Analysis and development of interface components and procedures for the design and commissioning toolchains in automation systems

The objective of the thesis project, developed within the Line Control & Software Engineering team of G.D company, is to analyze and identify the appropriate tool to automate the HW configuration process using Beckhoff technologies by importing data from an ECAD tool. This would save a great deal of time, since the I/O topology created as part of the electrical planning is presently imported manually in the related SW project of the machine. Moreover, a manual import is more error-prone because of human mistake than an automatic configuration tool. First, an introduction about TwinCAT 3, EtherCAT and Automation Interface is provided; then, it is analyzed the official Beckhoff tool, XCAD Interface, and the requirements on the electrical planning to use it: the interface is realized by means of the AutomationML format. Finally, due to some limitations observed, the design and implementation of a company internal tool is performed. Tests and validation of the tool are performed on a sample production line of the company.

Optimal bidding strategies of an electric vehicle aggregator in an electricity market

In recent years Electric Vehicles (EVs) are getting more importance as future transport systems, due to the increase of the concerns relevant to the greenhouse gases emission and the use fossil fuel. The management of the charging and discharging process of EVs could provide new business model for participating in the electricity markets. Moreover, vehicle to grid systems have the potential of increasing utility system flexibility. This thesis develops some models for the optimal integration of the EVs in the electricity market. In particular, the thesis focuses on the optimal bidding strategy of an EV aggregator participating to both the day ahead market and the secondary reserve market. The aggregator profit is maximized taking into account the energy balance equation, as well as the technical constraints of energy settlement, power supply and state of charge of the EVs. The results obtained by using the GAMS (General Algebraic Modelling System) environment are presented and discussed.

Effective procedure for the optimization of the linear controller in an industrial drive for a nonlinear mechanism

Il progetto di tesi è incentrato sull’ottimizzazione del procedimento di taratura dei regolatori lineari degli anelli di controllo di posizione e velocità presenti negli azionamenti usati industrialmente su macchine automatiche, specialmente quando il carico è ad inerzia variabile in dipendenza dalla posizione, dunque non lineare, come ad esempio un quadrilatero articolato. Il lavoro è stato svolto in collaborazione con l’azienda G.D S.p.A. ed il meccanismo di prova è realmente utilizzato nelle macchine automatiche per il packaging di sigarette. L’ottimizzazione si basa sulla simulazione in ambiente Matlab/Simulink dell’intero sistema di controllo, cioè comprensivo del modello Simulink degli anelli di controllo del drive, inclusa la dinamica elettrica del motore, e del modello Simscape del meccanismo, perciò una prima necessaria fase del lavoro è stata la validazione di tali modelli affinché fossero sufficientemente fedeli al comportamento reale. Il secondo passo è stato fornire una prima taratura di tentativo che fungesse da punto di partenza per l’algoritmo di ottimizzazione, abbiamo fatto ciò linearizzando il modello meccanico con l’inerzia minima e utilizzando il metodo delle formule di inversione per determinare i parametri di controllo. Già questa taratura, seppur conservativa, ha portato ad un miglioramento delle performance del sistema rispetto alla taratura empirica comunemente fatta in ambito industriale. Infine, abbiamo lanciato l’algoritmo di ottimizzazione definendo opportunamente la funzione di costo, ed il risultato è stato decisamente positivo, portando ad un miglioramento medio del massimo errore di inseguimento di circa il 25%, ma anche oltre il 30% in alcuni casi.

Task analysis for HMI design. Level of automation study of aircraft navigation functions

Advancements in technology have enabled increasingly sophisticated automation to be introduced into the flight decks of modern aircraft. Generally, this automation was added to accomplish worthy objectives such as reducing flight crew workload, adding additional capability, or increasing fuel economy. Automation is necessary due to the fact that not all of the functions required for mission accomplishment in today’s complex aircraft are within the capabilities of the unaided human operator, who lacks the sensory capacity to detect much of the information required for flight. To a large extent, these objectives have been achieved. Nevertheless, despite all the benefits from the increasing amounts of highly reliable automation, vulnerabilities do exist in flight crew management of automation and Situation Awareness (SA). Issues associated with flight crew management of automation include: • Pilot understanding of automation’s capabilities, limitations, modes, and operating principles and techniques. • Differing pilot decisions about the appropriate automation level to use or whether to turn automation on or off when they get into unusual or emergency situations. • Human-Machine Interfaces (HMIs) are not always easy to use, and this aspect could be problematic when pilots experience high workload situations. • Complex automation interfaces, large differences in automation philosophy and implementation among different aircraft types, and inadequate training also contribute to deficiencies in flight crew understanding of automation.

