Development of a state-variable filter for a motion control application.

In collaboration with G.D. SpA I attended an internship with the purpose of developing a filter for the position control of industrial machines during testing and maintenance operations. The filter elaborates a signal in position provided by an electonic handwheel, in order to enable the application to be controlled with a signal in velocity with arbitrarily dynamics chosen during the design phase. Limiting the dynamics of the filter provide a more stable and less demanding reference trajectory which reduce the vibrations and tracking errors of the motor controlled by it. It also prevents misusages of the handwheel from the technician which could end up in harmful interferences between the mechanical parts moved by the handwheel.

A comprehensive study on robotic manipulation of deformable linear objects

There are many deformable objects such as papers, clothes, ropes in a person’s living space. To have a robot working in automating the daily tasks it is important that the robot works with these deformable objects. Manipulation of deformable objects is a challenging task for robots because these objects have an infinite-dimensional configuration space and are expensive to model, making real-time monitoring, planning and control difficult. It forms a particularly important field of robotics with relevant applications in different sectors such as medicine, food handling, manufacturing, and household chores. In this report, there is a clear review of the approaches used and are currently in use along with future developments to achieve this task. My research is more focused on the last 10 years, where I have systematically reviewed many articles to have a clear understanding of developments in this field. The main contribution is to show the whole landscape of this concept and provide a broad view of how it has evolved. I also explained my research methodology by following my analysis from the past to the present along with my thoughts for the future.

Simulation and Implementation of a PMSM Sensorless Control Algorithm for Submarine Applications

In this thesis, the study and the simulation of two advanced sensorless speed control techniques for a surface PMSM are presented. The aim is to implement a sensorless control algorithm for a submarine auxiliary propulsion system. This experimental activity is the result of a project collaboration with L3Harris Calzoni, a leader company in A&D systems for naval handling in military field. A Simulink model of the whole electric drive has been developed. Due to the satisfactory results of the simulations, the sensorless control system has been implemented in C code for STM32 environment. Finally, several tests on a real brushless machine have been carried out while the motor was connected to a mechanical load to simulate the real scenario of the final application. All the experimental results have been recorded through a graphical interface software developed at Calzoni.

Virtualization of Wiring Harness Manipulation Tasks through the PyChrono Simulation Engine

The purpose of this thesis work is the study and creation of a harness modelling system. The model needs to simulate faithfully the physical behaviour of the harness, without any instability or incorrect movements. Since there are various simulation engines that try to model wiring's systems, this thesis work focused on the creation and test of a 3D environment with wiring and other objects through the PyChrono Simulation Engine. Fine-tuning of the simulation parameters were done during the test to achieve the most stable and correct simulation possible, but tests showed the intrinsic limits of the Engine regarding the collisions' detection between the various part of the cables, while collisions between cables and other physical objects such as pavement, walls and others are well managed by the simulator. Finally, the main purpose of the model is to be used to train Artificial Intelligence through Reinforcement Learnings techniques, so we designed, using OpenAI Gym APIs, the general structure of the learning environment, defining its basic functions and an initial framework.

Analysis and improvements of an existing industrial hot stamping machine

The importance of product presentation in the marketing industry is well known. Labels are crucial for providing information to the buyer, but at a modest additional expense, a beautiful label with exquisite embellishments may also give the goods a sensation of high quality and elegance. Enhancing the capabilities of stamping machines is required to keep up with the increasing velocity of the production lines in the modern manufacturing industry and to offer new opportunities for customization. It’s in this context of improvements and refinements that this work takes place. The thesis was developed during an internship at Studio D, the firm that designs the mechanics of the machines produced by Cartes. The The aim of this work is to study possible upgrades for the existing hot stamping machines. The main focus of this work is centred on two objectives: first, evaluating the pressing forces generated by this machine and characterising how the mat used in the stamping process reacts to such forces. Second, propose a new conformation for the press mechanism in order to improve the rigidity and performance of the machines. The first objective is reached through a combined approach: the mat is crudely characterized with experimental data, while the frame of the machine is studied through FEM analysis. The results obtained are combined and used to upgrade a worksheet that allows to estimate the forces exerted by the machines. The second objective is reached with the proposal of new, improved designs for the main components of the machines.

Design and implementation of a synchronous interleaved boost converter for fuel cell-powered catamaran

The present work describes the different stages of design, implementation, and validation procedures for an interleaved DC-DC boost converter intended for the 2022 Futura, a fuel cell-powered racing catamaran developed by the UniBoAT team. The main goal of the entire design has been the significant reduction of the weight of the converter by removing heat sinks and reducing component size while increasing its efficiency by adopting high-end power switches and the interleaved architecture operated with a synchronous control strategy. The obtained converter has been integrated into the structure containing the fuel cell stack obtaining a fully integrated system. The realized device has been based on an interleaved architecture with six phases controlled digitally through the average current mode control. The design has been validated through simulations carried out using the software LT-Spice, whereas experimental validations have been performed by means of laboratory bench tests and on-field tests. Detailed thermal and efficiency analyses are provided with the bench tests under the two synchronous and non-synchronous operating modes and with the adoption of the phase shedding technique. The prototype implementation and its performance in real operating conditions are also discussed. Eventually, it is underlined as the designed converter can be used in other applications requiring a voltage-controlled boost converter.

