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.

[pt] Nesse trabalho, nós apresentamos novas aplicações para alguns controladores robustos, tais como as abordagens SMC e STA. O principal objetivo é conseguir executar uma navegação autônoma precisa em campos agrícolas, usando robôs móveis com rodas, equipados com uma câmera monocular fixa. Primeiro, nós projetamos uma abordagem de controle robusto baseado em servo-visão, a fim de lidar com imprecisões do modelo e perturbações da trajetória no espaço da imagem. Além disso, uma abordagem de controle robusto baseada em cascata, é aplicada, na qual, a malha de realimentação externo está conectada com uma malha de realimentação interna para lidar com os efeitos de todas as fontes de perturbação. Desse modo, uma abordagem robusta de rastreamento de trajetória, baseada em super-twisting, é aplicada para estabilização de movimento afim de garantir o sucesso da tarefa de seguir uma linha de cultivo considerando os efeitos de derrapagem das rodas e derrapagem lateral do veículo. A plataforma ROS-Gazebo, um simulador de robótica de código aberto, foi utilizada para realização de simulações computacionais 3D usando um robô móvel do tipo differentialdrive e um ambiente ad-hoc projetado para cultivo em linha. A eficácia e a viabilidade dos controladores robustos são avaliadas analisando simulações numéricas e métricas de desempenho, tais como: (i) o Erro Quadrático Médio (EQM) e (ii) o Desvio Absoluto Médio (DAM). Além disso, nós veremos nos resultados, que em geral, só é possível ter estabilidade, utilizando os controladores rosbustos. / [en] In this work, we present a new application for some robust controllers, such as SMC and STA approaches. The main idea is to perform autonomous navigation in agricultural fields accurately using wheeled mobile robots, equipped with a fixed monocular camera . Here, we consider the existence of uncertainties in the parameters of the robot-camera system and external disturbances caused by high driving velocities, sparse plants, and uneven terrains. First, we design a robust image-based visual servoing approach to deal with model inaccuracies and trajectory perturbations in the image space. In addition, a cascade-based robust control approach is applied, in which the outer vision feedback loop is connected with an inner pose feedback loop to deal with the effects of all disturbances sources. Then, a robust trajectory tracking approach based on the super-twisting algorithm is applied for motion stabilization to ensure the successful execution of row crop following tasks under wheel slippage and vehicle sideslip. ROSGazebo platform, an open-source robotics simulator, was used to perform 3D computer simulation using a differential-drive mobile robot and an adhoc designed row-crop environment. The effectiveness and feasibility of the robust controllers are evaluated by analyzing numerical simulations and performance metrics, such as: (i) the root-mean square error (RMSE) and (ii) the mean-absolute deviation (MAD). Furthermore, we will see in results, that in general, it is only possible to have stability, using robust controllers.

No description available.

With the development of autonomous vehicles, more and more technology is introduced into the automotive industry. Ethernet has found its way into the vehicle network, and it is forced to coexist with the well-established CAN bus. In terms of data acquisition, the presence of a mixed network brings challenges with significant changes in network architecture. This thesis explores CAN-Ethernet gateways as a replacement for the PCIe bus CAN transceivers utilized in today's logging systems, with the purpose to improve the adaptability of the autonomous logging system. A CAN-Ethernet gateway was implemented using Kvaser's DIN Rail SE400S-X10in an experimental comparison against the PCIe logging solution, including both classical CAN and CAN FD communication. In addition, a case study on the benefits and drawbacks with implementing an Ethernet architecture was performed, utilizing semi-structured interviews. It was concluded that a CAN-Ethernet gateway provides a robust solution in relation to data loss. Throughout the tests, the message loss rate was 0% for both logging solutions. However, CAN-Ethernetlogging introduced additional delay into the system. For the tests on a truck simulation rig the mean additional delay from a CAN-Ethernet gateway, compared to the existing PCIe-CAN logging, was 2 ms. Moreover, some spikes occurred and in a number of cases it could be up to 6 ms in additional delay compared to the existing PCIe logging. It was also proven difficult to time synchronize the gateway with the autonomous logging system, and unknown delays had an impact. Relevant metrics were obtained from relative measurements of side-by-side logging between the PCIe and CAN-Ethernet communication. The standard deviation and fluctuation of the delay were relevant metrics, since smaller fluctuations made the delay more predictable and real-time compatible. A CAN-Ethernet deployment may create a more complex architecture in general, and as of now has limitations for real-time systems. On the other hand, it may offer significant possibilities in future development of a more adaptable and scalable logging system. / Med utvecklingen av autonoma fordon har mer och mer teknologi introducerats inom fordonsindustrin. Ethernet har funnit sin väg in i fordonsnätverket och tvingas existera sida vid sida med den väletablerade CAN-bussen. För dataloggning orsakar närvaron av ett blandat nätverk (med både CAN och Ethernet) utmaningar i samband med stora förändringar inom nätverksarkitektur. Det här examensarbetet utforskar nätslussar för CAN-Ethernet som en ersättare till PCIe-bussens CAN-sändtagare som används i dagens loggningssystem. En CAN-Ethernet-nätsluss implementerades genom att använda Kvasers DIN Rail SE400S-X10 i en experimentell jämförelse med PCIe-loggningssystemet, och inkluderade både klassisk CAN samt CAN FD kommunikation. I tillägg gjordes en fallstudie om fördelar och nackdelar med att implementera en Ethernet-arkitektur,vilken grundades på semi-strukturerade intervjuer. Slutsatsen var att CAN-Ethernet-nätslussar tillhandahåller en robust lösning i förhållande till dataförlust. Under alla testerna var meddelandeförlusten 0% hos båda loggningsmetoderna. Däremot introducerade CAN-Ethernet-loggning en ökad fördröjning till systemet. För testerna på lastbilsriggsimulatorn var fördröjningen 2 ms jämfört med PCIe-CAN-loggningen. Dessutom förekom spikar i fördröjningen och i vissa fall resulterade fördröjningen i upp mot 6 ms, jämfört mot den befintliga PCIe-loggningen. Det visade sig även vara svårt att tidssynkronisera nätslussen med det autonoma loggningssystemet och okända fördröjningar hade en inverkan. Relevanta mått erhölls från relativa mätningar av jämsides loggning mellan PCIe och CAN-Ethernet kommunikation. Standardavvikelsen och fluktuation av fördröjningen var relevanta mått eftersom mindre fluktuationer resulterade i en mer förutsägbar samt realtidskompatibel fördröjning. Användningen av CAN-Ethernet kan, i allmänhet, resultera i en mer avancerad arkitektur och har i dagsläget begränsningar inom realtidssystem. Å andra sidan kan CAN-Ethernet erbjuda markanta möjligheter inom framtida utveckling av ett mer modulärt och skalbart loggningssystem.