Integration of the computer controlled screwdriver for Robotic manufacturing in the ROS environment

The computer controlled screwdriver is a modern technique to perform automatic screwing/unscrewing operations.The main focus is to study the integration of the computer controlled screwdriver for Robotic manufacturing in the ROS environment.This thesis describes a concept of automatic screwing mechanism composed by universal robots, in which one arm of the robot is for inserting cables and the other is for screwing the cables on the control panel switch gear box. So far this mechanism is carried out by human operators and is a fairly complex one to perform, due to the multiple cables and connections involved. It's for this reason that an automatic cabling and screwing process would be highly preferred within automotive/automation industries. A study is carried out to analyze the difficulties currently faced and a controller based algorithm is developed to replace the manual human efforts using universal robots, thereby allowing robot arms to insert the cables and screw them onto the control panel switch gear box. Experiments were conducted to evaluate the insertion and screwing strategy, which shows the result of inserting and screwing cables on the control panel switch gearbox precisely.

On the Role of Automatic Guided Vehicles in the 4th Industrial Revolution

Elaborate presents automated guided vehicle state-of-art, describing AGVs' types and employed technologies. AGVs' applications is going to be exposed by means of performed work during Toyota's internship. It will be presented the acquired experience on automatic forklifts' implementation and tools employed in a realization of an AGV system. Morover, it will be presented the development of a python program able to retrieve data, stored in a database, and elaborate them to produce heatmaps on vehicles' errors. The said program has been tested live on customer's sites and obtained result will be explained. Finally, it is going to be presented the analysis on natural navigation technology applied to Toyota's AGVs. Tests on natural navigation have been run in warehouses to estimate capabilities and possible application in logistic field.

Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Integration of ROS for the control of industrial robotic systems in a proprietary environment

The paper deals with the integration of ROS, in the proprietary environment of the Marchesini Group company, for the control of industrial robotic systems. The basic tools of this open-source software are deeply studied to model a full proprietary Pick and Place manipulator inside it, and to develop custom ROS nodes to calculate trajectories; speaking of which, the URDF format is the standard to represent robots in ROS and the motion planning framework MoveIt offers user-friendly high-level methods. The communication between ROS and the Marchesini control architecture is established using the OPC UA standard; the tasks computed are transmitted offline to the PLC, supervisor controller of the physical robot, because the performances of the protocol don’t allow any kind of active control by ROS. Once the data are completely stored at the Marchesini side, the industrial PC makes the real robot execute a trajectory computed by MoveIt, so that it replicates the behaviour of the simulated manipulator in Rviz. Multiple experiments are performed to evaluate in detail the potential of ROS in the planning of movements for the company proprietary robots. The project ends with a small study regarding the use of ROS as a simulation platform. First, it is necessary to understand how a robotic application of the company can be reproduced in the Gazebo real world simulator. Then, a ROS node extracts information and examines the simulated robot behaviour, through the subscription to specific topics.

Robotic Manipulation of DLOs for Wiring Harness Assembly: a Machine Learning Approach

In recent times, a significant research effort has been focused on how deformable linear objects (DLOs) can be manipulated for real world applications such as assembly of wiring harnesses for the automotive and aerospace sector. This represents an open topic because of the difficulties in modelling accurately the behaviour of these objects and simulate a task involving their manipulation, considering a variety of different scenarios. These problems have led to the development of data-driven techniques in which machine learning techniques are exploited to obtain reliable solutions. However, this approach makes the solution difficult to be extended, since the learning must be replicated almost from scratch as the scenario changes. It follows that some model-based methodology must be introduced to generalize the results and reduce the training effort accordingly. The objective of this thesis is to develop a solution for the DLOs manipulation to assemble a wiring harness for the automotive sector based on adaptation of a base trajectory set by means of reinforcement learning methods. The idea is to create a trajectory planning software capable of solving the proposed task, reducing where possible the learning time, which is done in real time, but at the same time presenting suitable performance and reliability. The solution has been implemented on a collaborative 7-DOFs Panda robot at the Laboratory of Automation and Robotics of the University of Bologna. Experimental results are reported showing how the robot is capable of optimizing the manipulation of the DLOs gaining experience along the task repetition, but showing at the same time a high success rate from the very beginning of the learning phase.

Development of a Robotic Wires Storage System for Switchgear Manufacturing Automation

Considering the great development of robotics in industrial automation, the Remodel project aims to reproduce, through the use of Cobots, the wiring activity typical of a human operator and to realize an autonomous storage work. My researches focused on this second topic. In this paper, we will see how to realize a gripper compatible with an Omron TM5X-900, able to perform a pick and place of different types of cables, but also how to compute possible trajectories. In particular, what I needed, was a trajectory going from the Komax, the cables production machine, to a Warehouse taking into account the possible entangles of cables with the robot during its motion. The last part has been dedicated to the synchronization between robot and main machine work.