Machine Learning algorithm on seat comfort level measurement, mapping pressure values with body regions

The comfort level of the seat has a major effect on the usage of a vehicle; thus, car manufacturers have been working on elevating car seat comfort as much as possible. However, still, the testing and evaluation of comfort are done using exhaustive trial and error testing and evaluation of data. In this thesis, we resort to machine learning and Artificial Neural Networks (ANN) to develop a fully automated approach. Even though this approach has its advantages in minimizing time and using a large set of data, it takes away the degree of freedom of the engineer on making decisions. The focus of this study is on filling the gap in a two-step comfort level evaluation which used pressure mapping with body regions to evaluate the average pressure supported by specific body parts and the Self-Assessment Exam (SAE) questions on evaluation of the person’s interest. This study has created a machine learning algorithm that works on giving a degree of freedom to the engineer in making a decision when mapping pressure values with body regions using ANN. The mapping is done with 92% accuracy and with the help of a Graphical User Interface (GUI) that facilitates the process during the testing time of comfort level evaluation of the car seat, which decreases the duration of the test analysis from days to hours.

Study of an advanced transport system exploiting magnetic levitation: the Beckhoff XPlanar

The transport system is one of the most important components to be chosen in the design of an automatic machine. There is a wide variety of different choices that can be made in picking this element, each one having its own strengths and its own drawbacks. If it is desired to obtain some elaborate behaviour from the transport system, it is a good idea to think about some flexible and advanced solutions. Among these transport systems, the newest is the Beckhoff XPlanar. This transport system exploits magnetic levitation to move some passive magnetic movers on a completely customizable plane, in an entirely contact-free way. This provides a fast, clean, and noiseless motion, which is extremely desirable in a modern automatic machine. The purpose of this Thesis is to analyse the potentialities and the problems of this new device, starting from the basics. After having presented in detail the topic, an analysis on the hardware components needed to build this system is performed. Then, it is conducted a study on the concepts needed to know how to build a controller having the purpose of dealing with this system. After that, the various types of motion are studied and executed and, later on, some experiments on the real kit are carried out. These studies start from the diagnostic and involve other analyses that are used to test the limits of this transport system. In performing these analyses, it is noticed how the kit presents some problems in reaching the limits of the dynamics. Finally, two different types of station cycle are implemented, which are useful to get a rough idea on the potentialities of this new advanced transport system.

Axis control optimization: autotuning techniques and frequency response analysis of industrial mechatronic systems

In the field of industrial automation, there is an increasing need to use optimal control systems that have low tracking errors and low power and energy consumption. The motors we are dealing with are mainly Permanent Magnet Synchronous Motors (PMSMs), controlled by 3 different types of controllers: a position controller, a speed controller, and a current controller. In this thesis, therefore, we are going to act on the gains of the first two controllers by going to find, through the TwinCAT 3 software, what might be the best set of parameters. To do this, starting with the default parameters recommended by TwinCAT, two main methods were used and then compared: the method of Ziegler and Nichols, which is a tabular method, and advanced tuning, an auto-tuning software method of TwinCAT. Therefore, in order to analyse which set of parameters was the best,several experiments were performed for each case, using the Motion Control Function Blocks. Moreover, some machines, such as large robotic arms, have vibration problems. To analyse them in detail, it was necessary to use the Bode Plot tool, which, through Bode plots, highlights in which frequencies there are resonance and anti-resonance peaks. This tool also makes it easier to figure out which and where to apply filters to improve control.

Fleet Management System for Search and Rescue Operations

Over one million people lost their lives in the last twenty years from natural disasters like wildfires, earthquakes and man-made disasters. In such scenarios the usage of a fleet of robots aims at the parallelization of the workload and thus increasing speed and capabilities to complete time sensitive missions. This work focuses on the development of a dynamic fleet management system, which consists in the management of multiple agents cooperating in order to accomplish tasks. We presented a Mixed Integer Programming problem for the management and planning of mission’s tasks. The problem was solved using both an exact and a heuristic approach. The latter is based on the idea of solving iteratively smaller instances of the complete problem. Alongside, a fast and efficient algorithm for estimation of travel times between tasks is proposed. Experimental results demonstrate that the proposed heuristic approach is able to generate quality solutions, within specific time limits, compared to the exact one.

Realizzazione di uno slave EtherCAT integrante periferiche per applicazioni motion control

The technological enhancement of industrial automation and manufacturing is stricty connected to the innovations of communication technologies. The main impact of the last century is due to the introduction of FieldBus systems. Indeed, they have been fundamental for the lowest levels of the automation hierarchy. Besides factory automation, many processes nowadays would not be feasible without Fieldbus based networks. Indeed, these systems are employed in a large variety of application areas from energy distribution to in-vehicle networking but also in rail-way applications and avionics. In the following document, the main activities executed during the internship in I.M.A. S.p.A. are reported. The objective of the thesis is to develop an EtherCAT (Ethernet Fieldbus) slave integrated with peripherals for motion control applications. The slave is created by exploiting a micro-controller of Renesas Electronics called RX72M. Since, for the specific application the MCU lacks of several components needed for motion control, external devices are employed for developing the project.

