Automation is a key enabler of modern manufacturing. It can result in increased flexibility, safety, and reduced costs. Internal logistics processes have thus far been automated to a lower level compared to other processes in a manufacturing system. Autonomous Mobile Robots (AMRs) are an emerging technology with potential to automate internal logistics processes, but there exists limited literature on their integration and implementation. In a literature study, no papers were identified that directly studied the mechanical challenges that arise in the implementation of AMRs, and none suggested solutions to overcome those challenges. The industry has shown interest in exploring the benefits of employing mixed AMR fleets where different types of AMRs, with different mechanical specifications, can work together and share tasks.  This thesis aims to contribute to the flexible integration of AMRs into manufacturing lines, especially in the context of mixed AMR fleets. A self-adjusting logistic cart is the outcome of a systematic systems engineering concept development and design process. The advantage of flexible logistic infrastructure, regardless of the type of fleet employed (i.e., mixed or homogenous), is that it can provide the industry with freedom and time to learn which AMRs and fleets best fit their needs. The novel concept provides the industry with flexibility to optimise their line feeding processes without the added problem-solving and time required to adapt and/or update logistic infrastructure such as carts. In addition to the concept development, a generalised guide for the design of carts used with AMRs is contributed to facilitate future design for AMRs studies. The concept and prototype design are evaluated and discussed. Further iterations of the design process are needed for the concept to reach maturity. Future studies into the benefits and shortcomings of the concept are encouraged. / Automation är en viktig faktor för modern tillverkning. Den kan resultera i ökad flexibilitet, säkerhet och minskade kostnader. Interna logistikprocesser har hitintills automatiserats till en lägre nivå jämfört med andra processer i tillverkningssystemen. Autonoma Mobila Robotar (AMR) är en framväxande teknik med potential att automatisera interna logistikprocesser, men det finns begränsad litteratur som beskriver deras integration och implementering. I en litteraturstudie identifierades inga artiklar som direkt studerade de mekaniska utmaningar som uppstår vid en implementering av AMR, och inga förslag till lösningar för att övervinna dessa utmaningar. Branschen har visat intresse för att utforska fördelarna med att använda blandade AMR-flottor där olika typer av AMR med olika mekaniska specifikationer arbetar tillsammans och delar uppgifter.  Denna avhandling syftar till att bidra med kunskap om flexibel integration av AMR i tillverkningslinjer, särskilt i system med blandade flottor. En självjusterande logistikvagn är resultatet av systematisk konceptutveckling och konstruktionsprocess. Fördelen med en flexibel logistikinfrastruktur, oavsett vilken typ av flotta som används (dvs. blandad eller homogen), är att den kan ge industriell frihet och tid att lära sig vilka typer av AMR och flottor som bäst passar varje företags behov. Konceptet ger branschen flexibilitet att optimera sina linjematningsprocesser utan den extra problemlösning och den tid som krävs för att anpassa och/eller uppdatera logistikinfrastruktur med vagnar. Förutom konceptutvecklingen bidrar en generell guide för konstruktion av vagnar, som används med AMR, till att underlätta framtida AMR-studier. Konceptet och prototypkonstruktionen utvärderas och diskuteras. Ytterligare iterationer av utvecklings- och konstruktionsprocessen behövs för att konceptet ska bli moget. Framtida studier av fördelarna och bristerna hos det föreslagna konceptet uppmuntras.

<p dir="ltr">The emergence of Autonomous Mobile Robots (AMR) signifies a pivotal shift in vehicle-based material handling systems, demonstrating their effectiveness across a broad spectrum of applications. Advancing beyond the traditional Automated Guided Vehicles (AGV), AMRs offer unprecedented flexibility in movement, liberated from electromagnetic guidance constraints. Their decentralized control architecture not only enables remarkable scalability but also fortifies system resilience through advanced conflict resolution mechanisms. Nevertheless, transitioning from AGV to AMR presents intricate challenges, chiefly due to the expanded complexity in path planning and task selection, compounded by the heightened potential for conflicts from their dynamic interaction capabilities. This dissertation confronts these challenges by fully leveraging the technological advancements of AMRs. A kinematic-enabled agent-based simulator was developed to replicate AMR system behavior, enabling detailed analysis of fleet dynamics and interactions within AMR intralogistics systems and their environments. Additionally, a comprehensive fleet management protocol was formulated to enhance the throughput of AMR-based intralogistics systems from an integrated perspective. A pivotal discovery of this research is the inadequacy of existing path planning protocols to provide reliable plans throughout their execution, leading to task allocation decisions based on inaccurate plan information and resulting in false optimality. In response, a novel machine learning enhanced probabilistic Multi-Robot Path Planning (MRPP) protocol was introduced to ensure the generation of dependable path plans, laying a solid foundation for task allocation decisions. The contributions of this dissertation, including the kinematic-enabled simulator, the fleet management protocol, and the MRPP protocol, are intended to pave the way for practical enhancements in autonomous vehicle-based material handling systems, fostering the development of solutions that are both innovative and applicable in industrial practices.<br></p>

Under senare år har vi med hjälp av robotar som använder rörelseplanering kunnat automatisera olika processer och uppgifter. Idag finns det väldigt få strategier för lokal rörelseplanering vid jämförelse med global rörelseplanering. Syftet med det här projektet har varit att analysera tre strategier för lokal rörelseplanering, dessa har varit Dynamic Window Approach (DWA), Elastic Band (Eband) och Timed Elastic Band (TEB).I projektet har styrkor, svagheter, beteenden och förbättringsmöjligheter för respektive strategi studerats närmare genom att utföra olika simulerade tester. I testerna mätes tid för att nå mål, antal kollisioner och antalet gånger som målet nåddes. Under projektet användes en virtuell allriktad robot från ABB för att testa strategierna. Testerna genomfördes på ett så rättvist sätt som möjligt, där alla strategier fick samma antal försök och hade samma information om robotens begränsningar.Resultatet visar att TEB är den snabbaste strategin, följt av DWA och sista Eband som var den långsammaste strategin. TEB var också den strategi som presterade bäst vid dynamiska hinder, däremot var den också den strategi som kolliderade mest i testerna, medan Eband kolliderade minst. / In recent years, we have been able to automate various processes and tasks with the help of robots that use motion planning. Today, there are very few strategies for local motion planning when compared to global motion planning. The purpose of this project has been to analyze three strategies for local motion planning, these have been Dynamic Window Approach (DWA), Elastic Band (Eband) and Timed Elastic Band (TEB).In the project, strengths, weaknesses, behaviours and opportunities for improvement for each strategy have been studied in more detail by performing various simulated tests. The tests measure time to reach the goal, the number of collisions and the number of succeeding attempts. During the project, a virtual omni-directional robot from ABB was used to perform the tests. The tests were performed in as fair a way as possible, where all strategies got the same number of attempts and had the same information about the robot's limitations.The results show that TEB is the fastest strategy, followed by DWA and last Eband that was the slowest strategy. TEB was also the strategy that performed best in dynamic obstacles, however, it was also the strategy that collided most of the tests, while Eband collided the least.

Les travaux décrits dans cette thèse apportent une contribution au problème de la navigation autonome de robots mobiles dans un contexte de vision indoor. Il s’agit de chercher à concilier les avantages des différents paradigmes d’architecture de contrôle et des stratégies de navigation. Ainsi, nous proposons l’architecture RHIZOME (Robotic Hybrid Indoor-Zone Operational ModulE) : une architecture unique de contrôle robotique mettant en synergie ces différentes approches en s’appuyant sur un système neuronal. Les interactions du robot avec son environnement ainsi que les multiples connexions neuronales permettent à l’ensemble du système de s’adapter aux conditions de navigation. L’architecture RHIZOME proposée combine les avantages des approches comportementales (e.g. rapidité de réaction face à des problèmes imprévus dans un contexte d’environnement dynamique), et ceux des approches délibératives qui tirent profit d’une connaissance a priori de l’environnement. Cependant, cette connaissance est uniquement exploitée pour corroborer les informations perçues visuellement avec celles embarquées. Elle est représentée par une séquence de symboles artificiels de navigation guidant le robot vers sa destination finale. Cette séquence est présentée au robot soit sous la forme d’une liste de paramètres, soit sous la forme d’un plan. Dans ce dernier cas, le robot doit extraire lui-même la séquence de symboles à suivre grâce à une chaine de traitements d’images. Ainsi, afin de prendre la bonne décision lors de sa navigation, le robot traite l’ensemble de l’information perçue, la compare en temps réel avec l’information a priori apportée ou extraite, et réagit en conséquence. Lorsque certains symboles de navigation ne sont plus présents dans l’environnement de navigation, l’architecture RHIZOME construit de nouveaux lieux de référence à partir des panoramas extraits de ces lieux. Ainsi, le robot, lors de phases exploratoires, peut s’appuyer sur ces nouvelles informations pour atteindre sa destination finale, et surmonter des situations imprévues. Nous avons mis en place notre architecture sur le robot humanoïde NAO. Les résultats expérimentaux obtenus lors d’une navigation indoor, dans des scenarios à la fois déterministes et stochastiques, montrent la faisabilité et la robustesse de cette approche unifiée. / The work described in this dissertation is a contribution to the problem of autonomous indoor vision-based mobile robot navigation, which is still a vast ongoing research topic. It addresses it by trying to conciliate all differences found among the state-of-the-art control architecture paradigms and navigation strategies. Hence, the author proposes the RHIZOME architecture (Robotic Hybrid Indoor-Zone Operational ModulE) : a unique robotic control architecture capable of creating a synergy of different approaches by merging them into a neural system. The interactions of the robot with its environment and the multiple neural connections allow the whole system to adapt to navigation conditions. The RHIZOME architecture preserves all the advantages of behavior-based architectures such as rapid responses to unforeseen problems in dynamic environments while combining it with the a priori knowledge of the world used indeliberative architectures. However, this knowledge is used to only corroborate the dynamic visual perception information and embedded knowledge, instead of directly controlling the actions of the robot as most hybrid architectures do. The information is represented by a sequence of artificial navigation signs leading to the final destination that are expected to be found in the navigation path. Such sequence is provided to the robot either by means of a program command or by enabling it to extract itself the sequence from a floor plan. This latter implies the execution of a floor plan analysis process. Consequently, in order to take the right decision during navigation, the robot processes both set of information, compares them in real time and reacts accordingly. When navigation signs are not present in the navigation environment as expected, the RHIZOME architecture builds new reference places from landmark constellations, which are extracted from these places and learns them. Thus, during navigation, the robot can use this new information to achieve its final destination by overcoming unforeseen situations.The overall architecture has been implemented on the NAO humanoid robot. Real-time experimental results during indoor navigation under both, deterministic and stochastic scenarios show the feasibility and robustness of the proposed unified approach.