This project’s goal was to assess both the challenges of implementing the Deep Q-Learning algorithm to create an autonomous car in the CARLA simulator, and the driving performance of the resulting model. An agent was trained to follow waypoints based on two main approaches. First, a camera-based approach, which allowed the agent to gather information about the environment from a camera sensor. The image along with other driving features were fed to a convolutional neural network. Second, an approach focused purely on following the waypoints without the camera sensor. The camera sensor was substituted for an array containing the agent’s angle with respect to the upcoming waypoints along with other driving features. Even though the camera-based approach was the best during evaluation, no approach was successful in consistently following the waypoints of a straight route. To increase the performance of the camera-based approach more training episodes need to be provided. Furthermore, both approaches would greatly benefit from experimentation and optimization of the model’s neural network configuration and its hyperparameters.

This thesis addresses the problem of energy management of a hybrid electric power unit for an autonomous vehicle. We introduce, evaluate, and discuss the idea of autonomous-specific energy management strategy. This method is an optimization-based strategy which improves the powertrain fuel economy by exploiting motion planning data. First, to build a firm base for further evaluations, we will develop a high-fidelity system-level model for our case study using MATLAB/Simulink. This model mostly concerns about energy-related aspects of the powertrain and the vehicle. We will derive and implement the equations for each of the model subsystems. We derive model parameters using available data in the literature or online. Evaluation of the developed model shows acceptable conformity with the actual dynamometer data. We will use this model to replace the built-in rule-based logic with the proposed strategy and assess the performance.\par Second, since we are considering an optimization-based approach, we will develop a novel convex representation of the vehicle and powertrain model. This translates to reformulating the model equations using convex functions. Consequently, we will express the fuel-efficient energy management problem as the convex optimization problem. We will solve the optimization problem using dedicated numerical solvers. Extracting the control inputs using this approach and applying them on the high-fidelity model provides similar results to dynamic programming in terms of fuel consumption but in substantially less amount of time. This will act as a pivot for the subsequent real-time analysis.\par Third, we will perform a proof-of-concept for the autonomous-specific energy management strategy. We implement an optimization-based path and trajectory planning for a vehicle in the simplified driving scenario of a racing track. Accordingly, we use motion planning data to obtain the energy management strategy by solving an optimization problem. We will let the vehicle to travel around the circuit with the ability to perceive and plan up to an observable horizon using the receding horizon approach. Developed approach for energy management strategy shows a substantial reduction in the fuel consumption of the high-fidelity model, compared to the rule-based controller. / Thesis / Master of Science in Mechanical Engineering (MSME) / The automotive industry is on the verge of groundbreaking transformations as a result of electrification and autonomous driving. Electrified autonomous car of the future is sustainable, energy-efficient, more convenient, and safer. In addition to the advantages of electrification and autonomous driving individually, the intersection and interaction of these mainstreams provide new opportunities for further improvements on the vehicles. Autonomous cars generate an unprecedented amount of real-time data due to excessive use of perception sensors and processing units. This thesis considers the case of an autonomous hybrid electric vehicle and presents the novel idea of autonomous-specific energy management strategy. Specifically, this thesis is a proof-of-concept, a trial to exploit the motion planning data for a self-driving car to improve the fuel economy of the hybrid electric power unit by adopting a more efficient energy management strategy. With the ever-increasing number of autonomous hybrid electric vehicles, particularly in the self-driving fleets, the presented method shows an extremely promising potential to reduce the fuel consumption of these vehicles.

