Autonomous Vehicle Engineering

The course aims to provide the fundamental knowledges for the synthesis of physical-mathematical models of mechanical systems according to an approach that is functional to the design of the model-based controllers. The methodologies for modelling, the main sources of mechanical nonlinearities, as well as local linearization based tools are described, starting from the typical features of the mechanical systems, with particular reference to vehicle dynamics.
Conoscenza e capacita' di comprensione: Students should learn the basic concepts of vehicle modelling with particular reference to control design.
Capacita' di applicare conoscenza e comprensione: Students should learn the concepts for the design of vehicle dynamics models oriented to the synthesis of control systems.

Systemic approach for the design of a mechanical system: mechatronic system; passive, semi-active and active controls; feedforward and feedback controls. Modelling of mechanical systems: d’Alembert and Newton approaches, Lagrange approach, linear time-invariant mechanical systems, nonlinearities in mechanical systems, linearization technique. State-space modelling, input-output modelling, transfer function, Laplace domain, Laplace transform, stability analysis. Control of mechanical systems in terms of vibrational characteristics. Longitudinal and lateral dynamics of road and railway vehicles. Control of the dynamics of road and railway vehicles.

The section will be completed in accordance with the forthcoming PQA Guidelines

Vehicle dynamics and control, R. Rajamani, Springer Ed. Handbook of railway vehicle dynamics, S. Iwnicki, CRC Press

The course aims at providing students with tools and methods for designing interactive systems and interfaces by using multidomain modeling, simulations, and virtual prototyping. At the end of the course, the student will be able to: develop 3D models of mechanical assemblies; choose appropriate graphics and technical communication tools for the design of mechanical systems; assign and evaluate characteristics and properties of mechanical systems in a virtual environment: shapes, proportions, materials, tolerances, appearance; manage reference protocols for data exchange; execute finite element structural analysis (FEM) in virtual environment on mechanical parts and assemblies; develop multidomain models and simulations using MATLAB Simscape environment; simulate behavior of electro-mechanical systems.
Conoscenza e capacita' di comprensione: The course aims to provide students with knowledge related to methodological tools aimed at the design and development of interactive systems and interfaces using digital modelling techniques. The student must demonstrate the knowledge of the main design phases for a mechanical system with reference to the construction of the virtual prototype and its virtual verification using simulation tools.
Capacita' di applicare conoscenza e comprensione: The student needs to show the ability to build virtual prototypes using specific software for product life cycle management, geometric modeling, structural analysis, multidomain modeling and simulations. The student needs to show the ability to develop a series of planned exercises on mechanical subsystems or the conceptual project of an interactive system and its interfaces in a virtual environment. The student needs to show the ability to identify normalized elements and parts to be designed, manage the reference protocols for data exchange, and evaluate the simulation systems suitable for the development and virtual verification of the project.

Introduction to the course: objectives, contents, examination methods. Geometry Based Modeling. Knowledge-Based Modeling. Parametric and Variational Approach. Feature-Based Solid Modeling. Parametric-associative paradigm. Product structure: parts, assemblies. Part modeling. Tools for managing sketch-based features. Constraint Management. Sketch analysis tools. Tools for creating and managing advanced features. CAD tools for the creation, analysis, and manipulation of surfaces. Data exchange problems. Generation of drawings and product documentation starting from CAD models. The bill of materials. Generative Structural Analysis and Finite Element Method (FEM) in CAD-CAE environment; preprocessing: creation of the mesh, constraint conditions, application of loads; post-processing: evaluation of the stress state, displacements, and deformations (4CFU). Multidomain modeling principles. Definition of generalized variable in complex multidomain systems. Power and energy variables in different domains. Object-oriented mathematical modelling. Classes and instances. Inheritance. Equations. Physical Network Approach to Modeling. Acausal physical modelling. Component model. Library development and use. Multi-body modelling. Model Simulation in MATLAB Simscape environment. Multiobjective optimization (2 CFU).

The examination focuses on the development and discussion of planned exercises or the discussion of a project assigned during the lectures (weight 50%). In addition, knowledge of the course contents will be assessed through an oral discussion (weight 50%).
The section will be completed in accordance with the forthcoming PQA Guidelines.
Practical test on digital modelling and simulation of systems.

Kun Woo Lee, “Principles of CAD/CAE/CAM Systems”, Addison-Wesley Ed., 1999.
Shuvra Das, Modeling and Simulation of Mechatronic Systems using Simscape, Morgan & Claypool Publishers, 2020.
Dominique Madier. Practical Finite Element Analysis for Mechanical Engineers. FEA Academy, 2020.
MATLAB suite available for UNINA students.
Exercise tables (teachers’ websites).
Slides and supplementary handouts provided by the teachers (teachers’ websites).

Il corso ha lo scopo di fornire le conoscenze necessarie per progettare e sviluppare soluzioni efficienti di guida e navigazione per veicoli autonomi. Il fine ultimo è lo sviluppo del piano di missione, ovvero la traiettoria 3D o 4D del veicolo, a seconda della tipologia dei sistemi di trasporto. La guida può essere strategica o tattica a seconda che venga realizzata prima dell'inizio della missione o meno. La navigazione è la funzione che fornisce informazioni su posizione, velocità e assetto del veicolo. Si ottiene integrando misurazioni provenienti da diverse fonti, come sensori e ricevitori.
Conoscenza e capacita' di comprensione: Lo studente sarà in grado di comprendere gli elementi più comuni della guida e della navigazione dei veicoli autonomi.
Capacita' di applicare conoscenza e comprensione: Lo studente sarà in grado di progettare gli elementi più comuni della guida e della navigazione dei veicoli autonomi.

