<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>

Developments of commercial self-driving vehicle (SDV) technology has a potential for a paradigm shift in traffic control technology. Contrary to some previous research approaches, this research argues that, as any other technology, traffic control technology for SDVs should be developed having in mind improved quality of life through a sustainable developmental approach. Consequently, this research emphasizes upon the social perspective of sustainability, considering its neglect in the conventional control principles, and the importance of behavioral considerations for accurately predicting impacts upon economic or environmental factors. The premise is that traffic control technology can affect the distribution of advantages and disadvantages in a society, and thus it requires a framework of social justice. The framework of social justice is inspired by John Rawls' Theory of Justice as fairness, and tries to protect the inviolability of each user in a system. Consequently, the control objective is the distribution of delay per individual, considering for example that the effect of delay is not the same if a person is traveling to a grocery store as opposed to traveling to a hospital. The notion of social justice is developed as a priority system, with end-user responsibility, where user is able to assign a specific Priority Level for each individual trip with SDV. Selected Priority Level is used to determine the right-of-way for each self-driving vehicle at an intersection. As a supporting mechanism to the priority system, there is a structure of non-monetary Priority Credits. Rules for using Priority Credits are determined using knowledge from social science research and through empirical evaluation using surveys, interviews, and web-based experiment. In the physical space, the intersection control principle is developed as hierarchical self-organization, utilizing communication, sensing, and in-vehicle technological capabilities. This distributed control approach should enable robustness against failure, and scalability for future expansion. The control mechanism has been modeled as an agent-based system, allowing evaluation of effects upon safety and user delay. In conclusion, by reaching across multiple disciplines, this development provides the promise and the challenge for evolving SDV control technology. Future efforts for SDV technology development should continue to rely upon transparent public involvement and understanding of human decision-making. / Ph. D.

<p dir="ltr">Unmanned surface vehicles (USVs) offer a versatile platform for various maritime applications, including research, surveillance, and search-and-rescue operations. A critical capability for USVs is maintaining position (station keeping) in dynamic environments and coordinating movement with other USVs (formation control) for collaborative missions. This thesis investigates control strategies for USVs operating in challenging conditions. </p><p dir="ltr">The initial focus is on evaluating traditional control methods like Backstepping and Sliding Mode controllers for station keeping in simulated environments with disturbances. The results from these tests pointed towards the need for a more robust control technique, like deep-learning based control for enhanced performance. </p><p dir="ltr">The thesis then explores formation control, a crucial aspect of cooperative USV missions. A vision-based passive control strategy utilizing a virtual leader concept is proposed. This approach leverages onboard cameras to detect markers on other USVs, eliminating the need for direct communication and potentially improving scalability and resilience. </p><p dir="ltr">Then the thesis presents vision-based formation control architecture and the station keeping controller evaluations. Simulation results are presented, analyzed, and used to draw conclusions about the effectiveness of the proposed approaches. Finally, the thesis discusses the implications of the findings and proposes potential future research directions</p>

In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, Driver Assistance Systems (DAS) such as cruise control, Anti-Lock Braking Systems (ABS), and Electronic Stability Control (ESC) have become widely popular in the automotive industry. Therefore, vehicle control research attracts attention from both academia and industry, and has been an active area of vehicle research for over 30 years, resulting in impressive DAS contributions. Although current vehicle control systems have improved vehicle safety and performance, there is room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming local driving environment such as terrain roughness, elevation grade, bank angle, curvature, and friction. The local driving environment is measured in advance with a terrain measurement system to provide terrain data. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing the response measurements of a preceding vehicle. The response measurements of a preceding vehicle are acquired through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. The identification method analyzes the response measurements of a preceding vehicle to estimate road data. The estimated road data or the pre-measured road data is used as the upcoming driving environment information for the developed vehicle control system. The metric that objectively quantifies vehicle performance, the Performance Margin, is developed to accomplish the control objectives in an efficient manner. The metric is used as a control reference input and continuously estimated to predict current and future vehicle performance. Next, the predictive control algorithm is developed based on the upcoming driving environment and the performance metric. The developed system predicts future vehicle dynamics states using the upcoming driving environment and the Performance Margin. If the algorithm detects the risks of future vehicle dynamics, the control system intervenes between the driver's input commands based on estimated future vehicle states. The developed control system maintains vehicle handling capabilities based on the results of the prediction by regulating the metric into an acceptable range. By these processes, the developed control system ensures that the vehicle maintains stability consistently, and improves vehicle performance for the near future even if there are undesirable and unexpected driving circumstances. To implement and evaluate the integrated systems of this work, the real-time driving simulator, which uses precise real-world driving environment data, has been developed for advanced high computational vehicle control systems. The developed vehicle control system is implemented in the driving simulator, and the results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS. / Ph. D. / In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, cruise control, Anti-Lock Braking Systems, and Electronic Stability Controls have become widely popular in the automotive industry. Although vehicle control systems have improved vehicle safety and performance, there is still room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming driving conditions such as road roughness, elevation grade, bank angle, and curvature. The driving environment is measured in advance with a terrain measurement system. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing a preceding vehicle’s response to the road. The combined road data is used as the upcoming driving environment information. The measurement that indicates vehicle performance, the Performance Margin, is developed to accomplish the research objectives. It is used in the developed control system, which predicts future vehicle performance. If the system detects future risks, the control system will intervene to correct the driver’s input commands. By these processes, the developed system ensures that the vehicle maintains stability, and improves vehicle performance regardless of the upcoming and unexpected driving conditions. To implement and evaluate the proposed systems, a driving simulator has been developed. The results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS.

