A ROS-based software architecture for a versatile collaborative dual-armed autonomous mobile robot for the manufacturing industry

The industrial context is changing rapidly due to advancements in technology fueled by the Internet and Information Technology. The fourth industrial revolution counts integration, flexibility, and optimization as its fundamental pillars, and, in this context, Human-Robot Collaboration has become a crucial factor for manufacturing sustainability in Europe. Collaborative robots are appealing to many companies due to their low installation and running costs and high degree of flexibility, making them ideal for reshoring production facilities with a short return on investment. The ROSSINI European project aims to implement a true Human-Robot Collaboration by designing, developing, and demonstrating a modular and scalable platform for integrating human-centred robotic technologies in industrial production environments. The project focuses on safety concerns related to introducing a cobot in a shared working area and aims to lay the groundwork for a new working paradigm at the industrial level. The need for a software architecture suitable to the robotic platform employed in one of three use cases selected to deploy and test the new technology was the main trigger of this Thesis. The chosen application consists of the automatic loading and unloading of raw-material reels to an automatic packaging machine through an Autonomous Mobile Robot composed of an Autonomous Guided Vehicle, two collaborative manipulators, and an eye-on-hand vision system for performing tasks in a partially unstructured environment. The results obtained during the ROSSINI use case development were later used in the SENECA project, which addresses the need for robot-driven automatic cleaning of pharmaceutical bins in a very specific industrial context. The inherent versatility of mobile collaborative robots is evident from their deployment in the two projects with few hardware and software adjustments. The positive impact of Human-Robot Collaboration on diverse production lines is a motivation for future investments in research on this increasingly popular field by the industry.

Packet Level Coding for Mobile Broadcasting

The thesis deals with channel coding theory applied to upper layers in the protocol stack of a communication link and it is the outcome of four year research activity. A specific aspect of this activity has been the continuous interaction between the natural curiosity related to the academic blue-sky research and the system oriented design deriving from the collaboration with European industry in the framework of European funded research projects. In this dissertation, the classical channel coding techniques, that are traditionally applied at physical layer, find their application at upper layers where the encoding units (symbols) are packets of bits and not just single bits, thus explaining why such upper layer coding techniques are usually referred to as packet layer coding. The rationale behind the adoption of packet layer techniques is in that physical layer channel coding is a suitable countermeasure to cope with small-scale fading, while it is less efficient against large-scale fading. This is mainly due to the limitation of the time diversity inherent in the necessity of adopting a physical layer interleaver of a reasonable size so as to avoid increasing the modem complexity and the latency of all services. Packet layer techniques, thanks to the longer codeword duration (each codeword is composed of several packets of bits), have an intrinsic longer protection against long fading events. Furthermore, being they are implemented at upper layer, Packet layer techniques have the indisputable advantages of simpler implementations (very close to software implementation) and of a selective applicability to different services, thus enabling a better matching with the service requirements (e.g. latency constraints). Packet coding technique improvement has been largely recognized in the recent communication standards as a viable and efficient coding solution: Digital Video Broadcasting standards, like DVB-H, DVB-SH, and DVB-RCS mobile, and 3GPP standards (MBMS) employ packet coding techniques working at layers higher than the physical one. In this framework, the aim of the research work has been the study of the state-of-the-art coding techniques working at upper layer, the performance evaluation of these techniques in realistic propagation scenario, and the design of new coding schemes for upper layer applications. After a review of the most important packet layer codes, i.e. Reed Solomon, LDPC and Fountain codes, in the thesis focus our attention on the performance evaluation of ideal codes (i.e. Maximum Distance Separable codes) working at UL. In particular, we analyze the performance of UL-FEC techniques in Land Mobile Satellite channels. We derive an analytical framework which is a useful tool for system design allowing to foresee the performance of the upper layer decoder. We also analyze a system in which upper layer and physical layer codes work together, and we derive the optimal splitting of redundancy when a frequency non-selective slowly varying fading channel is taken into account. The whole analysis is supported and validated through computer simulation. In the last part of the dissertation, we propose LDPC Convolutional Codes (LDPCCC) as possible coding scheme for future UL-FEC application. Since one of the main drawbacks related to the adoption of packet layer codes is the large decoding latency, we introduce a latency-constrained decoder for LDPCCC (called windowed erasure decoder). We analyze the performance of the state-of-the-art LDPCCC when our decoder is adopted. Finally, we propose a design rule which allows to trade-off performance and latency.

The Role of B5G UAV-Aided Mobile Networks

With the advent of 5G, several novel network paradigms and technologies have been proposed to fulfil the key requirements imposed. Flexibility, efficiency and scalability, along with sustainability and convenience for expenditure have to be addressed in targeting these brand new needs. Among novel paradigms introduced in the scientific literature in recent years, a constant and increasing interest lies in the use of unmanned aerial vehicles (UAVs) as network nodes supporting the legacy terrestrial network for service provision. Their inherent features of moving nodes make them able to be deployed on-demand in real-time. Which, in practical terms, means having them acting as a base station (BS) when and where there is the highest need. This thesis investigates then the potential role of UAV-aided mobile radio networks, in order to validate the concept of adding an aerial network component and assess the system performance, from early to later stages of its deployment. This study is intended for 5G and beyond systems, to allow time for the technology to mature. Since advantages can be manyfold, the aerial network component is considered at the network layer under several aspects, from connectivity to radio resource management. A particular emphasis is given to trajectory design, because of the efficiency and flexibility it potentially adds to the infrastructure. Two different frameworks have been proposed, to take into account both a re-adaptable heuristic and an optimal solution. Moreover, diverse use cases are taken under analysis, from mobile broadband to machine and vehicular communications. The thesis aim is thus to discuss the potential and advantages of UAV-aided systems from a broad perspective. Results demonstrate that the technology has good prospects for diverse scenarios with a few arrangements.

Challenges and Opportunities of Machine Learning for Clinical and Omics Data

Clinical and omics data are a promising field of application for machine learning techniques even though these methods are not yet systematically adopted in healthcare institutions. Despite artificial intelligence has proved successful in terms of prediction of pathologies or identification of their causes, the systematic adoption of these techniques still presents challenging issues due to the peculiarities of the analysed data. The aim of this thesis is to apply machine learning algorithms to both clinical and omics data sets in order to predict a patient's state of health and get better insights on the possible causes of the analysed diseases. In doing so, many of the arising issues when working with medical data will be discussed while possible solutions will be proposed to make machine learning provide feasible results and possibly become an effective and reliable support tool for healthcare systems.

Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services.

Applying machine learning: a multi-role perspective

Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference.