Microfabricated elastomer tactile sensors for robotic fingertip systems

Tactile sensing is an important aspect of robotic systems, and enables safe, dexterous robot-environment interaction. The design and implementation of tactile sensors on robots has been a topic of research over the past 30 years, and current challenges include mechanically flexible “sensing skins”, high dynamic range (DR) sensing (i.e.: high force range and fine force resolution), multi-axis sensing, and integration between the sensors and robot. This dissertation focuses on addressing some of these challenges through a novel manufacturing process that incorporates conductive and dielectric elastomers in a reusable, multilength-scale mold, and new sensor designs for multi-axis sensing that improve force range without sacrificing resolution. A single taxel was integrated into a 1 degree of freedom robotic gripper for closed-loop slip detection. Manufacturing involved casting a composite silicone rubber, polydimethylsiloxane (PDMS) filled with conductive particles such as carbon nanotubes, into a mold to produce microscale flexible features on the order of 10s of microns. Molds were produced via microfabrication of silicon wafers, but were limited in sensing area and were costly. An improved technique was developed that produced molds of acrylic using a computer numerical controlled (CNC) milling machine. This maintained the ability to produce microscale features, and increased the sensing area while reducing costs. New sensing skins had features as small as 20 microns over an area as large as a human hand. Sensor architectures capable of sensing both shear and normal force sensing with high dynamic range were produced. Using this architecture, two sensing modalities were developed: a capacitive approach and a contact resistive approach. The capacitive approach demonstrated better dynamic range, while the contact resistive approach used simpler circuitry. Using the contact resistive approach, normal force range and resolution were 8,000 mN and 1,000 mN, respectively, and shear force range and resolution were 450 mN and 100 mN, respectively. Using the capacitive approach, normal force range and resolution were 10,000 mN and 100 mN, respectively, and shear force range and resolution were 1,500 mN and 50 mN, respectively.

Mission and Scenario Planning for Unmanned Aerial Vehicles (Path Planning and Collision Avoidance Systems)

As unmanned autonomous vehicles (UAVs) are being widely utilized in military and civil applications, concerns are growing about mission safety and how to integrate dierent phases of mission design. One important barrier to a coste ective and timely safety certication process for UAVs is the lack of a systematic approach for bridging the gap between understanding high-level commander/pilot intent and implementation of intent through low-level UAV behaviors. In this thesis we demonstrate an entire systems design process for a representative UAV mission, beginning from an operational concept and requirements and ending with a simulation framework for segments of the mission design, such as path planning and decision making in collision avoidance. In this thesis, we divided this complex system into sub-systems; path planning, collision detection and collision avoidance. We then developed software modules for each sub-system

Investigating automated chemical evolution of oil-in-water droplets

One of the main unresolved questions in science is how non-living matter became alive in a process known as abiognesis, which aims to explain how from a primordial soup scenario containing simple molecules, by following a ``bottom up'' approach, complex biomolecules emerged forming the first living system, known as a protocell. A protocell is defined by the interplay of three sub-systems which are considered requirements for life: information molecules, metabolism, and compartmentalization. This thesis investigates the role of compartmentalization during the emergence of life, and how simple membrane aggregates could evolve into entities that were able to develop ``life-like'' behaviours, and in particular how such evolution could happen without the presence of information molecules. Our ultimate objective is to create an autonomous evolvable system, and in order tp do so we will try to engineer life following a ``top-down'' approach, where an initial platform capable of evolving chemistry will be constructed, but the chemistry being dependent on the robotic adjunct, and how then this platform can be de-constructed in iterative operations until it is fully disconnected from the evolvable system, the system then being inherently autonomous. The first project of this thesis describes how the initial platform was designed and built. The platform was based on the model of a standard liquid handling robot, with the main difference with respect to other similar robots being that we used a 3D-printer in order to prototype the robot and build its main equipment, like a liquid dispensing system, tool movement mechanism, and washing procedures. The robot was able to mix different components and create populations of droplets in a Petri dish filled with aqueous phase. The Petri dish was then observed by a camera, which analysed the behaviours described by the droplets and fed this information back to the robot. Using this loop, the robot was then able to implement an evolutionary algorithm, where populations of droplets were evolved towards defined life-like behaviours. The second project of this thesis aimed to remove as many mechanical parts as possible from the robot while keeping the evolvable chemistry intact. In order to do so, we encapsulated the functionalities of the previous liquid handling robot into a single monolithic 3D-printed device. This device was able to mix different components, generate populations of droplets in an aqueous phase, and was also equipped with a camera in order to analyse the experiments. Moreover, because the full fabrication process of the devices happened in a 3D-printer, we were also able to alter its experimental arena by adding different obstacles where to evolve the droplets, enabling us to study how environmental changes can shape evolution. By doing so, we were able to embody evolutionary characteristics into our device, removing constraints from the physical platform, and taking one step forward to a possible autonomous evolvable system.

