299 research outputs found

    GRU-based denoising autoencoder for detection and clustering of unknown single and concurrent faults during system integration testing of automotive software systems

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    Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques

    On-board Obstacle Avoidance in the Teleoperation of Unmanned Aerial Vehicles

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    Teleoperation von Drohnen in Umgebungen ohne GPS-Verbindung und wenig Bewegungsspielraum stellt den Operator vor besondere Herausforderungen. Hindernisse in einer unbekannten Umgebung erfordern eine zuverlässige Zustandsschätzung und Algorithmen zur Vermeidung von Kollisionen. In dieser Dissertation präsentieren wir ein System zur kollisionsfreien Navigation einer ferngesteuerten Drohne mit vier Propellern (Quadcopter) in abgeschlossenen Räumen. Die Plattform ist mit einem Miniaturcomputer und dem Minimum an Sensoren ausgestattet. Diese Ausstattung genügt den Anforderungen an die Rechenleistung. Dieses Setup ermöglicht des Weiteren eine hochgenaue Zustandsschätzung mit Hilfe einer Kaskaden-Architektur, sehr gutes Folgeverhalten bezüglich der kommandierten Geschwindigkeit, sowie eine kollisionsfreie Navigation. Ein Komplementärfilter berechnet die Höhe der Drohne, während ein Kalman-Filter Beschleunigung durch eine IMU und Messungen eines Optical-Flow Sensors fusioniert und in die Softwarearchitektur integriert. Eine RGB-D Kamera stellt dem Operator ein visuelles Feedback, sowie Distanzmessungen zur Verfügung, um ein Roboter-zentriertes Modell umliegender Hindernisse mit Hilfe eines Bin-Occupancy-Filters zu erstellen. Der Algorithmus speichert die Position dieser Hindernisse, auch wenn sie das Sehfeld des Sensors verlassen, mit Hilfe des geschätzten Zustandes des Roboters. Das Prinzip des Ausweich-Algorithmus basiert auf dem Ansatz einer modell-prädiktiven Regelung. Durch Vorhersage der wahrscheinlichen Position eines Hindernisses werden die durch den Operator kommandierten Sollwerte gefiltert, um eine mögliche Kollision mit einem Hindernis zu vermeiden. Die Plattform wurde experimentell sowohl in einer räumlich abgeschlossenen Umgebung mit zahlreichen Hindernissen als auch bei Testflügen in offener Umgebung mit natürlichen Hindernissen wie z.B. Bäume getestet. Fliegende Roboter bergen das Risiko, im Fall eines Fehlers, sei es ein Bedienungs- oder Berechnungsfehler, durch einen Aufprall am Boden oder an Hindernissen Schaden zu nehmen. Aus diesem Grund nimmt die Entwicklung von Algorithmen dieser Roboter ein hohes Maß an Zeit und Ressourcen in Anspruch. In dieser Arbeit präsentieren wir zwei Methoden (Software-in-the-loop- und Hardware-in-the-loop-Simulation) um den Entwicklungsprozess zu vereinfachen. Via Software-in-the-loop-Simulation konnte der Zustandsschätzer mit Hilfe simulierter Sensoren und zuvor aufgenommener Datensätze verbessert werden. Eine Hardware-in-the-loop Simulation ermöglichte uns, den Roboter in Gazebo (ein bekannter frei verfügbarer ROS-Simulator) mit zusätzlicher auf dem Roboter installierter Hardware in Simulation zu bewegen. Ebenso können wir damit die Echtzeitfähigkeit der Algorithmen direkt auf der Hardware validieren und verifizieren. Zu guter Letzt analysierten wir den Einfluss der Roboterbewegung auf das visuelle Feedback des Operators. Obwohl einige Drohnen die Möglichkeit einer mechanischen Stabilisierung der Kamera besitzen, können unsere Drohnen aufgrund von Gewichtsbeschränkungen nicht auf diese Unterstützung zurückgreifen. Eine Fixierung der Kamera verursacht, während der Roboter sich bewegt, oft unstetige Bewegungen des Bildes und beeinträchtigt damit negativ die Manövrierbarkeit des Roboters. Viele wissenschaftliche Arbeiten beschäftigen sich mit der Lösung dieses Problems durch Feature-Tracking. Damit kann die Bewegung der Kamera rekonstruiert und das Videosignal stabilisiert werden. Wir zeigen, dass diese Methode stark vereinfacht werden kann, durch die Verwendung der Roboter-internen IMU. Unsere Ergebnisse belegen, dass unser Algorithmus das Kamerabild erfolgreich stabilisieren und der rechnerische Aufwand deutlich reduziert werden kann. Ebenso präsentieren wir ein neues Design eines Quadcopters, um dessen Ausrichtung von der lateralen Bewegung zu entkoppeln. Unser Konzept erlaubt die Neigung der Propellerblätter unabhängig von