· Introduction. Definitions and principles.
· System architecture of mission management for autonomous vehicles.
· Guidance
o Requirements and constraint for mission planning;
o Discrete path planning
o Differential path planning
o Real-time mission planning management;
· Navigation
o Reference terms for terrestrial navigation;
o Process sensors: inertial systems
§ Reference configurations for inertial navigation;
§ Traditional inertial sensors;
§ Advanced inertial sensors;
· Coriolis gyros;
· Fiber optic gyros;
· MEMS inertial sensors;
§ Inertial Navigation Equations;
§ Error of Inertial Navigation;
o Measurement sensors and systems
§ Systems for measurement of fluid-related dynamics;
§ Acoustic sensors;
§ Angle and angular rate encoders;
§ Micro-wave sensors;
§ Optical sensors: imaging and non-imaging;
§ Terrestrial radio navigation systems;
§ Satellite radio navigation systems;
· Application – GPS/INS integration.
§ Advanced applications of integrated navigation

Il punteggio scende fino ad un massimo di 4 punti per ogni singolo errore (4 punti è un errore molto grave). Più di 3 errori molto gravi determinano l'insufficienza. L'insufficienza può essere determinata anche da un singolo errore gravissimo che denota una complessiva ignoranza della materia.

Slide, appunti, reportistica tecnica.
Testi di riferimento:
Farrell J. and Barth M., "The Global Positioning System and Inertial Navigation", McGraw Hill, New York NY, USA, 1999
Savage P.G., Strapdown Analytics, Strapdown Associates Inc., Maple Plain MN, USA, 2000
Rogers R. M., "Applied Mathematics in Integrated Navigation Instruments”, AIAA Press, Washington DC, USA, 2000
Titterton, D. H., "Strapdown Inertial Navigation Technology", Peter Peregrinus, New York NY, USA, 1996

Lo scopo del corso è quello di fornire agli studenti nozioni di base e algoritmi per l'elaborazione di immagini e video digitali, con particolare attenzione ai veicoli a guida autonoma. Oltre a fornire gli strumenti matematici e concettuali per trattare analiticamente questi argomenti, il corso si propone di dare le conoscenze necessarie per sviluppare in Python i principali algoritmi per l’elaborazione delle immagini.
Conoscenza e capacita' di comprensione: Lo studente deve dimostrare di conoscere e saper comprendere gli strumenti metodologici per l’analisi e l’elaborazione delle immagini. Tali strumenti consentiranno agli studenti di risolvere problemi più complessi sia nel dominio spaziale che in quello temporale.
Capacita' di applicare conoscenza e comprensione: Lo studente deve dimostrare di saper ragionare sulle problematiche riguardo l’analisi e l’elaborazione di immagini e di saper scegliere la tecnica più adatta per la risoluzione di un problema pratico.

[2 CFU] Concetti base sull'elaborazione di immagini/video. Enhancement nel dominio spaziale. Trasformazioni di intensità: operazioni puntuali lineari e non lineari. Equalizzazione dell'istogramma. Bit-plane slicing. Operazioni aritmetiche. Operazioni geometriche. Fondamenti del filtraggio spaziale. Filtri di smoothing e sharpening. Filtro mediano. Rappresentazione RGB.
[0.5 CFU] Segmentazione. Tecniche basate sul rilevamento dei bordi. Detection di punti e linee. Filtri di Roberts, Prewitt e Sobel. Thresholding del gradiente. Zero-crossing del Laplaciano. Canny edge detector. Tecniche class-based. Algoritmo K-means.
[0.5 CFU] Analisi delle texture e classificazione delle immagini. Descrittori locali e globali. Local Binary Pattern (LBP) e sue varianti.
[2 CFU] Deep learning per l'elaborazione delle immagini. Fondamenti delle reti neurali convoluzionali (CNN), Backpropagation, loss function, learning rate. Classificazione delle immagini tramite fine-tuning. Data augmentation. Riconoscimento di oggetti mediante segmentazione semantica. Architetture encoder-decoder. U-Net. Meccanismi di attenzione. Interpretabilità. Modelli generativi, reti generative avversarie (GAN).
[1 CFU] Applicazioni. Esempi di applicazioni di elaborazione di immagini per la guida autonoma. Approcci basati sul deep learning per il riconoscimento degli oggetti (pedoni, automobili, segnali stradali), segmentazione semantica e tracking degli oggetti nei video.

La sezione verrà completata in conformità con le prossime linee guida PQA.
Altro: L'esame consiste in una prova scritta e in una prova orale. La prova scritta consiste in un test al computer (risolvere tre esercizi su applicazioni di elaborazione delle immagini in Python) o in un progetto sulle applicazioni di elaborazione delle immagini per la guida autonoma. La prova orale consiste in due domande su problemi/algoritmi spiegati durante il corso.

Libri di testo consigliati:

• R.C.Gonzalez, R.E.Woods: “Digital image processing”, 3nd edition, Prentice Hall, 2008.
• R. Szeliski, “Computer Vision: Algorithms And Applications”, Springer, 2nd edition, 2022.
• M. Nielsen: “Neural Networks and Deep learning”, available on-line, Dec. 2019.

Il corso si propone di presentare le principali tecniche di macchine learning, coprendo tutti gli aspetti dalla preparazione dei dati alla valutazione delle prestazioni, attraverso esercitazioni pratiche con strumenti commerciali e/o open-source. Viene, inoltre, fornita un'introduzione al ciclo di vita dei Big Data e della Data Analytics, che riguarda la progettazione di database distribuiti ed il processo di modellazione, acquisizione, condivisione, analisi e visualizzazione delle informazioni contenute nei Big Data.
Conoscenza e capacita' di comprensione: Lo studente deve conoscere i principali algoritmi di Machine Learning (ML) e deve dimostrare di saper scegliere l'algoritmo di ML più adatto a risolvere un problema specifico, in base ai requisiti del problema stesso. Lo studente deve conoscere le tecniche da utilizzare per valutare correttamente le prestazioni degli algoritmi di ML.
Inoltre, lo studente deve conoscere i principali framework di Big Data per acquisire, modellare, condividere, analizzare e visualizzare grandi quantità di informazioni. Lo studente deve inoltre dimostrare di essere in grado di scegliere il framework più adatto per affrontare i diversi task.
Capacita' di applicare conoscenza e comprensione: Lo studente deve dimostrare di essere in grado di risolvere problemi reali utilizzando le tecniche di Machine Learning. Lo studente deve inoltre dimostrare di saper valutare correttamente le prestazioni di un sistema di machine learning.
Lo studente deve dimostrare di essere in grado di gestire, modellare e analizzare grandi quantità di dati attraverso diversi framework Big Data per affrontare diversi compiti, valutando anche le prestazioni dell'architettura progettata.