Design and characterization of automated agricultural vehicle systems

Nowadays, technological advancements have brought industry and research towards the automation of various processes. Automation brings a reduction in costs and an improvement in product quality. For this reason, companies are pushing research to investigate new technologies. The agriculture industry has always looked towards automating various processes, from product processing to storage. In the last years, the automation of harvest and cultivation phases also has become attractive, pushed by the advancement of autonomous driving. Nevertheless, ADAS systems are not enough. Merging different technologies will be the solution to obtain total automation of agriculture processes. For example, sensors that estimate products' physical and chemical properties can be used to evaluate the maturation level of fruit. Therefore, the fusion of these technologies has a key role in industrial process automation. In this dissertation, ADAS systems and sensors for precision agriculture will be both treated. Several measurement procedures for characterizing commercial 3D LiDARs will be proposed and tested to cope with the growing need for comparison tools. Axial errors and transversal errors have been investigated. Moreover, a measurement method and setup for evaluating the fog effect on 3D LiDARs will be proposed. Each presented measurement procedure has been tested. The obtained results highlight the versatility and the goodness of the proposed approaches. Regarding the precision agriculture sensors, a measurement approach for the Moisture Content and density estimation of crop directly on the field is presented. The approach regards the employment of a Near Infrared spectrometer jointly with Partial Least Square statistical analysis. The approach and the model will be described together with a first laboratory prototype used to evaluate the NIRS approach. Finally, a prototype for on the field analysis is realized and tested. The test results are promising, evidencing that the proposed approach is suitable for Moisture Content and density estimation.

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.

Integrated communication and learning towards autonomous mobile radio networks

The integration of distributed and ubiquitous intelligence has emerged over the last years as the mainspring of transformative advancements in mobile radio networks. As we approach the era of “mobile for intelligence”, next-generation wireless networks are poised to undergo significant and profound changes. Notably, the overarching challenge that lies ahead is the development and implementation of integrated communication and learning mechanisms that will enable the realization of autonomous mobile radio networks. The ultimate pursuit of eliminating human-in-the-loop constitutes an ambitious challenge, necessitating a meticulous delineation of the fundamental characteristics that artificial intelligence (AI) should possess to effectively achieve this objective. This challenge represents a paradigm shift in the design, deployment, and operation of wireless networks, where conventional, static configurations give way to dynamic, adaptive, and AI-native systems capable of self-optimization, self-sustainment, and learning. This thesis aims to provide a comprehensive exploration of the fundamental principles and practical approaches required to create autonomous mobile radio networks that seamlessly integrate communication and learning components. The first chapter of this thesis introduces the notion of Predictive Quality of Service (PQoS) and adaptive optimization and expands upon the challenge to achieve adaptable, reliable, and robust network performance in dynamic and ever-changing environments. The subsequent chapter delves into the revolutionary role of generative AI in shaping next-generation autonomous networks. This chapter emphasizes achieving trustworthy uncertainty-aware generation processes with the use of approximate Bayesian methods and aims to show how generative AI can improve generalization while reducing data communication costs. Finally, the thesis embarks on the topic of distributed learning over wireless networks. Distributed learning and its declinations, including multi-agent reinforcement learning systems and federated learning, have the potential to meet the scalability demands of modern data-driven applications, enabling efficient and collaborative model training across dynamic scenarios while ensuring data privacy and reducing communication overhead.