der Ausrichtung des Roboters mit Hilfe zweier zusätzlicher Aktuatoren. Nachdem wir das dynamische Modell dieses Systems hergeleitet haben, synthetisierten wir einen auf Feedback-Linearisierung basierten Regler. Simulationen bestätigen unsere Überlegungen und heben die Verbesserung der Manövrierfähigkeit dieses neuartigen Designs hervor.The teleoperation of unmanned aerial vehicles (UAVs), especially in cramped, GPS-restricted, environments, poses many challenges. The presence of obstacles in an unfamiliar environment requires reliable state estimation and active algorithms to prevent collisions. In this dissertation, we present a collision-free indoor navigation system for a teleoperated quadrotor UAV. The platform is equipped with an on-board miniature computer and a minimal set of sensors for this task and is self-sufficient with respect to external tracking systems and computation. The platform is capable of highly accurate state-estimation, tracking of the velocity commanded by the user and collision-free navigation. The robot estimates its state in a cascade architecture. The attitude of the platform is calculated with a complementary filter and its linear velocity through a Kalman filter integration of inertial and optical flow measurements. An RGB-D camera serves the purpose of providing visual feedback to the operator and depth measurements to build a probabilistic, robot-centric obstacle state with a bin-occupancy filter. The algorithm tracks the obstacles when they leave the field of view of the sensor by updating their positions with the estimate of the robot's motion. The avoidance part of our navigation system is based on the Model Predictive Control approach. By predicting the possible future obstacles states, the UAV filters the operator commands by altering them to prevent collisions. Experiments in obstacle-rich indoor and outdoor environments validate the efficiency of the proposed setup. Flying robots are highly prone to damage in cases of control errors, as these most likely will cause them to fall to the ground. Therefore, the development of algorithm for UAVs entails considerable amount of time and resources. In this dissertation we present two simulation methods, i.e. software- and hardware-in-the-loop simulations, to facilitate this process. The software-in-the-loop testing was used for the development and tuning of the state estimator for our robot using both the simulated sensors and pre-recorded datasets of sensor measurements, e.g., from real robotic experiments. With hardware-in-the-loop simulations, we are able to command the robot simulated in Gazebo, a popular open source ROS-enabled physical simulator, using computational units that are embedded on our quadrotor UAVs. Hence, we can test in simulation not only the correct execution of algorithms, but also the computational feasibility directly on the robot's hardware. Lastly, we analyze the influence of the robot's motion on the visual feedback provided to the operator. While some UAVs have the capacity to carry mechanically stabilized camera equipment, weight limits or other problems may make mechanical stabilization impractical. With a fixed camera, the video stream is often unsteady due to the multirotor's movement and can impair the operator's situation awareness. There has been significant research on how to stabilize videos using feature tracking to determine camera movement, which in turn is used to manipulate frames and stabilize the camera stream. However, we believe that this process could be greatly simplified by using data from a UAV’s on-board inertial measurement unit to stabilize the camera feed. Our results show that our algorithm successfully stabilizes the camera stream with the added benefit of requiring less computational power. We also propose a novel quadrotor design concept to decouple its orientation from the lateral motion of the quadrotor. In our design the tilt angles of the propellers with respect to the quadrotor body are being simultaneously controlled with two additional actuators by employing the parallelogram principle. After deriving the dynamic model of this design, we propose a controller for this platform based on feedback linearization. Simulation results confirm our theoretical findings, highlighting the improved motion capabilities of this novel design with respect to standard quadrotors