Topics:

• Introduction to Artificial Intelligence (AI)
• Introduction to Business Intelligence
• BI tools overview
• Introduction to Big Data
• Hadoop & MapReduce
• Languages for data analysis (SQL recap, Pig Latin and HQL)
• Analytics with Pig and Hive
• NoSQL Databases
• From BI to Big Data Analytics and AI
• Overview on Machine Learning techniques supporting AI
• Google Colab for developing Big Data Analytics applications

Textbooks :
1)Alessando Rezzani, Big Data Analytics. Il manuale del data scientist”,Apogeo (Maggioli), 2017.
2)Andrea De Mauro, Big Data Analytics, Apogeo, 2019.
3) J. Leskovec, A. Rajarman, J.D.Ullman, Mining of Massive Datasets, 2014 (on line book).
Course Slides
Lecture notes

The aim of the course is to provide knowledge about techniques typical of applied mechanics
useful to improve the energy efficiency of mechanical systems. Two paths are followed for
improving efficiency: the first concerns the reduction of the energy used while the second
concerns the recovery of energy that should be dissipated. Therefore, the main causes of
dissipation due to dynamic phenomena and the techniques to reduce their effects will be faced.
Furthermore, the mechanical phenomena that allow energy recovery and the techniques used in
the case of low-power applications will be illustrated, with a focus on modelling and design methodologies
Conoscenza e capacita' di comprensione: Once the student has deepened the engineering issues related to lubrication, surface wear and
recovery of energy dispersed by vibrations, he will be able to develop energy efficiency projects.
The student will identify the main components of transmission systems and sources of inefficiency. The student will understand the basic principles of energy storage.
Capacita' di applicare conoscenza e comprensione: The student must demonstrate that he knows how to choose, among the different possibilities
available, employing between various tools and different analysis methodologies those most
suitable for solving a specific problem in the mechanical energy efficiency field. The student will
learn how to choose suitable combination of mechanical and electric components for energy
transformation, transfer and storage.

Mechanical energy efficiency Introduction to functional design, classification of the mechanisms and motion systems. Advanced model-based approaches to study energy efficiency in: • Contact mechanics • Friction phenomena • Mechanical hysteresis and viscoelasticity • Wear mechanisms • Lubricants and lubrication • Mechanical and hybrid power transmissions Mechanical components for transferring and transforming energy. Classification based on function, working principle as well as performance and efficiency. Optimal mechanical design for machine quality of motion and energy efficiency. (5 CFU)
Mechanical energy harvesting Mechanical energy sources: analysis and extraction. Cyclic sources, stationary sources, intermittent sources and noise. Energetic aspects of vibration mechanics: systems with n degrees of freedom and modal analysis. Main physical phenomena linking mechanical and electrical quantities (piezoelectric effect, Villari effect,
Triboelectricity). Electro-mechanical characteristics and dynamic behavior of piezoelectric and magnetorheological materials. Working principle and applications of triboelectric nanogenerators. Outline of electrical energy storage and management circuits. Multiphysics modelling methodologies and approaches. Energy harvesting applications for low power systems: self-powered, autonomous and smart devices. (4 CFU).

The section will be completed in accordance with the forthcoming PQA Guidelines

Slides provided to the students after each lecture. There is no single textbook that covers the
entire course. A collection of suggested readings from various sources will be announced during
the course.

The Course deals with the Bio-Inspired Generative Design (GD), a design method that mimics nature’s evolutionary approach to design, and Additive Manufacturing. GD is used to design complex shapes and optimized forms according to forces, cost, weight, environmental impact and other data that may influence the design. GD will allow to explore all the solutions in order to find the best option. Artificial intelligence (AI) and machine learning will transform the design process into a sophisticated engineer-computer interaction. Taking advantage of the Additive Manufacturing processes, complex geometries, which are in many cases technically unfeasible using conventional manufacturing methods, can be realised. Thus, the main steps from concept design to Additive Manufacturing will be developed. The presented concepts will be exploited to develop a project work, in which the students, grouped in teams, will work together on a specific case study. The CREAMI and RICREAMI laboratories will be available to the students.
Conoscenza e capacita' di comprensione: The student must demonstrate that he/she has achieved an adequate knowledge of strategies towards the design for Additive Manufacturing with a special emphasis on the following features:

• Knowledge of technical language for the communication of technical information at the international level;
• Knowledge of the basic methodologies of Design for Manufacturing and Assembly/Design for Additive Manufacturing;
• Knowledge and understanding of International Standards;
• Knowledge of the basic principles in design and development of sustainable and smart products;
• Knowledge of the basic methodologies for the assessment of the functional analysis of Additive Manufactured Products;
• To define biomimetic and bioinspired approach;
• To distinguish among different cellular structures and lattice structures;
• To describe topology optimization algorithms;
• To illustrate the main features of generative design and AI-driven process towards the development of design solutions;
• To outline the potential of developing custom-made and lightweight products.
  Capacita' di applicare conoscenza e comprensione: At the end of the Course the student must demonstrate that he/she has acquired notions of Bio-Inspired Generative Design for Additive Manufacturing and, in particular, the following abilities:
• Knowledge and planning of the functional design of additive manufactured components of systems and products by applying the basic principles of bio-inspired and generative design;
• Understanding of the effect of the manufacturing errors on the functional and mechanical characteristics of the additive manufactured products;
• Interpretation of concepts for the design of smart and sustainable products towards the ecological transition;
• Understanding of verification methodologies and technical reference standards;
• To modify conventional methodologies and to rewrite/organise novel functional analyses for
  innovative products, as a consequence of product reimagination from a new standpoint:
• To solve technical problems related to the simultaneous optimization of several response variables (e.g., mechanical and further functional features) as well as to the development of multi-material structures;
• Correct use of the developed products according to the specific applications, and production of reference documentation;
• Management and implementation of several algorithms to develop innovative products.

