An optimal control tool for trajectory generation of autonomous drones

In the recent years, autonomous aerial vehicles gained large popularity in a variety of applications in the field of automation. To accomplish various and challenging tasks the capability of generating trajectories has assumed a key role. As higher performances are sought, traditional, flatness-based trajectory generation schemes present their limitations. In these approaches the highly nonlinear dynamics of the quadrotor is, indeed, neglected. Therefore, strategies based on optimal control principles turn out to be beneficial, since in the trajectory generation process they allow the control unit to best exploit the actual dynamics, and enable the drone to perform quite aggressive maneuvers. This dissertation is then concerned with the development of an optimal control technique to generate trajectories for autonomous drones. The algorithm adopted to this end is a second-order iterative method working directly in continuous-time, which, under proper initialization, guarantees quadratic convergence to a locally optimal trajectory. At each iteration a quadratic approximation of the cost functional is minimized and a decreasing direction is then obtained as a linear-affine control law, after solving a differential Riccati equation. The algorithm has been implemented and its effectiveness has been tested on the vectored-thrust dynamical model of a quadrotor in a realistic simulative setup.

Deep learning based style transfer for low altitude aerial imagery

Unmanned Aerial Vehicle (UAVs) equipped with cameras have been fast deployed to a wide range of applications, such as smart cities, agriculture or search and rescue applications. Even though UAV datasets exist, the amount of open and quality UAV datasets is limited. So far, we want to overcome this lack of high quality annotation data by developing a simulation framework for a parametric generation of synthetic data. The framework accepts input via a serializable format. The input specifies which environment preset is used, the objects to be placed in the environment along with their position and orientation as well as additional information such as object color and size. The result is an environment that is able to produce UAV typical data: RGB image from the UAVs camera, altitude, roll, pitch and yawn of the UAV. Beyond the image generation process, we improve the resulting image data photorealism by using Synthetic-To-Real transfer learning methods. Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different - although related - problem. This approach has been widely researched in other affine fields and results demonstrate it to be an interesing area to investigate. Since simulated images are easy to create and synthetic-to-real translation has shown good quality results, we are able to generate pseudo-realistic images. Furthermore, object labels are inherently given, so we are capable of extending the already existing UAV datasets with realistic quality images and high resolution meta-data. During the development of this thesis we have been able to produce a result of 68.4% on UAVid. This can be considered a new state-of-art result on this dataset.

Towards model driven software development for Arduino platforms: a DSL and automatic code generation

La tesi ha lo scopo di esplorare la produzione di sistemi software per Embedded Systems mediante l'utilizzo di tecniche relative al mondo del Model Driven Software Development. La fase più importante dello sviluppo sarà la definizione di un Meta-Modello che caratterizza i concetti fondamentali relativi agli embedded systems. Tale modello cercherà di astrarre dalla particolare piattaforma utilizzata ed individuare quali astrazioni caratterizzano il mondo degli embedded systems in generale. Tale meta-modello sarà quindi di tipo platform-independent. Per la generazione automatica di codice è stata adottata una piattaforma di riferimento, cioè Arduino. Arduino è un sistema embedded che si sta sempre più affermando perché coniuga un buon livello di performance ed un prezzo relativamente basso. Tale piattaforma permette lo sviluppo di sistemi special purpose che utilizzano sensori ed attuatori di vario genere, facilmente connessi ai pin messi a disposizione. Il meta-modello definito è un'istanza del meta-metamodello MOF, definito formalmente dall'organizzazione OMG. Questo permette allo sviluppatore di pensare ad un sistema sotto forma di modello, istanza del meta-modello definito. Un meta-modello può essere considerato anche come la sintassi astratta di un linguaggio, quindi può essere definito da un insieme di regole EBNF. La tecnologia utilizzata per la definizione del meta-modello è stata Xtext: un framework che permette la scrittura di regole EBNF e che genera automaticamente il modello Ecore associato al meta-modello definito. Ecore è l'implementazione di EMOF in ambiente Eclipse. Xtext genera inoltre dei plugin che permettono di avere un editor guidato dalla sintassi, definita nel meta-modello. La generazione automatica di codice è stata realizzata usando il linguaggio Xtend2. Tale linguaggio permette di esplorare l'Abstract Syntax Tree generato dalla traduzione del modello in Ecore e di generare tutti i file di codice necessari. Il codice generato fornisce praticamente tutta la schematic part dell'applicazione, mentre lascia all'application designer lo sviluppo della business logic. Dopo la definizione del meta-modello di un sistema embedded, il livello di astrazione è stato spostato più in alto, andando verso la definizione della parte di meta-modello relativa all'interazione di un sistema embedded con altri sistemi. Ci si è quindi spostati verso un ottica di Sistema, inteso come insieme di sistemi concentrati che interagiscono. Tale difinizione viene fatta dal punto di vista del sistema concentrato di cui si sta definendo il modello. Nella tesi viene inoltre introdotto un caso di studio che, anche se abbastanza semplice, fornisce un esempio ed un tutorial allo sviluppo di applicazioni mediante l'uso del meta-modello. Ci permette inoltre di notare come il compito dell'application designer diventi piuttosto semplice ed immediato, sempre se basato su una buona analisi del problema. I risultati ottenuti sono stati di buona qualità ed il meta-modello viene tradotto in codice che funziona correttamente.