Molecular and hybrid systems for solar-to-chemical energy conversion

My Ph.D. thesis was dedicated to the exploration of different paths to convert sunlight into the shape of chemical bonds, by the formation of solar fuels. During the past three years, I have focused my research on two of these, namely molecular hydrogen H2 and the reduced nicotinamide adenine dinucleotide enzyme cofactor NAD(P)H. The first could become the ideal energy carrier for a truly clean energy system; it currently represents the best chance to liberate humanity from its dependence on fossil fuels. To address this, I studied different systems which can achieve proton reduction upon light absorption. More specifically, part of my work was aimed to the development of a cost-effective and stable catalyst in combination with a well-known photochemical cycle. To this extent, I worked on transition metal oxides which, as demonstrated in this work, have been identified as promising H2 evolution catalysts, showing excellent activity, stability, and previously unreported versatility. Another branch of my work on hydrogen production dealt with the use of a new class of polymeric semiconductor materials to absorb light and convert it into H2. The second solar fuel mentioned above is a key component of the most powerful methods for chemical synthesis: enzyme catalysis. The high cost of the reduced forms prohibits large-scale utilization, so artificial photosynthetic approaches for regenerating it are being intensively studied. The first system I developed exploits the tremendous reducing properties of a scarcely known ruthenium complex which is able to reduce NAD+. Lastly, I sought to revert the classical role of the sacrificial electron donor to an active component of the system and, to boost the process, I build up an autonomous microfluidic system able to generate highly reproducible NAD(P)H amount, demonstrating the superior performance of microfluidic reactors over batch and representing another successful photochemical NAD(P)H regeneration system.

Addressing challenges in heterogeneous embedded systems programming: security and performance

Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

Factors affecting the mission time of an autonomous mobile robot: a theoretical and experimental investigation

I vantaggi dell’Industria 4.0 hanno stravolto il manufacturing. Ma cosa vuol dire "Industria 4.0"? Essa è la nuova frontiera del manufacturing, basata su princìpi che seguono i passi avanti dei sistemi IT e della tecnologia. Dunque, i suoi pilastri sono: integrazione, verticale e orizzontale, digitalizzazione e automazione. L’Industria 4.0 coinvolge molte aree della supply chain, dai flussi informativi alla logistica. In essa e nell’intralogistica, la priorità è sviluppare dei sistemi di material handling flessibili, automatizzati e con alta prontezza di risposta. Il modello ideale è autonomo, in cui i veicoli fanno parte di una flotta le cui decisioni sono rese decentralizzate grazie all'alta connettività e alla loro abilità di collezionare dati e scambiarli rapidamente nel cloud aziendale.Tutto ciò non sarebbe raggiungibile se ci si affidasse a un comune sistema di trasporto AGV, troppo rigido e centralizzato. La tesi si focalizza su un tipo di material handlers più flessibile e intelligente: gli Autonomous Mobile Robots. Grazie alla loro intelligenza artificiale e alla digitalizzazione degli scambi di informazioni, interagiscono con l’ambiente per evitare ostacoli e calcolare il percorso ottimale. Gli scenari dell’ambiente lavorativo determinano perdite di tempo nel tragitto dei robot e sono queste che dovremo studiare. Nella tesi, i vantaggi apportati dagli AMR, come la loro decentralizzazione delle decisioni, saranno introdotti mediante una literature review e poi l’attenzione verterà sull’analisi di ogni scenario di lavoro. Fondamentali sono state le esperienze nel Logistics 4.0 Lab di NTNU, per ricreare fisicamente alcuni scenari. Inoltre, il software AnyLogic sarà usato per riprodurre e simulare tutti gli scenari rilevanti. I risultati delle simulazioni verranno infine usati per creare un modello che associ ad ogni scenario rilevante una perdita di tempo, attraverso una funzione. Per questo saranno usati software di data analysis come Minitab e MatLab.

