Unmanned aerial vehicles are becoming more popular in today’s society, which results in the rise of laws intended to maintain safety. To abide by these, while allowing the technology to expand, functioning path-planning algorithms are required.This also includes having methods for detecting and managing obstacles. This project aims to improve an existing path-planning algorithm that is based on A* and implemented in Python.The solution consisted of using functions for finding polytopeintersection,as well as optimizing the collision avoidance and the search algorithm. In addition to that, realistic constraints were implemented on the generated trajectory in order to reflect real-life limitations. The results demonstrated that the paths were always feasible, with respect to input and position constraints. The program’s computation time was also reduced up to 89% of the original run-time. There is, however, still room for improvement since the original code generated a shorter path for the three scenarios it was created for. On the other hand,the improved algorithm could handle a new scenario, which the original code failed to do. / Obemannade flygfarkoster blir alltmer vanliga i dagens samhälle, vilket resulterar i uppkomsten av nya lagar ämnade åt att upprätthålla säkerhet. För att förhålla sig till dessa, samtidigt som teknologin tillåts expandera, krävs fungerande vägplaneringsalgoritmer. Där ingår det även att ha metoder för att upptäcka och hantera hinder. Detta projekt syftar till att förbättra en befintlig vägplaneringsalgoritm som är baserad på A* och implenterad i Python. Lösningsmetoden bestod av att använda inbyggda Python-funktioner ämnade åt att finna skärningar mellan polytoper, samt optimera kollisionshantering och sökalgoritmen. Dessutom infördes realistiska krav på den framställda vägen i syfte om att reflektera verlighetens begränsningar. Resultatet visade att vägarna alltid var genomförbara, med avseende på inmatningsoch positionsrelaterade villkor. Programmets beräkningstid hade även reducerats upptill 89% av den ursprungliga körtiden. Det finns dock utrymme för förbättringar då den ursprungliga koden generar en kortare väg för de tre scenarion den tillverkades för. Däremot kinde den förbättrade algoritmen hantera ett nytt scenario, en ursprungliga koden misslyckades med. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Research in Unmanned Air Vehicles (UAV's) continues to push the limitations of size and weight. As technical advances have made UAV's smaller and less expensive, they have become more flexible and extensive in their roles. To continue using smaller and less expensive components while retaining and even enhancing performance requires more sophisticated processing of sensor data in order for the UAV to accurately determine its state and thereby allow the use of feedback in controlling the aircraft automatically. This work presents a three-stage state-estimation scheme for the class of UAV's know as Miniature Air Vehicles (MAV's). The first stage estimates pitch and roll, the second stage estimates heading, and the third stage produces a position estimate and an estimate of wind speed and direction. All three stages make use of the extended Kalman filter, a framework for using a system dynamic model to predict future states and to update the predictions using weighted sensor measurements as they become available, where the weighting is based on the relative uncertainty of the dynamic model and the sensors. Using the three-stage state esti-mation scheme, significant improvements in the estimation of pitch, roll and heading have been achieved in simulation and flight testing. Performance of the navigation (position and wind) stage is comparable to an existing baseline algorithms for position and wind, and shows additional promise for use in dead reckoning when GPS updates become unavailable.

Humanity has always wanted to explore the world we live in and answer different questions about our universe. After the International Space Station will end its service one possible next step could be a Moon Outpost: a convenient location for research, astronaut training and technological development that would enable long-duration space. This location can be inside one of the presumed lava tubes that should be present under the surface but would first need to be inspected, possibly by machine capable of capturing and relaying a map to a team on Earth.In this report the past and future Moon base missions will be summarized considering feasible outpost scenarios from the space companies or agencies. and their prospected manned budget. Potential mission profiles, objectives, requirements and constrains of the BATonomous Moon cave Explorer (BAT-ME) mission will be discussed and defined. Vehicle and mission concept will be addressed, comparing and presenting possible propulsion or locomotion approaches inside the lava tube.The Inkonova “Batonomous™” system is capable of providing Simultaneous Localization And Mapping (SLAM), relay the created maps, with the possibility to easily integrate the system on any kind of vehicle that would function in a real-life scenario.Although the system is not fully developed, it will be assessed from a technical perspective, and proper changes for a viable system transition for the space-Moon environment will be devised. The transition of the system from the Batonomous™ state to the BAT-ME required state will be presented from the requirement, hardware, software, electrical and operational point of view.The mission will be devised into operational phases, with key goals in mind. Two different vehicles will be presented and designed on a high engineering level. A risk analysis and management system will be made to understand the possible negative outcomes of different parts failure on the mission outcome.