The course contents will cover the following aspects: ·Key Advantages of the Additive Manufacturing (AM) techniques. ·Design for Manufacturing and Assembly (DfMA) / Design for Additive Manufacturing (DfAM). ·Design for Additive Manufacturing in sustainable and smart product design and development: Trends and Opportunities. ·DfAM and International Standards. ·Understanding the influence of AM roughness, geometrical and dimensioning tolerances, mechanical properties, process parameters on DfAM. ·From Darwin’s Theory of Natural Selection to Generative Design. Nature-Inspired Metaheuristic Algorithms. Genetic Algorithms. Generative Design: challenges and innovative design solutions. Generative Design: artificial intelligence (AI) and machine learning to transform the design process into a sophisticated engineer-computer interaction. AI-driven process towards the development of design solutions. · Cellular Structures. Lattice Structures and 3D CAD Modelling. ·Geometrically Hybrid Lattice Structures and Solid-Lattice Hybrid Structures. ·Topology Optimization Algorithms. Gradient-Based Optimization Algorithms. Optimality Criteria Methods. Integrated Design Methods. · Bioinspired architectures for sustainable and smart products ·Design for Additive Manufacturing of custom-made advanced, lightweight and multi-material structures. ·Practical lessons based on project work concerning innovative architectures for sustainable and smart products.

The minimum requirements for passing the exam concern the following features: i) knowledge of the basic principles of the Design for Additive Manufacturing, bio-inspired and generative design; ii) knowledge of dedicated algorithms and optimality criteria methods; iii) ability to design cellular and lattice structures with optimized properties. The oral test focuses on the subjects of the program and starts from the discussion of a project work made by the students.
The students must use textbooks, manuals or collection of technical standards during the oral test.
The students with SLD (Specific Learning Disorders) or disabilities can use teaching support material, such as
synoptic tables and multimedia devices, with the aim to help the learning process.

All students will find technical information and/or teaching material related to classroom presentations and exercises on the teacher’s website and/or TEAMS platform. International Standards will be available.

The course is intended to:

• complete student knowledge about key technologies for unmanned/autonomous aircraft;
• provide insight and hands-on experience on state-of-the-art approaches and technologies;
• present practical cases of design and development of autonomous aircraft technologies (exploiting the potential of advanced simulation environments, working with experimental datasets and/or with real hardware, introducing the possibilities offered by mixed approaches).
  Conoscenza e capacita' di comprensione: Students should complete their knowledge regarding design options and state of the art technologies for autonomous aircraft operations and missions
  Capacita' di applicare conoscenza e comprensione: Students should learn how to apply their knowledge in designing autonomous aircraft technologies tailored for specific missions, and should develop a practical understanding of the aspects relevant to simulation-based and flight-based performance assessment.

First Part – Selected topics and tutorials
Selected Topics [4 CFU]
Complements of 3d path planning for unmanned/autonomous aircraft missions Unmanned Aircraft Systems Communications: architectures, constraints, and budgets Airborne and Ground-based Target Tracking Systems
Integration of unmanned/autonomous aircraft in the airspace

• Regulations, airspace principles
• UAS Traffic Management and Urban/Advanced Air Mobility
• Sense and Avoid
  Complements of A-PNT (Alternative/Assured Positioning Navigation and Timing) for unmanned/autonomous aircraft
  Tutorials [1 CFU]
• Open source mission planning and data processing tools
• Matlab/Simulink toolboxes and integrated environments
• Robot Operating System (ROS)
  Second Part – Design projects [4 CFU]
  The focus is set on one or more key technologies for autonomous/unmanned aircraft. Hands-on and design activities are carried out interactively by students and teacher.
• Simulation-based project(s)
• Experimental project(s)
• Flight-oriented final design project

The section will be completed in accordance with the forthcoming PQA Guidelines

Slides, lecture notes, technical papers.
Main Textbooks :
J. Gundlach, Designing Unmanned Aircraft Systems: A Comprehensive Approach, AIAA Education Series, 2012 R. Austin, Unmanned Aircraft Systems: UAVs Design, Development and Deployment, Wiley, 2010 R.W. Beard, T.W. McLain, Small Unmanned Aircraft: Theory and Practice, Princeton University Press, 2012 S. Blackman, R. Popoli, Design and analysis of modern tracking systems, Artech House, 1999. R.C. Nelson, Flight Stability and Automatic Control, McGraw Hill, 1998

• provide knowledge on different types, mission profiles and general layouts of unmanned/autonomous marine vehicles.
• provide the fundamentals for the design of unmanned/autonomous marine vehicles by focusing on "hands on" experience from state of the art approaches and technologies.
• present design cases and development of autonomous marine vehicles.
• present the modeling techniques for the ship motion control
  Conoscenza e capacita' di comprensione: The student should achieved adequate knowledge of an AUTONOMOUS MARINE SURFACE VEHICLE design, starting from the comparison with reference vessels and then continuing with the definition of the hull and the evaluation of brake horsepower, and maneuvrability, identifying the reference regulatory frameworks and construction techniques.
  Capacita' di applicare conoscenza e comprensione: At the end of the course the student should demonstrate the ability to:
• develop the preliminary design of an AUTONOMOUS MARINE SURFACE VEHICLE based on defined functional specifications and/or mission profiles.
• design, and model in numerical simulation environment, the systems to governor the ship motions

• AMV types, hull forms and configurations, mission profiles.
• Technical and peculiar features of marine design. Buoyancy, trim, stability.
• General plans, definition and representation of the hull form, evaluation of displacement, preliminary evaluation of motion resistance and brake horsepower.
• Notes on propulsion type. Technical description of marine propellers.
• Notes on materials and construction techniques.
• Hull manouvrability, course keeping, stability and control.
• Collision avoidance, COLREG

The final mark is obtained from two evaluations: 50 % oral exam and 50 % discussion and presentation of assignment reports

Dynamics of a rigid ship, by J. Matusiak
Naval architecture for non-naval architects, by H. Benford
Lecture slides