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.

Feedback control of underactuated cable-driven parallel robots

In this work an Underactuated Cable-Driven Parallel Robot (UACDPR) that operates in the three dimensional Euclidean space is considered. The End-Effector has 6 degrees of freedom and is actuated by 4 cables, therefore from a mechanical point of view the robot is defined underconstrained. However, considering only three controlled pose variables, the degree of redundancy for the control theory can be considered one. The aim of this thesis is to design a feedback controller for a point-to-point motion that satisfies the transient requirements, and is capable of reducing oscillations that derive from the reduced number of constraints. A force control is chosen for the positioning of the End-Effector, and error with respect to the reference is computed through data measure of several sensors (load cells, encoders and inclinometers) such as cable lengths, tension and orientation of the platform. In order to express the relation between pose and cable tension, the inverse model is derived from the kinematic and dynamic model of the parallel robot. The intrinsic non-linear nature of UACDPRs systems introduces an additional level of complexity in the development of the controller, as a result the control law is composed by a partial feedback linearization, and damping injection to reduce orientation instability. The fourth cable allows to satisfy a further tension distribution constraint, ensuring positive tension during all the instants of motion. Then simulations with different initial conditions are presented in order to optimize control parameters, and lastly an experimental validation of the model is carried out, the results are analysed and limits of the presented approach are defined.

Smart freight transport in urban areas: a crowd-shipping model using autonomous vehicles

L'avanzamento dell'e-commerce e l'aumento della densità abitativa nel centro città sono elementi che incentivano l'incremento della richiesta merci all'interno dei centri urbani. L'attenzione all'impatto ambientale derivante da queste attività operative è un punto focale oggetto di sempre maggiore interesse. Attraverso il seguente studio, l'obiettivo è definire attuali e potenziali soluzioni nell'ambito della logistica urbana, con particolare interesse alle consegne dell'ultimo miglio. Una soluzione proposta riguarda la possibilità di sfruttare la capacità disponibile nei flussi generati dalla folla per movimentare merce, pratica nota sotto il nome di Crowd-shipping. L'idea consiste nella saturazione di mezzi già presenti nella rete urbana al fine di ridurre il numero di veicoli commerciali e minimizzare le esternalità negative annesse. A supporto di questa iniziativa, nell'analisi verranno considerati veicoli autonomi elettrici a guida autonoma. La tesi è incentrata sulla definizione di un modello di ottimizzazione matematica, che mira a designare un network logistico-distributivo efficiente per le consegne dell'ultimo miglio e a minimizzare le distanze degli attori coinvolti. Il problema proposto rappresenta una variante del Vehicle Routing Problem con time windows e multi depots. Il problema è NP-hard, quindi computazionalmente complesso per cui sarà necessario, in fase di analisi, definire un approccio euristico che permetterà di ottenere una soluzione sub-ottima in un tempo di calcolo ragionevole per istanze maggiori. L'analisi è stata sviluppata nell'ambiente di sviluppo Eclipse, attraverso il risolutore Cplex, in linguaggio Java. Per poterne comprendere la validità, è prevista un'ultima fase in cui gli output del modello ottimo e dell'euristica vengono confrontati tra loro su parametri caratteristici. Bisogna tuttavia considerare che l' utilizzo di sistemi cyber-fisici a supporto della logistica non può prescindere da un costante sguardo verso il progresso.