Continual learning on nano drones

Miniaturized flying robotic platforms, called nano-drones, have the potential to revolutionize the autonomous robots industry sector thanks to their very small form factor. The nano-drones’ limited payload only allows for a sub-100mW microcontroller unit for the on-board computations. Therefore, traditional computer vision and control algorithms are too computationally expensive to be executed on board these palm-sized robots, and we are forced to rely on artificial intelligence to trade off accuracy in favor of lightweight pipelines for autonomous tasks. However, relying on deep learning exposes us to the problem of generalization since the deployment scenario of a convolutional neural network (CNN) is often composed by different visual cues and different features from those learned during training, leading to poor inference performances. Our objective is to develop and deploy and adaptation algorithm, based on the concept of latent replays, that would allow us to fine-tune a CNN to work in new and diverse deployment scenarios. To do so we start from an existing model for visual human pose estimation, called PULPFrontnet, which is used to identify the pose of a human subject in space through its 4 output variables, and we present the design of our novel adaptation algorithm, which features automatic data gathering and labeling and on-device deployment. We therefore showcase the ability of our algorithm to adapt PULP-Frontnet to new deployment scenarios, improving the R2 scores of the four network outputs, with respect to an unknown environment, from approximately [−0.2, 0.4, 0.0,−0.7] to [0.25, 0.45, 0.2, 0.1]. Finally we demonstrate how it is possible to fine-tune our neural network in real time (i.e., under 76 seconds), using the target parallel ultra-low power GAP 8 System-on-Chip on board the nano-drone, and we show how all adaptation operations can take place using less than 2mWh of energy, a small fraction of the available battery power.

Robotic grasping: State of the art and modular real-world implementation

Robotic Grasping is an important research topic in robotics since for robots to attain more general-purpose utility, grasping is a necessary skill, but very challenging to master. In general the robots may use their perception abilities like an image from a camera to identify grasps for a given object usually unknown. A grasp describes how a robotic end-effector need to be positioned to securely grab an object and successfully lift it without lost it, at the moment state of the arts solutions are still far behind humans. In the last 5–10 years, deep learning methods take the scene to overcome classical problem like the arduous and time-consuming approach to form a task-specific algorithm analytically. In this thesis are present the progress and the approaches in the robotic grasping field and the potential of the deep learning methods in robotic grasping. Based on that, an implementation of a Convolutional Neural Network (CNN) as a starting point for generation of a grasp pose from camera view has been implemented inside a ROS environment. The developed technologies have been integrated into a pick-and-place application for a Panda robot from Franka Emika. The application includes various features related to object detection and selection. Additionally, the features have been kept as generic as possible to allow for easy replacement or removal if needed, without losing time for improvement or new testing.

Automated Software Testing in Machine Automation

The problem of software (SW) defaults is becoming more and more topical because of increasing amount of the SW and its complication. The majority of these defaults are founded during the test part that consumes about 40-50% of the development efforts. Test automation allows reducing the cost of this process and increasing testing effectiveness. In the middle of 1980 the first tools for automated testing appeared and the automated process was implemented in different kinds of SW testing. In short time, it became obviously, automated testing can cause many problems such as increasing product cost, decreasing reliability and even project fail. This thesis describes automated testing process, its concept, lists main problems, and gives an algorithm for automated test tools selection. Also this work presents an overview of the main automated test tools for embedded systems.

Brand Identity and Image in B2B Factory Automation Context

The goal of this thesis is to study how a solution-oriented business-to-business company can utilize its brand as a strategic asset by using the concepts of brand identity and brand image. The study analyses the intended brand message (identity) contrasting it with the customer perceptions (image) to reveal points of parity and congruence. The study uses a case company as an example and discusses the benefits of brand management as well. Internally, brands can be studied by performing a set of interviews amongst top and middle management. The interviews need to consider the various elements of branding from associations to differentiation and value creation. Customers’ perceptions can be reliably studied via online survey designed to compare the intended brand message with customers’ experiences. From the perspective of industrial management the incentive for brand development lies in both monetary and managerial benefits. In literature the four essential benefits of B2B branding are risk dilution, efficiency of communications, strategic direction and price premiums. As a result, suggestive models for brand identity and image were devised and compared. The Case Company perceives itself as a technically oriented open-integrator, with a strong focus on reliability and customer service. Customers agree with the picture in general, but there are some points of parity as well: they are quite satisfied with the company and perceive it as reliable and providing the promised value. The problematic areas revolve around customer interaction and maintaining the leadership position. The results confirm previous findings in B2B branding theory, where the reliability and credibility of the supplier are in major role. The results also suggest a holistic, corporate approach on branding.