Fornire metodologie avanzate per la scelta e l’interfacciamento tra motori, strutture e componenti o gruppi di larga applicazione, a partire dalle specifiche progettuali di tipo strutturale e quantitativo di un sistema meccanico. Valutare la risposta della struttura intesa come catena elastica introducendo i criteri di deformabilità controllata. Presentare i principali elementi costruttivi dei sistemi meccatronici inclusi i sensori di misura per spostamento, deformazione e forza, esaminando le tipologie con particolare riguardo ai principi di progettazione ed alle caratteristiche di utilizzo. Presentare e sviluppare modelli costitutivi avanzati per la descrizione del comportamento meccanico dei materiali, con l’obbiettivo di determinare in modo consapevole la risposta del materiale in funzione delle condizioni ambientali, di carico o delle lavorazioni a cui è soggetto.
Conoscenza e capacita' di comprensione: Conoscenza e comprensione dei principi fondamentali di funzionamento, cinematica e progettazione di semplici sistemi meccatronici. Metodologie per l'analisi della risposta della struttura come catena elastica. Comprensione dei principali elementi costruttivi dei sistemi meccatronici. Progettazione e verifica di sistemi meccatronici soggetti a diverse forze e vincoli, in funzione della geometria, delle condizioni al contorno e delle leggi avanzate di materiale.
Capacita' di applicare conoscenza e comprensione: Lo studente sarà in grado di affrontare l'analisi del comportamento cinematico e la previsione di resistenza di sistemi meccatronici. Inoltre, sarà in grado di identificare i principi più appropriati per eseguire la progettazione strutturale di componenti di sistemi meccatronici, sulla base dei dati di partenza, delle caratteristiche di utilizzo, delle condizioni al contorno. Progettazione e verifica di elementi o sistemi relativi ad assemblaggi meccatronici sottoposti a diverse forze e vincoli, in funzione della geometria, delle condizioni al contorno e dei modelli costitutivi avanzati.

Aspetti generali e definizione dei sistemi meccatronici e dei loro componenti. Individuazione degli aspetti costruttivi di un sistema meccanico composto di struttura fissa e di membrature a geometria variabile. Individuazione delle disposizioni generali (layout) e del ciclo operativo generale e dei gruppi. Analisi dei principali tipi di vincolo (cuscinetti, giunti, guide, e slitte, etc.), trasmissioni flessibili e attuatori (viti a ricircolo di sfere e sistemi oleodinamici). Considerazioni relative ai tempi ed all’energia connessa con i transitori. Confronto tra differenti cicli con accelerazioni variabili. Distribuzione delle forze, delle reazioni e della risposta della struttura meccatronica. Analisi di elementi giuntati. Creep e stress relaxation di materiali polimerici. Scorrimento viscoso in elementi riparati. Instabilità di travi per sollecitazione assiale, flessionale e torsionale. Componentistica meccanica. Funi per sollevamento e per trasmissioni flessibili. Affidabilità strutturale

Il campo sarà definito secondo l'aggiornamento delle Linee guida del PQA in corso di predisposizione.

Dispense rese disponibili sul sito docente. Libri consigliati: R. Isermann, Mechatronic Systems. Springer, 2005. Hans Groß, Jens Hamann and Georg Wiegartner, Electrical Feed Drives in Automation: Basics, Computation, Dimensioning, Siemens, 2001. Findley WN, Lai JS, Onaran K. Creep and relaxation of nonlinear viscoelastic materials with an introduction to linear viscoelasticity. North-Holland Publishing Company; 1976.

Il corso si propone di introdurre lo studente ai modelli matematici della Meccanica Classica. Partendo dalla meccanica newtoniana si derivano le equazioni di Lagrange e di Hamilton, e i principi variazionali ad esse equivalenti.
Viene fornito il bagaglio culturale per lo studio e la risoluzione di problemi della meccanica classica.
Conoscenza e capacita' di comprensione: Lo studente dovrà dimostrare di aver raggiunto un'adeguata conoscenza della fenomenologia, delle formulazioni teoriche e della modellistica fisico-matematica della meccanica classica.
Capacita' di applicare conoscenza e comprensione: Al termine del corso lo studente dovrà dimostrare di aver acquisito concetti e tecniche da applicare per risolvere problemi di dinamica dei sistemi olonomi a uno o più gradi di libertà.

[1.5 CFU] Richiami. Richiami di calcolo vettoriale e matriciale. Richiami di cinematica del punto e di meccanica Newtoniana. Equazioni cardinali della Meccanica. Moti relativi.
[2 CFU] Meccanica Lagrangiana. Sistemi olonomi. Grado di libertà. Coordinate lagrangiane. Spostamenti infinitesimi e virtuali. Sistemi a vincoli ideali. Energia cinetica. Principio dei lavori virtuali. Principio di d’Alembert. Equazioni di Lagrange. Caso conservativo. Sistemi derivanti da un potenziale generalizzato. Integrali primi. Integrale di Jacobi e coordinate cicliche.
[1 CFU] Elementi di calcolo delle variazioni. Introduzione al calcolo delle variazioni. Problemi classici. Il problema della brachistocrona. Funzionali integrali. Perturbazioni infinitesime di una curva. Equazioni di Eulero-Lagrange di un funzionale integrale. Principio di Hamilton. Applicazioni.
[2 CFU] Dinamica dei solidi. Cinematica dei sistemi rigidi. Formule di Poisson. Atto di moto rigido. Moti relativi. Baricentro di un sistema materiale. Matrice di inerzia. Riferimenti principali di inerzia. Quantità di moto e momento angolare di un solido. Dinamica del corpo rigido libero. Equazioni di Eulero. Dinamica del corpo rigido con punto fisso. Moti alla Poinsot. Rotazioni permanenti. Dinamica del solido con asse fisso. Cimenti dinamici.
[2.5 CFU] Meccanica Hamiltoniana. Trasformata di Legendre. Equazioni canoniche della meccanica. Formulazione variazionale. Parentesi di Poisson e loro proprietà. Integrali primi. Teorema di Poisson sugli integrali primi. Coordinate cicliche. Trasformazioni canoniche. Applicazioni. Teoria di Hamilton-Jacobi. Equazione di Hamilton-Jacobi. Funzione principale di Hamilton. Teorema di Jacobi. Hamiltoniana indipendente dal tempo. Metodo di separazione delle variabili.

La valutazione finale è formulata dalla Commissione d'esame sulla base dell'adeguatezza delle risposte fornite dallo studente ai quesiti formulati. L'esame consiste nella formulazione di quesiti volti alla verifica del livello di acquisizione delle nozioni teoriche e pratiche, oggetto del corso. Il voto finale è, inoltre, opportunamente motivato allo studente durante le fasi della valutazione.
Prove in itinere e/o prova orale.

Libri di testo:
Torok J.S.: Analytical Mechanics. John Wiley & Sons, INC.
Goldstein H., Poole C.P., Safko J.: Classical Mechanics. Third Edition. Addison Wesley.
Gantmacher F.: Lectures in Analytical Mechanics.