Distributed collision-free traffic management via vertex cover for multi-vehicle systems

In this thesis, we state the collision avoidance problem as a vertex covering problem, then we consider a distributed framework in which a team of cooperating Unmanned Vehicles (UVs) aim to solve this optimization problem cooperatively to guarantee collision avoidance between group members. For this purpose, we implement a distributed control scheme based on a robust Set-Theoretic Model Predictive Control ( ST-MPC) strategy, where the problem involves vehicles with independent dynamics but with coupled constraints, to capture required cooperative behavior.

A Microleakage Study Of Gutta-percha/ah Plus And Resilon/real Self-etch Systems After Different Irrigation Protocols.

The development and maintenance of the sealing of the root canal system is the key to the success of root canal treatment. The resin-based adhesive material has the potential to reduce the microleakage of the root canal because of its adhesive properties and penetration into dentinal walls. Moreover, the irrigation protocols may have an influence on the adhesiveness of resin-based sealers to root dentin. The objective of the present study was to evaluate the effect of different irrigant protocols on coronal bacterial microleakage of gutta-percha/AH Plus and Resilon/Real Seal Self-etch systems. One hundred ninety pre-molars were used. The teeth were divided into 18 experimental groups according to the irrigation protocols and filling materials used. The protocols used were: distilled water; sodium hypochlorite (NaOCl)+eDTA; NaOCl+H3PO4; NaOCl+eDTA+chlorhexidine (CHX); NaOCl+H3PO4+CHX; CHX+eDTA; CHX+ H3PO4; CHX+eDTA+CHX and CHX+H3PO4+CHX. Gutta-percha/AH Plus or Resilon/Real Seal Se were used as root-filling materials. The coronal microleakage was evaluated for 90 days against Enterococcus faecalis. Data were statistically analyzed using Kaplan-Meier survival test, Kruskal-Wallis and Mann-Whitney tests. No significant difference was verified in the groups using chlorhexidine or sodium hypochlorite during the chemo-mechanical preparation followed by eDTA or phosphoric acid for smear layer removal. The same results were found for filling materials. However, the statistical analyses revealed that a final flush with 2% chlorhexidine reduced significantly the coronal microleakage. A final flush with 2% chlorhexidine after smear layer removal reduces coronal microleakage of teeth filled with gutta-percha/AH Plus or Resilon/Real Seal SE.

Does The Reciproc File Remove Root Canal Bacteria And Endotoxins As Effectively As Multifile Rotary Systems?

To evaluate the effectiveness of Reciproc for the removal of cultivable bacteria and endotoxins from root canals in comparison with multifile rotary systems. The root canals of forty human single-rooted mandibular pre-molars were contaminated with an Escherichia coli suspension for 21 days and randomly assigned to four groups according to the instrumentation system: GI - Reciproc (VDW); GII - Mtwo (VDW); GIII - ProTaper Universal (Dentsply Maillefer); and GIV -FKG Race(™) (FKG Dentaire) (n = 10 per group). Bacterial and endotoxin samples were taken with a sterile/apyrogenic paper point before (s1) and after instrumentation (s2). Culture techniques determined the colony-forming units (CFU) and the Limulus Amebocyte Lysate assay was used for endotoxin quantification. Results were submitted to paired t-test and anova. At s1, bacteria and endotoxins were recovered in 100% of the root canals investigated (40/40). After instrumentation, all systems were associated with a highly significant reduction of the bacterial load and endotoxin levels, respectively: GI - Reciproc (99.34% and 91.69%); GII - Mtwo (99.86% and 83.11%); GIII - ProTaper (99.93% and 78.56%) and GIV - FKG Race(™) (99.99% and 82.52%) (P < 0.001). No statistical difference were found amongst the instrumentation systems regarding bacteria and endotoxin removal (P > 0.01). The reciprocating single file, Reciproc, was as effective as the multifile rotary systems for the removal of bacteria and endotoxins from root canals.