Mitigating non-Lambertian surfaces issues in Stereo Matching with Neural Radiance Fields

Depth estimation from images has long been regarded as a preferable alternative compared to expensive and intrusive active sensors, such as LiDAR and ToF. The topic has attracted the attention of an increasingly wide audience thanks to the great amount of application domains, such as autonomous driving, robotic navigation and 3D reconstruction. Among the various techniques employed for depth estimation, stereo matching is one of the most widespread, owing to its robustness, speed and simplicity in setup. Recent developments has been aided by the abundance of annotated stereo images, which granted to deep learning the opportunity to thrive in a research area where deep networks can reach state-of-the-art sub-pixel precision in most cases. Despite the recent findings, stereo matching still begets many open challenges, two among them being finding pixel correspondences in presence of objects that exhibits a non-Lambertian behaviour and processing high-resolution images. Recently, a novel dataset named Booster, which contains high-resolution stereo pairs featuring a large collection of labeled non-Lambertian objects, has been released. The work shown that training state-of-the-art deep neural network on such data improves the generalization capabilities of these networks also in presence of non-Lambertian surfaces. Regardless being a further step to tackle the aforementioned challenge, Booster includes a rather small number of annotated images, and thus cannot satisfy the intensive training requirements of deep learning. This thesis work aims to investigate novel view synthesis techniques to augment the Booster dataset, with ultimate goal of improving stereo matching reliability in presence of high-resolution images that displays non-Lambertian surfaces.

Development and testing of a safety module for diagnostics and protection from hazards in secondary woodworking machinery

The main theme covered by this dissertation is safety, set in the context of automatic machinery for secondary woodworking. The thesis describes in detail the project of a software module for CNC machining centers to protect the operator against hazards and to report errors in the machine safety management. Its design has been developed during an internship at SCM Group technical department. The development of the safety module is addressed step by step in a detailed way: first the company and the reference framework are introduced and then all the design choices are explained and justified. The discussion begins with a detailed analysis of the standards concerning woodworking machines and safety-related software. In this way, a clear and linear procedure can be established to develop and implement the internal structure of the module, its interface, and its application to specific safety-critical conditions. Afterwards, particular attention is paid to software testing, with the development of a comprehensive test procedure for the module, and to diagnostics, especially oriented towards signal management in IoT mode. Finally, the safety module is used as an anti-regression tool to initiate a design improvement of the machine control program. The refactoring steps performed in the process are explained in detail and the SCENT approach is introduced to test the result.

Development of Optimal Energy Management Strategies for a Hybrid Boat

The present work proposes different approaches to extend the mathematical methods of supervisory energy management used in terrestrial environments to the maritime sector, that diverges in constraints, variables and disturbances. The aim is to find the optimal real-time solution that includes the minimization of a defined track time, while maintaining the classical energetic approach. Starting from analyzing and modelling the powertrain and boat dynamics, the energy economy problem formulation is done, following the mathematical principles behind the optimal control theory. Then, an adaptation aimed in finding a winning strategy for the Monaco Energy Boat Challenge endurance trial is performed via ECMS and A-ECMS control strategies, which lead to a more accurate knowledge of energy sources and boat’s behaviour. The simulations show that the algorithm accomplishes fuel economy and time optimization targets, but the latter adds huge tuning and calculation complexity. In order to assess a practical implementation on real hardware, the knowledge of the previous approaches has been translated into a rule-based algorithm, that let it be run on an embedded CPU. Finally, the algorithm has been tuned and tested in a real-world race scenario, showing promising results.

Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems.

In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.

The road to augmented reality: a comprehensive study of hardware and applications over the last ten years

Augmented reality has been growing extensively over the years in all aspects and multiple fields. My aim in this paper is to present a comprehensive study on augmented reality(AR) hardware and its applications from early developments to the possible future trends. Particularly my research is more focused on last 11 years(2012-2022), where I systematically reviewed 30 research papers per year to get a clear knowledge on trends of AR. A total of 330 publications were reviewed and grouped according to their application. The review's main contribution is to show the entire landscape of AR research and to provide a broad view of how it has evolved. Along with various AR glasses history and specifications are presented in detail. In the penultimate chapter I explained my methodology of research following my analysis from the past to the present along with my thoughts for the future. To conclude my study, In the final chapter I made some statements about possible future with AR, VR and XR(extended reality).