The course aims at providing students with advanced notions related to the design and management of the propulsion systems of ground unmanned vehicles. Notions cover the operation of powertrain components and their integration, with particular focus on energy efficiency and environmental impact.
Conoscenza e capacita' di comprensione: The course provides students with knowledge and basic methodological tools needed to analyze the operation of the propulsion system of unmanned vehicles, and the mutual interaction of its sub-components.
Capacita' di applicare conoscenza e comprensione: The student needs to show ability to solve problems related to the design and optimal control of powertrain systems of unmanned vehicles. The course delivers ability and tools needed to apply knowledge in practice, favoring the ability to use methodological tools to design and control of the components of the powertrain systems, their optimal energy exchange, with the aim to maximize the energy economy.

• Environment impact of transport sector and related legislation (0.5 CFU)
• Classification of powertrains for UV (0.5 CFU)
• Fundamentals of internal combustion engines and its thermodynamics (3,5 CFU)
• Basics of Fuel Cell in propulsion applications (1 CFU)
• Thermal/ electric hybrid systems (0.5 CFU)
• On board energy and power needs (1 CFU)
• Integration, configuration, and control of the powertrain components (1 CFU)
• Numerical tools for powertrain control and management (1 CFU)

The section will be completed in accordance with the forthcoming PQA Guidelines

Slides
J.B. Heywood, Internal combustion engine fundamentals, McGraw-Hill Education, 2018, ISBN: 9781260116106.
L. Guzzella, A. Sciarretta, Vehicle Propulsion Systems, Springer-Verlag Berlin Heidelberg, 2005, doi: 10.1007/3-540-28853-8.
A. Taghavipour, M. Vajedi, N. Azad, Intelligent Control of Connected Plug-in Hybrid Electric Vehicles, Springer International Publishing, 2019, doi: 10.1007/978-3-030-00314-2.
S. Onori, L. Serrao, G. Rizzoni, Hybrid Electric Vehicles, Springer-Verlag London, 2016, doi: 10.1007/978-1-4471-6781-5.
J. Pukrushpan, A. Stefanopoulou, H. Peng - Control of Fuel Cell Power Systems. Principles, Modeling, Analysis and Feedback Design. Springer, 2004, ISBN: 978-1-4471-3792-4.

L’insegnamento si propone di fornire le conoscenze teoriche, tecnologiche e progettuali necessarie per comprendere il funzionamento e l’integrazione dei principali sensori e microsistemi elettronici.
Il corso approfondisce i meccanismi di trasduzione, le tecnologie di fabbricazione, i dispositivi sensibili e i circuiti elettronici di polarizzazione, lettura e condizionamento del segnale. Particolare attenzione è dedicata ai MEMS, ai sensori integrati, ai sensori ottici e in fibra, ai sensori chimici e biologici e alle architetture multisensore.
L’insegnamento intende infine fornire allo studente gli strumenti necessari per individuare la soluzione tecnologica e circuitale più adatta a una specifica applicazione e per sviluppare il progetto preliminare di un sistema elettronico di sensing, dalla scelta del dispositivo sensibile fino all’acquisizione e all’elaborazione del segnale.
Conoscenza e capacita' di comprensione: Lo studente sarà in grado di:

• descrivere l’architettura generale di un sistema sensoriale;
• comprendere i principali meccanismi fisici di trasduzione;
• conoscere le caratteristiche statiche e dinamiche dei sensori;
• comprendere i principi di funzionamento dei principali sensori elettronici, MEMS, ottici, chimici e biologici;
• conoscere le principali tecnologie di fabbricazione dei microsistemi;
• comprendere il funzionamento dei circuiti elettronici di lettura e condizionamento del segnale;
• identificare le principali sorgenti di rumore, deriva, non linearità e incertezza.
  Capacita' di applicare conoscenza e comprensione: Lo studente sarà in grado di:
• analizzare il comportamento di un sensore mediante modelli analitici semplificati;
• calcolare sensibilità, risoluzione, linearità, intervallo dinamico, tempo di risposta e banda;
• selezionare una tecnologia sensoriale in funzione delle specifiche applicative;
• dimensionare semplici circuiti di interfaccia per sensori resistivi, capacitivi, in corrente e in tensione;
• valutare il contributo del rumore elettronico alla risoluzione del sistema;
• analizzare dati sperimentali ottenuti dalla caratterizzazione di un sensore;
• confrontare differenti tecnologie sensoriali sulla base di prestazioni, consumi, ingombro, costo e complessità tecnologica;
• sviluppare il progetto preliminare di un sistema sensoriale.

Introduzione ai sensori e ai sistemi di misura
Caratteristiche statiche e dinamiche
Meccanismi fisici di trasduzione
Sensori elettronici e a semiconduttore
MEMS e tecnologie di microsistema
Sensori ottici e in fibra ottica
Sensori chimici e biologici
Elettronica di interfaccia per sensori
Rumore e prestazioni del sistema di misura
Array di sensori e fusione dei dati

La valutazione si basa sulla verifica delle conoscenze e delle competenze acquisite nell’ambito dei sensori e dei microsistemi, considerando la capacità di analizzarne i principi di funzionamento, le tecnologie realizzative e le principali soluzioni circuitali di interfaccia. Saranno inoltre valutate la capacità di applicare i concetti appresi a problemi di analisi e progetto, la qualità dell’elaborato progettuale e l’uso corretto del linguaggio tecnico.

Slide e dispense fornite dal docente.
Testi consigliati
J. Fraden, Handbook of Modern Sensors: Physics, Designs, and Applications , Springer.
E. Udd, W. B. Spillman, Fiber Optic Sensors: An Introduction for Engineers and Scientists , Wiley.

The course provides students with a clear and in-depth understanding of the technical and functional requirements that must be met for vehicle-road interaction in connected and automated driving scenarios.
Conoscenza e capacita' di comprensione: Students gain knowledge of digital road transformation, understanding the potential of new technologies to solve road and traffic problems. Students understand how to deal with V2X (Vehicle to Everything) communication systems and exploit the services offered by C-ITS (Cooperative Intelligent Transportation Systems) contexts, in an ecosystem where roads are equipped with traffic sensors and are interconnected and communicate with vehicles. Students acquire the methodological tools and interpretative foundations to deepen their understanding of the principles of use and operational aspects of modern traffic control centres.
Capacita' di applicare conoscenza e comprensione: Students learn to act as highly qualified technicians in the smart roads sector, using their knowledge to work for road management operators, but also in the automotive sector, developing and testing connected vehicles in interoperable vehicle-infrastructure environments.