Measurement of LSP-induced residual stresses and fatigue life on thin-walled aluminium alloys specimens

This thesis work encloses activities carried out in the Laser Center of the Polytechnic University of Madrid and the laboratories of the University of Bologna in Forlì. This thesis focuses on the superficial mechanical treatment for metallic materials called Laser Shock Peening (LSP). This process is a surface enhancement treatment which induces a significant layer of beneficial compressive residual stresses underneath the surface of metal components in order to improve the detrimental effects of the crack growth behavior rate in it. The innovation aspect of this work is the LSP application to specimens with extremely low thickness. In particular, after a bibliographic study and comparison with the main treatments used for the same purposes, this work analyzes the physics of the operation of a laser, its interaction with the surface of the material and the generation of the surface residual stresses which are fundamentals to obtain the LSP benefits. In particular this thesis work regards the application of this treatment to some Al2024-T351 specimens with low thickness. Among the improvements that can be obtained performing this operation, the most important in the aeronautic field is the fatigue life improvement of the treated components. As demonstrated in this work, a well-done LSP treatment can slow down the progress of the defects in the material that could lead to sudden failure of the structure. A part of this thesis is the simulation of this phenomenon using the program AFGROW, with which have been analyzed different geometric configurations of the treatment, verifying which was better for large panels of typical aeronautical interest. The core of the LSP process are the residual stresses that are induced on the material by the interaction with the laser light, these can be simulated with the finite elements but it is essential to verify and measure them experimentally. In the thesis are introduced the main methods for the detection of those stresses, they can be mechanical or by diffraction. In particular, will be described the principles and the detailed realization method of the Hole Drilling measure and an introduction of the X-ray Diffraction; then will be presented the results I obtained with both techniques. In addition to these two measurement techniques will also be introduced Neutron Diffraction method. The last part refers to the experimental tests of the fatigue life of the specimens, with a detailed description of the apparatus and the procedure used from the initial specimen preparation to the fatigue test with the press. Then the obtained results are exposed and discussed.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.

Analytic Network Process (ANP) a supporto della scelta della strategia produttiva: sviluppo e test di un approccio decisionale

Di fronte alla concorrenza globale, la sopravvivenza di un'azienda manifatturiera dipende sempre più da come essa può progettare, gestire e strutturare al meglio il proprio sistema di produzione per far fronte alla diversità dei prodotti, per migliorare l'affidabilità di consegna e anche per ridurre i costi. In questo contesto, le aziende manifatturiere utilizzano spesso sistemi di produzione diversi, in base a ciò che richiede il mercato. Molto in generale, i sistemi produttivi possono essere classificati in due categorie principali: make-to-stock (MTS) e make-to-order (MTO), in base alla politica di risposta alla domanda del mercato. Nel nuovo contesto competitivo le aziende si sono trovate a dover produrre costantemente prodotti specifici e di alta qualità con costi unitari bassi e livelli di servizio elevati (ossia, tempi di consegna brevi). È chiaro, dunque, che una delle principali decisioni strategiche da prendere da parte delle aziende sia quella relativa alla ripartizione dei prodotti in MTS/MTO, ovvero quale prodotto o famiglia di prodotti può essere fabbricato per essere stoccato a magazzino (MTS), quale può essere prodotto su ordinazione (MTO) e quale dovrebbe essere fabbricato in base alla politica di produzione ibrida MTS/MTO. Gli ultimi anni hanno mostrato una serie di cambiamenti nella politica di produzione delle aziende, che si stanno gradualmente spostando sempre più verso la modalità̀ di produzione ibrida MTS/MTO. In particolare, questo elaborato si concentrerà sul delayed product differentiation (DPD), una particolare strategia produttiva ibrida, e ne verrà proposto un modello decisionale basato sul funzionamento dell’Analytic Network Process (ANP) implementato attraverso il software Superdecisions.

SmartForge: An event-driven architecture for simulator-based process optimization

In the metal industry, and more specifically in the forging one, scrap material is a crucial issue and reducing it would be an important goal to reach. Not only would this help the companies to be more environmentally friendly and more sustainable, but it also would reduce the use of energy and lower costs. At the same time, the techniques for Industry 4.0 and the advancements in Artificial Intelligence (AI), especially in the field of Deep Reinforcement Learning (DRL), may have an important role in helping to achieve this objective. This document presents the thesis work, a contribution to the SmartForge project, that was performed during a semester abroad at Karlstad University (Sweden). This project aims at solving the aforementioned problem with a business case of the company Bharat Forge Kilsta, located in Karlskoga (Sweden). The thesis work includes the design and later development of an event-driven architecture with microservices, to support the processing of data coming from sensors set up in the company's industrial plant, and eventually the implementation of an algorithm with DRL techniques to control the electrical power to use in it.