in which the AUV is assigned the task of locating a number of targets in an underwater environment. To solve the task, the AUV is able to use two different methods to solve the same problem. Either by using the sonar to locate points of interest and subsequently investigate those locations or systematically traversing the entire search area using a camera to search. The trade-off between thoroughness and time consumption makes the methods favourable in different situations. Four modules are developed and implemented in the architecture. A sensor module that collects the data, an SA module that refines the data, a decision module that handles decisions in two layers and an execution module that performs the actions. A modular architecture is developed, which enables the AUV to decide which search method to prioritise and interpret the surroundings to differentiate between targets and decoy objects. Dynamic decision-making is accomplished by changing search strategies during missions and returning when all targets are found. The targets are located with a precision high enough to make out individual targets and keep track of previously seen targets. The research contributes to the field of autonomous decision making by laying a foundation for adding more sophisticated algorithms for future problem-solving. The implemented algorithms are placeholders to showcase the capabilities developed, and the architecture is modular to enable future changes and extensions of the work.

Formation planning and control for multi-agent robotic systems enables tasks to be completed more efficiently and robustly compared to using a single agent. Applications are found in fields such as agriculture, mining, autonomousvehicle platooning, surveillance, space exploration, etc. In this paper, a complete framework for formation path planning for holonomic ground robots in an obstacle-rich environment is proposed. The method utilizes the Fast Marching Square (FM2) path planning algorithm, and a formation keeping approach which falls within the Leader-Follower category. Contrary to most related works, the role of leader is dynamically assigned to avoid unnecessary rotation of the formation. Furthermore, the roles of the followers are also dynamically assigned to fit the current geometry of the formation. A flexible spring-damper system prevents inter-robot collisions and helps maintain the formation shape. An obstacle avoidance step at the end of the pipeline keeps the spring forces from driving robots into obstacles. The framework is tested on a formation consisting of three Unitree Go1 quadruped robots, both in the Gazebo simulation environment and in lab experiments. The results are successful and indicate that the method is feasible, although further work is needed to adjust the role assignment for larger formations, combine the framework with Simultaneous Localization and Mapping (SLAM) and provide a more robust handling of dynamic obstacles.