Ce travail porte sur la conception d’un système capable d’effectuer des manœuvres de parking automatique plus polyvalent que ceux actuellement commercialisés, tout en conservant une définition technique des capteurs extéroceptifs limités en prix et en gabarit. Un cas d’usage typique est de permettre au véhicule de se rendre automatiquement dans la zone de garage du domicile de son propriétaire, cette fonction est classiquement appelée voiturier autonome à domicile. Partant de l’existant et connaissant les performances attendues, une architecture système et une architecture fonctionnelle ont été tracées. Cela a permis de constituer un ensemble de fonctions interconnectées qui ont participé dans la création d’une architecture software modulaire ainsi que dans la création des interfaces de connexion au véhicule prototype. Dans un premier temps, nous explorons la problématique de la détection d’obstacles. Partant d’un système propriétaire fermé de capteurs ultrason, nous avons réussi à réaliser une carte d’obstacle à un niveau de précision supérieur au produit d’origine. Une augmentation de la limite de détection des capteurs ultrason a été réalisée utilisant une technique Structure from Motion. Ces informations d’occupation ont été exploitées par la suite pour traiter la problématique de détection du couloir de navigation. Dans un second temps, la fonction de localisation du véhicule est abordée. Trois techniques de localisation collaborent pour une robustesse de fonctionnement continu : la localisation odométrique, la localisation par appariement des grilles d’occupation et la localisation par appariement entre une image actuelle et une base d’images adaptée à notre besoin et améliorée en termes de temps de calcul. Enfin, nous nous sommes intéressés à la problématique de navigation du véhicule. Nous avons considéré résolue la problématique de contrôle des actionneurs pour le suivi d’une trajectoire donnée et nous nous sommes concentrés sur la création d’une trajectoire admissible. Nous avons développé une technique de planification locale pour l’évitement d’un d’obstacles non cartographiés. Pour la construction de trajectoire nous avons utilisé des courbes à géométrie connue et avons montré qu’en utilisant trois clothoïdes et éventuellement deux arcs de cercle (si le braquage maximal est atteint) il est possible de créer des trajectoires à courbure continue adaptées à notre situation. Nous avons montré que l’utilisation d’une carte d’obstacles nous permet de prédire plus en avance de la possibilité d’emprunter un certain couloir de navigation. Chacune des parties de ce travail a fait l’objet de validations en simulation mais aussi sur des données réelles démontrant la pertinence des approches proposées quant à l’application visée. / This work covers the conception of a system capable to do automatic parking maneuvers more versatile than those already commercialized, respecting the technical definition of exteroceptive sensors limited by costs and weight. A typical use case is to set a vehicle to park autonomously in the parking lot of a home, function generally called autonomous home valet parking. Taking from the existing and knowing the expected performances, a system architecture and a functional architecture were drawn. This allowed to compose an assembly of interconnected functions that participated in the creation of modular software architecture, as well as in the creation of connection interfaces with the prototype vehicle. First, we explored the obstacle detection problem. Having a closed property system with ultrasonic sensors, we managed to build an obstacle map with a higher precision level than the build-in product. An increasing limit detection of the ultrasonic sensors was developed using the Structure from Motion technique. This obstacle occupancy information was exploited afterwards in order to solve the detection problem of the navigation corridors. Second, the vehicle localization is addressed. Three localization techniques work for a continuous functioning robustness: the localization by odometry, the localization by occupancy grid map matching and the localization by comparing the current image with the images stored in a database adapted to our needs and improved by computing means. Last, we interested in the vehicle navigation problem. We considered solved the actuator control problem for the tracking of a given trajectory and we concentrated on an admissible trajectory planning. We developed a local path planning technique for avoiding the unmapped obstacles. In order to build the trajectory we used curves of known geometry and we proved that by using clothoides and eventually two circle arches (if maximum steering angle achieved) it is therefore be possible to create trajectories with continuous curves adapted to our situation. We confirmed that using an obstacle map will allow us to predict forehead the possibility to take a specific navigation corridor. Each part of this work was validated in simulation as well as on real data, proving the pertinence of the proposed approaches for the intended application.