General Principles

• Definitions and terminology for Smart Roads
• Smart roads and sensing systems
• Data Exchange protocols and models for traffic forecasting
• V2X Communication and C-ITS services
• introduction to the Italian project CCAM4Italy
  Driving behaviour and traffic theory fundamentals
• Traffic variable: microscopic, macroscopic, and relationships
• Measuring traffic variable
• (sensing technologies and systems)
• Fundamental traffic equations
• The impact of driving behaviour on traffic
• (safety vs behavioural density/speed relationship)
• Understanding the impact of driving behaviour on road capacity
• Understanding the effect of cooperative, connected and automated driving on road capacity
  Simulation and co-simulation of traffic in Smart Roads environments
• Simulating traffic
• Integrating other components
• Integrating communication and C-ITS services
• Fundamentals of co-simulation
  Developing a complete co-simulation environment and testing ITS and C-ITS solutions

The examination is oral. Students will be asked to discuss the work carried out in class, with particular reference to the development and application of a complete co-simulation environment for smart roads. Two questions will also be asked about the theoretical foundation of Driving Behaviour and Smart Roads. Students will be required to respond to the lecturer's requests for further explanation and elaboration to defend their answers. Fully and properly presenting the classwork and the student's original contribution is mandatory for a positive assessment. An appropriate, fluid discussion that demonstrates full awareness of the theoretical and practical topics covered in the course will be rewarded with the highest marks. A discussion that demonstrates a correct answer to the questions but with weak arguments and a lack of precision in the terminology used and the arguments presented will result in an intermediate mark. Students who demonstrate they can answer only one question will receive low marks. Students who demonstrate more than one knowledge gap in their presentation will not be allowed to pass the exam.

Slides, lecture notes, technical papers, and regulatory documents.

The course is aimed at introducing the methods of space flight dynamics that are applied to real space systems. Starting from the basic knowledge linked to two-body mechanics, several topics will be covered in depth, including orbit perturbations analysis and propagation methods, orbital maneuvers, orbit maintenance approaches, and interplanetary trajectories. Special emphasis will also be given to the study of relative dynamics in space and its application to distributed space systems, and to autonomous rendezvous and docking in missions such as on orbit servicing and active debris removal.
Conoscenza e capacita' di comprensione: The student should achieve an adequate knowledge of methods and phenomena related to space flight dynamics
Capacita' di applicare conoscenza e comprensione: The student must demonstrate that he/she is able to apply the concepts acquired in the study of the space flight dynamics with specific reference to orbital maneuvers, orbit maintenance, relative dynamics in space, orbit design

[1 CFU] Orbital maneuvers and launch constraints
[1 CFU] Fundamentals of orbit determination and estimation, introduction to space surveillance
[3 CFU] Orbit perturbations analysis:

• General and special perturbation techniques. Orbit propagation methods and tools.
• Perturbation effects on different classes of Earth orbits.
  [1 CFU] Orbit maintenance for LEO and GEO satellites
  [2 CFU] Relative motion in space:
• Hill's equations and advanced mathematical models.
• Formation flying, on orbit monitoring, rendezvous and docking.
  •Spaceborne collision avoidance strategies.
  [1 CFU] Fundamentals of interplanetary trajectories.

The section will be completed in accordance with the forthcoming PQA Guidelines

Slides, lecture notes, technical papers. Main textbooks: D.A. Vallado, Fundamentals of Astrodynamics and Applications, 4th ed., Springer Space Technology Library, 2013. R. R. Bate, Fundamentals of Astrodynamics, Dover Publications, 1972. V.A. Chobotov, Orbital Mechanics, AIAA Education Series, 2002.

The course is intended to provide students with knowledge of engineering problems related to design and operations of unmanned aircraft system. The course tackles the problem of autonomy for aircraft with specific reference to unmanned aircraft systems. UAS are natively realized to perform most of the tasks in an automatic fashion. Very high level of automation, where a number of subsequent decision branches are programmed into the on-board computer, progressively enables fully autonomous aircraft operations. The course covers basic topics of flight dynamics and control, and applies standard concepts of linear system control theory to enable the design of the onboard autopilot and its integration with autonomous tasks to be performed at higher system level, e.g. path planning and management.
Conoscenza e capacita' di comprensione: Students should learn the basic concepts of unmanned aircraft systems for autonomous operations and missions.
Capacita' di applicare conoscenza e comprensione: Students should learn the drivers for designing unmanned aircraft systems with reference to the common engineering problems associated with autonomous flight dynamics and control, autonomous navigation, autonomous guidance and path planning.

[0.5 CFU] Intro duction to Autonomous Aircraft. Autonomy in aviation. Levels of Automation. Airworthiness Certification. System Representation of Unmanned Aircraft. Categories of Systems based upon Air Vehicle Types. The System Basis of UAS: Control Station, Payload, Air Vehicle, Navigation System, Launch, Recovery and Transportation Equipment, Communications. UAS System Architecture and the Design Process. [1 CFU] Coordinate Frames. Inertial, Body, and Wind frame. Euler’s Angles. Wind triangle. State Variables for aircraft motion modelling. Translational and Rotational Kinematics. Rigid Body Dynamics. Aerodynamic Forces and Moments. Control Surfaces. Laminar and turbulent flow. Stall. Aircraft static and dynamic stability. Stability and control derivatives. Propulsion forces and moments. Atmospheric disturbances. [1 CFU] Linear Design Models and Open-Loop Dynamics. Coordinated Turn and Trim Conditions. Transfer function models. Block Diagram for lateral dynamics controlled by the ailerons. Block Diagram for rudder to sideslip dynamics. Longitudinal Transfer function for aileron to pitch and altitude. Transfer function from pitch and throttle to the airspeed. [0.5 CFU] Linear State-space Models. Lateral and Longitudinal State-space Equations. Reduced order Modes. Short Period Mode, Phugoid Mode, Roll Mode, Spiral-divergence Mode, Dutch-roll Mode. [1 CFU] Autopilot Design by Successive Loop Closure. Saturation limits and controller gain selection. Lateral-directional autopilot. Longitudinal Autopilot Design. Altitude-control State Machine. [0.5 CFU] Sensors and State Estimation. Accelerometers, Rate gyros, Pressure sensors, Magnetometers, GNSSs. Extended Kalman Filter for attitude determination and GNSS smoothing. [1 CFU] Design Models for Guidance. Path Planner, Path Manager and Path Follower. [0.5 CFU] Multirotor Aerial Vehicles. Modeling and Simulation. State Estimation and Control of Multirotor Aerial Vehicle. Vision-based altitude hold and position tracking. Hierarchical approach and bandwidth separation in control architectures. Piloting modes. Behavior-based autonomy.