    Smart Grid challenges - Device Trustworthiness

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    The Power Grid development brings about technological design changes, resulting in increased connectivity and dependency on IoT devices. The changes offer opportunities to manipulate the IoT hardware as the root of trust. Although terrifying, hardware attacks are considered resource-demanding and rare. Nonetheless, Power Grids are attractive targets for resourceful attackers. As such, the Ukraine attacks boosted Power Grid cybersecurity focus. However, physical assurance and hardware device trustworthiness received less attention. Overhead Line Sensors are utilized in Dynamic Line Rating doctrines for Power Grids. They are potentially essential in the future to optimize conductor ampacity. Conductor optimization is crucial for Power Grids because future throughput volatility demands a high level of grid flexibility. However, there may be challenges to the integrity and availability of the data collected using Overhead Line sensors. We believe that in securing the future Smart Grid, stakeholders need to raise attention to device trustworthiness entailing the hardware layer. That said, integrated into cloud-enhanced digital ecosystems, Overhead Line Sensors can also be manipulated through the network, software, and supply chain to impact their trustworthiness

    Actuators for Intelligent Electric Vehicles

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    This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

    An OpenEaagles Framework Extension for Hardware-in-the-Loop Swarm Simulation

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    Unmanned Aerial Vehicle (UAV) swarm applications, algorithms, and control strategies have experienced steady growth and development over the past 15 years. Yet, to this day, most swarm development efforts have gone untested and thus unimplemented. Cost of aircraft systems, government imposed airspace restrictions, and the lack of adequate modeling and simulation tools are some of the major inhibitors to successful swarm implementation. This thesis examines how the OpenEaagles simulation framework can be extended to bridge this gap. This research aims to utilize Hardware-in-the-Loop (HIL) simulation to provide developers a functional capability to develop and test the behaviors of scalable and modular swarms of autonomous UAVs in simulation with high confidence that these behaviors will prop- agate to real/live ight tests. Demonstrations show the framework enhances and simplifies swarm development through encapsulation, possesses high modularity, pro- vides realistic aircraft modeling, and is capable of simultaneously accommodating four hardware-piloted swarming UAVs during HIL simulation or 64 swarming UAVs during pure simulation

    Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

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    The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system

    Control-Theoretical Perspective in Feedback-Based Systems Testing

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    Self-Adaptive Systems (SAS) and Cyber-Physical Systems (CPS) have received significant attention in recent computer engineering research. This is due to their ability to improve the level of autonomy of engineering artefacts. In both cases, this autonomy increase is achieved through feedback. Feedback is the iteration of sens- ing and actuation to respectively acquire knowledge about the current state of said artefacts and steer them toward a desired state or behaviour. In this thesis we dis- cuss the challenges that the introduction of feedback poses on the verification and validation process for such systems, more specifically, on their testing. We highlight three types of new challenges with respect to traditional software testing: alteration of testing input and output definition, and intertwining of components with different nature. Said challenges affect the ways we can define different elements of the test- ing process: coverage criteria, testing set-ups, test-case generation strategies, and oracles in the testing process. This thesis consists of a collection of three papers and contributes to the definition of each of the mentioned testing elements. In terms of coverage criteria for SAS, Paper I proposes the casting of the testing problem, to a semi-infinite optimisation problem. This allows to leverage the Scenario Theory from the field of robust control, and provide a worst-case probabilistic bound on a given performance metric of the system under test. For what concerns the definition of testing set-ups for control-based CPS, Paper II investigates the implications of the use of different abstractions (i.e., the use of implemented or emulated compo- nents) on the significance of the testing. The paper provides evidence that confutes the common assumption present in previous literature on the existence of a hierar- chy among commonly used testing set-ups. Finally, regarding the test-case gener- ation and oracle definition, Paper III defines the problem of stress testing control- based CPS software. We contribute to the generation and identification of stress test cases for such software by proposing a novel test case parametrisation. Leveraging the proposed parametrisation we define metamorphic relations on the expected be- haviour of the system under test. We use said relations for the development of stress testing approach and sanity checks on the testing results

    Development of battery management system for hybrid electric propulsion system.

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    Because of the high overall efficiency and low emissions, Hybrid Electric Propulsion System (HEPS) have become an attractive research area. In this research, a parallel HEPS architecture is adopted and a Hardware test platform is constructed. As a relative new power source in powertrains, battery system plays an important role in HEPS. Hence, a Battery Management System (BMS) is investigated in this research. Battery pack State of Charge (SOC) is a key feedback value in HEPS control. In order to estimate SOC, firstly, an operation-classification adaptive battery model is proposed for Li-Po batteries. Considering the fact that model parameter accuracy is of importance in model-based system state estimation method, an event triggered Adaptive Genetic Algorithm (AGA) is applied for online parameter identification. Secondly, the Extended Kalman Filter (EKF) is applied for single battery cell SOC estimation. Finally, a fuzzy estimator is proposed for battery pack SOC estimation based on maximum/minimum cell voltages and SOC values. Experimental results show that the proposed AGA can effectively track battery parameter variation and SOC estimation error for single cell as well as for the battery pack are both less than 1%. Moreover, considering the Li-Po battery characteristics, a converter based battery cell balancing method is proposed. Simulation result shows that proposed balancing method can be effective in balancing battery cells. In addition, in relation to safety and reliability concerns, a Discrete Wavelet Transform (DWT) based battery circuit detection method is proposed and simulation results showing its feasibility are presented.PhD in Aerospac

    Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems

    Get PDF
    The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system
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