Cars are becoming increasingly automated and expected to become fully autonomous in the near future. How will this a ect the car and its position of a symbol of freedom? This thesis investigates how an autonomous vehicle can evolve this symbolic value and be adapted to the use case of an explorative road trip. Based on learnings from travellers and experts the starting point has been the positive experience of a road trip in a conventional vehicle. The target has been to enhance the current experience and create an even more spontaneous and explorative atmosphere with the help of a future scenario and emerging technology. This project gives an example of an interface that supports and en- courages spontaneity which lets the travellers direct and control the vehicle intuitively in order to explore and enjoy what they nd during their journey. Furthermore it extends the travel experience beyond what a road trip has been by connecting travellers to locals.

The global population is growing, and more people than before are moving to cities. This creates a need for increased building efficiency and possibility to work in remote environments. On today’s construction sites, there is a need to able to organize the site in a better way. In the future, autonomous vehicles will instead find it difficult to localize materials on a construction site. The autonomous vehicles can localize themselves with cameras and sensors, but they do not know how to localize the materials and items. This report is based on a project where Volvo Construction Equipment acted as a customer and the project was performed by students from Blekinge Institute of Technology and Stanford University. The prompt for this project was “From elephants to ants – from Earth to Mars” and would later be interpreted as finding a solution for the future that will be able to function without human’s intervention. From this project, this report was created. The following research questions for this report were: • How can workers locate building materials on today’s construction sites? • How will autonomous vehicles be able to locate material without human assistance in future construction sites? To solve these problems a design-process started, using an engineering design method. This method was chosen because of the type of problem. In engineering, the problem is identified to create a solution to the problem, comparing to when studying science, a question should be answered. The outcome from this report is a Product Service System (PSS) for a tracking system and a device for materials on today’s and future construction sites. When this solution was created no economic aspects were considered. Also, the focus of this report is the first steps of going from today’s construction sites to the future construction sites where autonomous vehicles will be used. The result from this research shows that the same problem of organizing a construction site is a pattern that can be seen in the majority of the sites that were visited during field works. Also, the workers today have little trust in the autonomous vehicles which is a result of lacking information and communication within companies. Furthermore, to be able to move to an autonomous future the mindset and attitude has to be changed. The collected data was analysed, and the outcome was a tracing system that will enable, both humans and machines, to localize materials on today’s and future construction sites. With this solution, today’s workers can track their materials wherever it is placed, without any need of changing the site. The autonomous vehicles will be able to use the tags to localize materials when there are no humans around. / Den globala befolkningen växer och fler flyttar till städerna än tidigare. Detta skapar ett behov av ökad effektivitet i byggbranschen och möjlighet till arbete i avlägsna miljöer. På dagens byggarbetsplatser är det nödvändigt att kunna organisera platsen på ett bättre sätt. I framtiden kommer de autonoma fordonen få det svårare att lokalisera material på en byggarbetsplats. De autonoma fordonen kan lokalisera sig med kameror och sensorer, men de vet inte hur man lokaliserar material och föremål. Rapporten bygger på ett projekt där kunden var Volvo Construction Equipment och projektet utfördes av studenter från Blekinge Tekniska Högskola och Stanford University. Prompten för projektet löd "Från elefanter till myror - från jorden till mars" och som senare tolkades till att finna en lösning för framtiden som kommer att kunna fungera utan mänsklig påverkan. Från detta projekt skapades denna rapport. Följande forskningsfrågor skulle besvaras: • Hur kan arbetare lokalisera byggmaterial på dagens byggarbetsplatser? • Hur kommer autonoma fordon kunna lokalisera material utan mänsklig hjälp på de framtida byggarbetsplatserna? För att lösa dessa problem startades en designprocess, med vald ingenjörsmetod. Denna metod valdes på grund av typen av problem. I ingenjörsmetoden identifieras problemet för att skapa en lösning till problemet, jämfört men en vetenskaplig metod, där en fråga besvaras. Resultatet från denna rapport är ett produkttjänstesystem (PSS) för ett spårningssystem för att kunna spåra material på dagens och framtida byggarbetsplatser. När denna lösning skapades togs det ingen hänsyn till de ekonomiska aspekterna. Fokus på denna rapport är de första stegen för att gå från dagens byggarbetsplatser mot de framtida byggplatserna där autonomiska fordon kommer att användas. Resultatet av forskningen visade att det finns ett stort behov av att organisera de olika byggarbetsplatserna som besöktes under studiebesöken. Arbetarna har idag ett litet förtroende för de autonoma fordonen som är ett resultat av bristande information och kommunikation inom företagen. För att kunna gå till en autonom framtid måste tankesätt och attityd ändras. Den samlade data analyserades och resultatet var ett spårningssystem som gör det möjligt för både människor och maskiner att lokalisera material på dagens och framtida byggarbetsplatser. Med denna lösning kan dagens arbetare enkelt spåra materialet, utan att behöva omstrukturera arbetsplatsen. De autonoma fordonen kommer kunna använda spårningssystem för att kunna lokalisera material när det inte finns några människor till hands.