The section will be completed in accordance with the forthcoming PQA Guidelines

J. Gundlach, Designing Unmanned Aircraft Systems, ISBN 978-1-60086-843-6, Jan. 2012, AIAA Education Series. R. Austin, Unmanned Aircraft Systems, UAVs Design, Development and Deployment, John Wiley &Sons, 2010. R. Beard, T. McLain, Small Unmanned Aircraft: Theory and Practice, Princeton University Press, 2012. Y. B. Sebbane, Smart Autonomous Aircraft, Flight Control and Planning for UAV, CRC Press, 2016.

Il corso fornisce agli studenti le competenze teoriche, metodologiche e tecniche necessarie per la verifica e la validazione delle funzionalità di guida assistita e automatizzata dei veicoli stradali. Dopo un inquadramento delle funzionalità di automazione e degli indicatori di prestazione (KPI) impiegati per valutarne sicurezza, efficienza e comfort, vengono presentati e confrontati i diversi approcci di testing — prove reali, studi di guida naturalistica, protocolli standardizzati e prove virtuali — insieme al quadro normativo e regolamentare di riferimento. Le attività si focalizzano quindi sull'utilizzo di ambienti di simulazione del traffico veicolare e su tecniche di co-simulazione in configurazioni model-in-the-loop e hardware-in-the-loop, con particolare riferimento al coinvolgimento di scenari di traffico complessi e realistici nelle attività di testing. Il corso mira infine a sviluppare la capacità di progettare una campagna di testing virtuale, di analizzare statisticamente i risultati e di valutare criticamente le prestazioni delle funzionalità automatizzate, competenze applicate nello sviluppo di un elaborato progettuale.
Conoscenza e capacita' di comprensione: Al termine del corso gli studenti conoscono le principali funzionalità di guida assistita e automatizzata e i livelli di automazione, e comprendono vantaggi e svantaggi dei diversi approcci di testing (prove reali, studi di guida naturalistica, protocolli EuroNCAP, prove virtuali). Acquisiscono la conoscenza delle tecniche per eseguire attività di test virtuali — con particolare riferimento all'incorporazione dei fattori del traffico nel processo di validazione dei veicoli automatizzati — dei principali KPI e dei metodi statistici per la loro analisi, nonché del quadro normativo e regolamentare di riferimento, comprendendo l'effetto del codice della strada sulle prestazioni e sulla sicurezza del veicolo in contesti di guida assistita e automatizzata. Conoscono inoltre i principi metodologici e operativi della co-simulazione del traffico in ambienti di validazione model-in-the-loop e hardware-in-the-loop.
Capacita' di applicare conoscenza e comprensione: Gli studenti sono in grado di applicare le conoscenze acquisite alla progettazione e implementazione di campagne di testing virtuale in ambienti di simulazione del traffico: sanno selezionare gli scenari di test e i KPI appropriati per la funzionalità in esame, costruire e calibrare modelli di simulazione, adottare tecniche di co-simulazione in configurazioni model-in-the-loop e hardware-in-the-loop, analizzare statisticamente i risultati e trarne valutazioni fondate sulle prestazioni e sulla sicurezza delle funzionalità automatizzate. Tali capacità vengono esercitate e verificate attraverso casi di studio pratici e lo sviluppo di un elaborato progettuale.

Approcci di Testing (Real Tests, Naturalistic Driving Studies, EuroNCAP) (1.5 CFU)
Testing Scenarios taxonomy (0.5 CFU)
Testing regulations and norms (1.5 CFU)
Modellazione dei sistemi di trasporto tramite ambienti di simulazione del traffico veicolare (1 CFU)
Analisi statistica dei KPIs (1 CFU)
Implementazione di attività di testing in ambienti di simulazione adeguati (2.75 CFU)

La sezione verrà completata in conformità con le prossime linee guida PQA.

Diapositive, dispense, documenti tecnici, attività di laboratorio, apprendimento pratico e apprendimento basato su challenges.

The course aims to provide the student with the basic knowledge of propulsion and auxiliary systems of seagoing ships, with particular reference to their automatic management.
Conoscenza e capacita' di comprensione: The student will be familiar with the ship technology and the marine environment. He should know the main aspects of the most important systems onboard and their automatic operation under different environmental conditions. He will be able to understand P&I diagrams of ship plants in order to provide automatic operation.
Capacita' di applicare conoscenza e comprensione: The student must demonstrate that he is able to read and interpret P&ID diagram relating to the main ship installations and its connection with the relative automatic control logic. He must also show an understanding of the operating principles of the systems presented during the course.

Introduction on ship technology and regulations. The role of classification society and the rules for unmanned plants. – 1 CFU
Propulsion plants and machinery automation – 2 CFU
The electric power generation plant and its management – 1 CFU
Integrated ship automation – 0.5 CFU
Simulation of automated marine systems – 1 CFU
Introduction to autonomous ships – 0.5 CFU

The section will be completed in accordance with the forthcoming PQA Guidelines

Slides, lecture notes, technical papers.
Textbooks:

• Woud H. K., Stapersma D., Design of Propulsion and Electric Power Generation Systems, Institute of Marine Engineers, 2002
• Harrington, R.L. , Marine Engineering, Society of Naval Architects &; Revised 1992
• Cowley, J. , The Running and Maintenance of Marine Machinery, Marine Management Ltd.; 6th edition 1992