<p>Upon introducing semi- and fully automated vehicles on the road, drivers will be reluctant to focus on the traffic interaction and rely on the vehicles' decision-making. However, encountering pedestrians still poses a significant difficulty for modern automated driving technologies. Considering the high-level complexity in human behavior modeling to solve a real-world problem, deep-learning algorithms trained from naturalistic data have become promising solutions. Nevertheless, although developing such algorithms is achievable based on scene data collection and driver knowledge extraction, evaluation remains challenging due to the potential crash risks and limitations in acquiring ground-truth intention changes. </p> <p><br></p> <p>This study proposes a VR-based testing bed to evaluate real-time pedestrian intention algorithms as VR simulators are recognized for their affordability and adaptability in producing a variety of traffic situations, and it is more reliable to conduct human-factor research in autonomous cars. The pedestrian wears the head-mounted headset or uses the keyboard input and makes decisions in accordance with the circumstances. The simulator has added a credible and robust experience, essential for exhibiting the real-time behavior of the pedestrian. While crossing the road, there exists uncertainty associated with pedestrian intention. Our simulator will anticipate the crossing intention with consideration of the ambiguity of the pedestrian behavior. The case study has been performed over multiple subjects in several crossing conditions based on day-to-day life activities. It can be inferred from the study outcomes that the pedestrian intention can be precisely inferred using this VR-based simulator. However, depending on the speed of the car and the distance between the vehicle and the pedestrian, the accuracy of the prediction can differ considerably in some cases.</p>

<p dir="ltr">The future of the global economy lies in space. As the economic and scientific benefits from space become more accessible and apparent to the public, the demand for more spacecrafts will only increase. However, simply using the current space architecture to sustain any major activities past low Earth orbit is infeasible. The limiting factor of relying on ground operators via the Deep Space Network will blunt future growth in cislunar space traffic as the bandwidth is insufficient to satisfy the needs of every spacecraft in this domain. For this reason, spacecrafts must begin to operate autonomously or semi-autonomously for operators to be able to manage more missions at a given time. This thesis focuses on the guidance and navigation policies that could help vehicles such as logistical or resupply spacecrafts perform their rendezvous autonomously. It is found that using GNSS signals and Moon-based optical navigation has the potential to help spacecrafts perform autonomous orbit determination in near-Moon trajectories. The estimations are high enough quality such that a stochastic controller can use this navigation solution to confidently guide the spacecraft to a target within a tolerance before proximity operations commence. As the reliance on the ground is shifted away, spacecrafts would be able to operate in greater numbers outside of Earth's lower orbits, greatly assisting humanity's presence in space. </p>

<p dir="ltr">Traffic safety and congestion are global concerns. Autonomous vehicles (AVs) are expected to enhance transportation safety and reduce congestion. However, achieving their full potential requires 100% market penetration, a challenging task. This study addresses key issues in mixed traffic environments, where human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) coexist. A number of critical questions persist: 1) inadequate exploration of human errors (errors originating from non-CAV sources) in mixed traffic; 2): limited focus on information selection and learning efficiency in network-level rerouting, particularly in highly dynamic environments; 3) inadequacy of personalized element driver inputs in motion-planning frameworks; 4) lack of consideration of user privacy concerns.</p><p dir="ltr">With the goal of advancing the existing knowledge in this field and shedding light on these matters, this dissertation introduces multiple frameworks. These frameworks leverage connectivity and automation to improve safety and mobility in mixed traffic, addressing various research levels, including local-level and network-level safety enhancement, as well as network-level and global-level mobility enhancement. With optimization- and learning-based methods implemented (Model Predictive Control, Deep Neural Network, Deep Reinforcement Learning, Transformer model and Federated Learning), frameworks introduced in this dissertation are expected to help highway agencies and vehicle manufacturers improve the safety and efficiency of traffic flow in the mixed-traffic era. Our research findings revealed increased crash-avoidance rates in critical situations, enhanced accuracy in predicting lane changes, improved dynamic rerouting within urban areas, and the implementation of effective data-sharing mechanisms with a focus on user privacy. This research underscores the potential of connectivity and automation to significantly enhance mixed-traffic safety and mobility.</p>