<div>In this thesis, we explore different methods to enhance the safety and robustness for autonomous systems. We achieve this goal using concepts and tools from reachability analysis and control barrier functions. We first take on a multi-player reach-avoid game that involves two teams of players with competing objectives, namely the attackers and the defenders. We analyze the problem and solve the game from the attackers' perspectives via a moving horizon approach. The resulting solution provides a safety guarantee that allows attackers to reach their goals while avoiding all defenders. </div><div><br></div><div>Next, we approach the problem of target re-association after long-term occlusion using concepts from reachability as well as Bayesian inference. Here, we set out to find the probability identity matrix that associates the identities of targets before and after an occlusion. The solution of this problem can be used in conjunction with existing state-of-the-art trackers to enhance their robustness.</div><div><br></div><div>Finally, we turn our attention to a different method for providing safety guarantees, namely control barrier functions. Since the existence of a control barrier function implies the safety of a control system, we propose a framework to learn such function from a given user-specified safety requirement. The learned CBF can be applied on top of an existing nominal controller to provide safety guarantees for systems.</div>

The study aimed to identify and analyse methods for establishing external communication between humans and autonomous/teleoperated machines/vehicles using various Human-Machine Interfaces (HMIs). The study was divided into three phases. The purpose of the first phase was to identify and highlight previously tested/researched methods for establishing external communication by conducting a literature review. The findings from the literature review were categorised into six points of interest: machine indications, test delivery methods, HMI technologies/types, symbols, textual/numerical messages, and colours associated with different indications. Based on these findings, four HMIs (projection, display, LED-strip, and auditory) were selected for evaluation in a virtual reality environment for the second phase of the study, which has the purpose of identifying which of the human-machine interfaces can effectively communicate the intentions of autonomous/teleoperated machines to humans. The results of phase two indicate that the participants preferred projection as the most effective individual HMI, and when given the option to combine two HMIs, projection combined with auditory was the most preferred combination. The participants were also asked to pick three HMIs of their choosing, resulting in the projection, display and audible HMI combination being the preferred option. The evaluation of HMIs in a virtual reality environment contributes to improving dependability and identifying usability issues.  The objective of the third and final phase was to gather all the findings from the previous phases and subsequently refine the report until it was considered finalised. Future work includes enhancing the realism of the VR environment, refining machine behaviour and scenarios, enabling multiple participants to simultaneously interact with the environment, and exploring alternative evaluation methods. Addressing these areas will lead to more realistic evaluations and advancements in human-machine interaction research.

The ability to gather and process information regarding the condition of forest fires is essential to cost-effective, safe, and efficient fire fighting. Advances in sensory and autopilot technology have made miniature unmanned aerial systems (UASs) an important tool in the acquisition of information. This thesis addresses some of the challenges faced when employing UASs for forest-fire perimeter surveillance; namely, perimeter tracking, cooperative perimeter surveillance, and path planning. Solutions to the first two issues are presented and a method for understanding path planning within the context of a forest-fire environment is demonstrated. Both simulation and hardware results are provided for each solution.