Recent advancements in time-optimal trajectory planning, control, and state estimation for autonomous vehicles have paved the way for the emerging field of autonomous racing. In the last 5-10 years, this form of racing has become a popular and challenging testbed for autonomous driving algorithms, aiming to enhance the safety and performance of future intelligent vehicles. In autonomous racing, the main goal is to develop real-time algorithms capable of autonomously maneuvering a vehicle around a racetrack, even in the presence of moving opponents. However, as a vehicle approaches its handling limits, several challenges arise for online trajectory planning and control. The vehicle dynamics become nonlinear and hard to capture with low-complexity models, while fast re-planning and good generalization capabilities are crucial to execute optimal maneuvers in unforeseen scenarios. These challenges leave several open research questions, three of which will be addressed in this thesis. The first explores developing accurate yet computationally efficient vehicle models for online time-optimal trajectory planning. The second focuses on enhancing learning-based methods for trajectory planning, control, and state estimation, overcoming issues like poor generalization and the need for large amounts of training data. The third investigates the optimality of online-executed trajectories with simplified vehicle models, compared to offline solutions of minimum-lap-time optimal control problems using high-fidelity vehicle models. This thesis consists of four parts, each of which addresses one or more of the aforementioned research questions, in the fields of time-optimal vehicle trajectory planning, control and state estimation. The first part of the thesis presents a novel artificial race driver (ARD), which autonomously learns to drive a vehicle around an obstacle-free circuit, performing online time-optimal vehicle trajectory planning and control. The following research questions are addressed in this part: How optimal is the trajectory executed online by an artificial agent that drives a high-fidelity vehicle model, in comparison with a minimum-lap-time optimal control problem (MLT-OCP), based on the same vehicle model and solved offline? Can the artificial agent generalize to circuits and conditions not seen during training? ARD employs an original neural network with a physics-driven internal structure (PhS-NN) for steering control, and a novel kineto-dynamical vehicle model for time-optimal trajectory planning. A new learning scheme enables ARD to progressively learn the nonlinear dynamics of an unknown vehicle. When tested on a high-fidelity model of a high-performance car, ARD achieves very similar results as an MLT-OCP, based on the same vehicle model and solved offline. When tested on a 1:8 vehicle prototype, ARD achieves similar lap times as an offline optimization problem. Thanks to its physics-driven architecture, ARD generalizes well to unseen circuits and scenarios, and is robust to unmodeled changes in the vehicle’s mass. The second part of the thesis deals with online time-optimal trajectory planning for dynamic obstacle avoidance. The research questions addressed in this part are: Can time-optimal trajectory planning for dynamic obstacle avoidance be performed online and with low computational times? How optimal is the resulting trajectory? Can the planner generalize to unseen circuits and scenarios? At each planning step, the proposed approach builds a tree of time-optimal motion primitives, by performing a sampling-based exploration in a local mesh of waypoints. The novel planner is validated in challenging scenarios with multiple dynamic opponents, and is shown to be computationally efficient, to return near-time-optimal trajectories, and to generalize well to new circuits and scenarios. The third part of the thesis shows an application of time-optimal trajectory planning with optimal control and PhS-NNs in the context of autonomous parking. The research questions addressed in this part are: Can an autonomous parking framework perform fast online trajectory planning and tracking in real-life parking scenarios, such as parallel, reverse and angle parking spots, and unstructured environments? Can the framework generalize to unknown variations in the vehicle’s parameters and road adherence, and operate with measurement noise? The autonomous parking framework employs a novel penalty function for collision avoidance with optimal control, a new warm-start strategy and an original PhS-NN for steering control. The framework executes complex maneuvers in a wide range of parking scenarios, and is validated with a high-fidelity vehicle model. The framework is shown to be robust to variations in the vehicle’s mass and road adherence, and to operate with realistic measurement noise. The fourth and last part of the thesis develops novel kinematics-structured neural networks (KS-NNs) to estimate the vehicle’s lateral velocity, which is a key quantity for time-optimal trajectory planning and control. The KS-NNs are a special type of PhS-NNs: their internal structure is designed to incorporate the kinematic principles, which enhances the generalization capabilities and physical explainability. The research questions addressed in this part are: Can a neural network-based lateral velocity estimator generalize well when tested on a vehicle not used for training? Can the network’s parameters be physically explainable? The approach is validated using an open dataset with two race cars. In comparison with traditional and neural network estimators of the literature, the KS-NNs improve noise rejection, exhibit better generalization capacity, are more sample-efficient, and their structure is physically explainable.