En följd av såväl politiska beslut som den ökade medvetenheten hos kunder är att efterfrågan av grönare teknik har ökat. Idag står andelen av laddbara personbilar för ungefär en tredjedel av alla nyregistrerade personbilar i Sverige. Det är inte bara personbilstillverkare som går igenom detta teknikskifte mot grönare teknik och smartare självkörande bilar, samma trend ses i lastbilsindustrin. Även fast kunderna i detta fall är andra företag som t.ex. stora åkerier finns det även här en stor efterfrågan på den nya tekniken. Det kan tyckas motstridigt att tillverka ett elfordon som både ska klara av att transportera tung last samtidigt som det ska kunna erbjuda en lång räckvidd. Självkörande lastbilar är något som Scania tillsammans med flera stora lastbilstillverkare jobbar med att utveckla. De utmaningar som finns för lastbilstillverkare gällande det autonoma skiljer sig från personbilstillverkare. Syftet med rapporten är att undersöka hur produktutvecklingsprocessen påverkas av teknikskiftet mot eldrivna, autonoma lastbilar. Inledningsvis genomfördes en litteraturstudie där tidigare kända teorier och studier inom relevanta områden undersöktes för att få en bättre förståelse för det nuvarande kunskapsläget. Därefter hölls tre stycken semistrukturerade intervjuer med personer från olika avdelningar med olika kompetenser från företag inom dels lastbilsindustrin, men även personbilsindustrin. Intervjuerna syftade till att samla så mycket information som möjligt kring de utmaningar som lastbilsindustrin står inför ur ett produktutvecklings perspektiv. Även de förändringar som skett i samband med teknikskiftet var av intresse under intervjuerna. Efter intervjuerna gjordes en sammanställning och en analys av det resultat som framkommit under studien. Resultatet visar att det har skett en stor förändring inom industrin på flera områden. Nya arbetsmetoder har implementerats för att korta ned ledtiderna och öka kvaliteten på produkterna. Det finns ett ökat behov av nya kompetenser inom branschen och det blir allt vanligare att anställa personal från hela världen som kan arbeta på distans. Vidare satsas det allt mer på att omskola befintlig personal. Resultatet visar även att antalet samarbeten har ökat och att det sker ett stort informationsutbyte mellan företag som båda jobbar med autonom teknik. / An increasing demand for greener technology has been forced through by political decisions, but also as a result of growing awareness amongst customers. Today, the proportion of rechargeable passenger cars accounts for about a third of all newly registered passenger cars in Sweden. It is not just passenger car manufacturers who are experiencing this shift towards greener technology and smarter autonomous cars. The same trend can be seen in the truck industry, even if the customers in this case are large hauliers. It may seem contradictory to manufacture a vehicle that must both be able to transport heavy loads and withstand long range. Scania, amongst other big truck manufacturers, has come a long way in developing autonomous trucks. The challenges of truck manufacturers differ from those of passenger car manufacturers regarding autonomous technology. This study will investigate how the product development process is affected by the shift in technologytowards electric, autonomous trucks.Initially, a literature study was conducted where previous theories and studies in relevant areas were examined in order to gain a better understanding of the current state of knowledge. Subsequently, three semi-structured interviews were held with people from different departments and with various competences from companies in both the truck and car industries. The aim of the interviews was to gather as much information as possible about the challenges facing the truck industry from the product development perspective. The changes that have taken place as a result of the technology shift were also of interest in the interviews. Afterwards, an analysis summary was made of the results that emerged during the study. The results show that there has been significant change in several areas within the industry. New working methods have been implemented to shorten lead times and increase the quality of the products. There is a growing need for new skills within the industry and it is becoming increasingly common to employ staff from all over the world who can work remotely. At the same time, more and more funding is going towards further training of existing staff. The results also show that the number of collaborations has increased and that there is a large exchange of information between companies that are in the autonomous technology business.

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<p dir="ltr">Path-tracking control is an integral part of motion planning in autonomous vehicles, in which the vehicle's lateral and longitudinal positions are controlled by a control system that will provide acceleration and steering angle commands to ensure accurate tracking of longitudinal and lateral movements in reference to a pre-defined trajectory. Extensive research has been conducted to address the growing need for efficient algorithms in this area. In this dissertation, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a Deep Reinforcement Learning model is developed to facilitate the control of longitudinal speed. A Deep Deterministic Policy Gradient algorithm is employed as the primary algorithm in training the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed using Neural Networks to control the steering angle of the vehicle with the main goal of following a reference trajectory. Then, a path-planning algorithm is developed using a hybrid A* planner. Finally, the longitudinal and lateral control models are coupled together to obtain a complete path-tracking controller that follows a path generated by the hybrid A* algorithm at a wide range of vehicle speeds. The state-of-the-art path-tracking controller is also built using Model Predictive Control and Stanley control to evaluate the performance of the proposed model. The results showed the effectiveness of both proposed models in the same scenario, in terms of velocity error, lateral yaw angle error, and lateral distance error. The results from the simulation show that the developed hybrid A* algorithm has good performance in comparison to the state-of-the-art path planning algorithms.</p>

These days most of the research related to autonomous vehicle technology focuses on vehicle technology itself and lesser on road infrastructure, including geometric design. This research project aims to lower the deficiency of research works required to make the optimized geometric road design for autonomous vehicle sustainable. In geometric design, significant concerns are designing the road geometrics such as lane width, the radius of horizontal curves, sag vertical curves and crest vertical curves, extra widening, setback distance, and intersection, making the road safer for the vehicles to travel comfortably.Road geometrics is widely designed using the stopping sight distance model, which provides sufficient time to avoid accidents and is efficient. Here in the research work, the stopping sight design model is used for autonomous vehicle technology. At first, the art of autonomous vehicle technology is studied, and a significant difference between autonomous vehicle technology and human-driven vehicle to apply stopping sight distance model is figured out. A literature study is also done for the geometric design of the road for the vehicle with the human driver and autonomous vehicle. The AASHTO model derived for the human-driven vehicle is used and modified for the autonomous vehicle, which gives the optimized geometric design for the autonomous vehicle. The Optimized geometric design parameter is designed individually in AutoCAD Civil 3D. Two road designs follow this in a random rural topography consisting of a normal road design for the vehicle with the human driver and a fully autonomous vehicle. Finally, the sustainability of optimized geometric design compared to road design for the human-driven vehicle is checked in terms of earthwork, pavement surface areas, and pavement materials volume. The result shows that the optimization of a geometric road design for autonomous vehicles is sustainable and extensive research is required.