Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Synthesis, Characterization, and Applications of a Novel Cu(II)-MOF Based on a Propargylcarbamate-Functionalized Isophthalate Ligand

This work describes the synthesis of a propargylcarbamate-functionalized isophthalate ligand and its use in the solvothermal preparation of a new copper(II)-based metal organic framework named [Cu(1,3-YBDC)]ˑxH2O (also abbreviated as Cu-MOF. The characterization of this compound was performed using several complementary techniques such as infrared (ATR-FTIR) and Raman spectroscopy, X-ray powder diffraction spectroscopy (PXRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), atomic absorption spectroscopy (AAS) as well as thermal and surface area measurements. Synchrotron X-ray diffraction analysis revealed that this MOF contains a complex network of 5-substituted isophthalate anions bound to Cu(II) centers, arranged in pairs within paddlewheel (or “Chinese lantern”) structure with a short Cu…Cu distance of 2.633 Å. Quite unexpectedly, the apical atom in the paddlewheel structure belongs to the carbamate carbonyl oxygen atom. Such extra coordination by the propargylcarbamate groups drastically reduces the MOF porosity, a feature that was also confirmed by BET measurements. Indeed, its surface area was determined to be low (14.5 ± 0.8 m2/g) as its total pore volume (46 mm3/g). Successively the Cu-MOF was treated with HAuCl4 with the aim of studying the ability of the propargylcarbamate functionality to capture the Au(III) ion and reduce it to Au(0) to give gold nanoparticles (AuNPs). The overall amount of gold retained by the Cu-MOF/Au was determined by AAS while the amount of gold and its oxidation state on the surface of the MOF was studied by XPS. A glassy carbon (GC) electrode was drop-casted with a Cu-MOF suspension to electrochemically characterize the material through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The performance of the modified electrodes towards nitrite oxidation was tested by CV and chronoamperometry.

Models for the assessment of resilience in water distribution networks

The following thesis work focuses on the use and implementation of advanced models for measuring the resilience of water distribution networks. In particular, the functions implemented in GRA Tool, a software developed by the University of Exeter (UK), and the functions of the Toolkit of Epanet 2.2 were investigated. The study of the resilience and failure, obtained through GRA Tool and the development of the methodology based on the combined use of EPANET 2.2 and MATLAB software, was tested in a first phase, on a small-sized literature water distribution network, so that the variability of the results could be perceived more clearly and with greater immediacy, and then, on a more complex network, that of Modena. In the specific, it has been decided to go to recreate a mode of failure deferred in time, one proposed by the software GRA Tool, that is failure to the pipes, to make a comparison between the two methodologies. The analysis of hydraulic efficiency was conducted using a synthetic and global network performance index, i.e., Resilience index, introduced by Todini in the years 2000-2016. In fact, this index, being one of the parameters with which to evaluate the overall state of "hydraulic well-being" of a network, has the advantage of being able to act as a criterion for selecting any improvements to be made on the network itself. Furthermore, during these analyzes, was shown the analytical development undergone over time by the formula of the Resilience Index. The final intent of this thesis work was to understand by what means to improve the resilience of the system in question, as the introduction of the scenario linked to the rupture of the pipelines was designed to be able to identify the most problematic branches, i.e., those that in the event of a failure it would entail greater damage to the network, including lowering the Resilience Index.

Optimal pose selection for the calibration of an overconstrained Cable-Driven Parallel Robot

In this project an optimal pose selection method for the calibration of an overconstrained Cable-Driven Parallel robot is presented. This manipulator belongs to a subcategory of parallel robots, where the classic rigid "legs" are replaced by cables. Cables are flexible elements that bring advantages and disadvantages to the robot modeling. For this reason, there are many open research issues, and the calibration of geometric parameters is one of them. The identification of the geometry of a robot, in particular, is usually called Kinematic Calibration. Many methods have been proposed in the past years for the solution of the latter problem. Although these methods are based on calibration using different kinematic models, when the robot’s geometry becomes more complex, their robustness and reliability decrease. This fact makes the selection of the calibration poses more complicated. The position and the orientation of the endeffector in the workspace become important in terms of selection. Thus, in general, it is necessary to evaluate the robustness of the chosen calibration method, by means, for example, of a parameter such as the observability index. In fact, it is known from the theory, that the maximization of the above mentioned index identifies the best choice of calibration poses, and consequently, using this pose set may improve the calibration process. The objective of this thesis is to analyze optimization algorithms which aim to calculate an optimal choice of poses both in quantitative and qualitative terms. Quantitatively, because it is of fundamental importance to understand how many poses are needed. Not necessarily a greater number of poses leads to a better result. Qualitatively, because it is useful to understand if the selected combination of poses actually gives additional information in the process of the identification of the parameters.