Robotic Sensing and Manipulation of Deformable Linear Objects with Learning-based methods

Nowadays robotic applications are widespread and most of the manipulation tasks are efficiently solved. However, Deformable-Objects (DOs) still represent a huge limitation for robots. The main difficulty in DOs manipulation is dealing with the shape and dynamics uncertainties, which prevents the use of model-based approaches (since they are excessively computationally complex) and makes sensory data difficult to interpret. This thesis reports the research activities aimed to address some applications in robotic manipulation and sensing of Deformable-Linear-Objects (DLOs), with particular focus to electric wires. In all the works, a significant effort was made in the study of an effective strategy for analyzing sensory signals with various machine learning algorithms. In the former part of the document, the main focus concerns the wire terminals, i.e. detection, grasping, and insertion. First, a pipeline that integrates vision and tactile sensing is developed, then further improvements are proposed for each module. A novel procedure is proposed to gather and label massive amounts of training images for object detection with minimal human intervention. Together with this strategy, we extend a generic object detector based on Convolutional-Neural-Networks for orientation prediction. The insertion task is also extended by developing a closed-loop control capable to guide the insertion of a longer and curved segment of wire through a hole, where the contact forces are estimated by means of a Recurrent-Neural-Network. In the latter part of the thesis, the interest shifts to the DLO shape. Robotic reshaping of a DLO is addressed by means of a sequence of pick-and-place primitives, while a decision making process driven by visual data learns the optimal grasping locations exploiting Deep Q-learning and finds the best releasing point. The success of the solution leverages on a reliable interpretation of the DLO shape. For this reason, further developments are made on the visual segmentation.

Acqua: solvente elettivo in Organocatalisi e Biocatalisi

Water is a safe, harmless, and environmentally benign solvent. From an eco-sustainable chemistry perspective, the use of water instead of organic solvent is preferred to decrease environmental contamination. Moreover, water has unique physical and chemical properties, such as high dielectric constant and high cohesive energy density compared to most organic solvents. The different interactions between water and substrates, make water an interesting candidate as a solvent or co-solvent from an industrial and laboratory perspective. In this regard, organic reactions in aqueous media are of current interest. In addition, from practical and synthetic standpoints, a great advantage of using water is immediately evident, since it does not require any preliminary drying process. This thesis was found on this aspect of chemical research, with particular attention to the mechanisms which control organo and bio-catalysis outcome. The first part of the study was focused on the aldol reaction. In particular, for the first time it has been analyzed for the first time the stereoselectivity of the condensation reaction between 3-pyridincarbaldehyde and the cyclohexanone, catalyzed by morpholine and 4-tertbutyldimethylsiloxyproline, using water as sole solvent. This interest has resulted in countless works appeared in the literature concerning the use of proline derivatives as effective catalysts in organic aqueous environment. These studies showed good enantio and diastereoselectivities but they did not present an in depth study of the reaction mechanism. The analysis of the products diastereomeric ratios through the Eyring equation allowed to compare the activation parameters (ΔΔH≠ and ΔΔS≠) of the diastereomeric reaction paths, and to compare the different type of catalysis. While morpholine showed constant diasteromeric ratio at all temperatures, the O(TBS)-L-proline, showed a non-linear Eyring diagram, with two linear trends and the presence of an inversion temperature (Tinv) at 53 ° C, which denotes the presence of solvation effects by water. A pH-dependent study allowed to identify two different reaction mechanisms, and in the case of O(TBS)-L-proline, to ensure the formation of an enaminic species, as a keyelement in the stereoselective process. Moreover, it has been studied the possibility of using the 6- aminopenicillanic acid (6-APA) as amino acid-type catalyst for aldol condensation between cyclohexanone and aromatic aldehydes. A detailed analysis of the catalyst regarding its behavior in different organic solvents and pH, allowed to prove its potential as a candidate for green catalysis. Best results were obtained in neat conditions, where 6-APA proved to be an effective catalyst in terms of yields. The catalyst performance in terms of enantio- and diastereo-selectivity, was impaired by the competition between two different catalytic mechanisms: one via imine-enamine mechanism and one via a Bronsted-acid catalysis. The last part of the thesis was dedicated to the enzymatic catalysis, with particular attention to the use of an enzyme belonging to the class of alcohol dehydrogenase, the Horse Liver Alcohol Dehydrogenase (HLADH) which was selected and used in the enantioselective reduction of aldehydes to enantiopure arylpropylic alcohols. This enzyme has showed an excellent responsiveness to this type of aldehydes and a good tolerance toward organic solvents. Moreover, the fast keto-enolic equilibrium of this class of aldehydes that induce the stereocentre racemization, allows the dynamic-kinetic resolution (DKR) to give the enantiopure alcohol. By analyzing the different reaction parameters, especially the pH and the amount of enzyme, and adding a small percentage of organic solvent, it was possible to control all the parameters involved in the reaction. The excellent enatioselectivity of HLADH along with the DKR of arylpropionic aldehydes, allowed to obtain the corresponding alcohols in quantitative yields and with an optical purity ranging from 64% to >99%.

Rainfall spatial predictions: a two-part model and its assessment

Spatial prediction of hourly rainfall via radar calibration is addressed. The change of support problem (COSP), arising when the spatial supports of different data sources do not coincide, is faced in a non-Gaussian setting; in fact, hourly rainfall in Emilia-Romagna region, in Italy, is characterized by abundance of zero values and right-skeweness of the distribution of positive amounts. Rain gauge direct measurements on sparsely distributed locations and hourly cumulated radar grids are provided by the ARPA-SIMC Emilia-Romagna. We propose a three-stage Bayesian hierarchical model for radar calibration, exploiting rain gauges as reference measure. Rain probability and amounts are modeled via linear relationships with radar in the log scale; spatial correlated Gaussian effects capture the residual information. We employ a probit link for rainfall probability and Gamma distribution for rainfall positive amounts; the two steps are joined via a two-part semicontinuous model. Three model specifications differently addressing COSP are presented; in particular, a stochastic weighting of all radar pixels, driven by a latent Gaussian process defined on the grid, is employed. Estimation is performed via MCMC procedures implemented in C, linked to R software. Communication and evaluation of probabilistic, point and interval predictions is investigated. A non-randomized PIT histogram is proposed for correctly assessing calibration and coverage of two-part semicontinuous models. Predictions obtained with the different model specifications are evaluated via graphical tools (Reliability Plot, Sharpness Histogram, PIT Histogram, Brier Score Plot and Quantile Decomposition Plot), proper scoring rules (Brier Score, Continuous Rank Probability Score) and consistent scoring functions (Root Mean Square Error and Mean Absolute Error addressing the predictive mean and median, respectively). Calibration is reached and the inclusion of neighbouring information slightly improves predictions. All specifications outperform a benchmark model with incorrelated effects, confirming the relevance of spatial correlation for modeling rainfall probability and accumulation.

Diffusive models and chaos indicators for non-linear betatron motion in circular hadron accelerators

Understanding the complex dynamics of beam-halo formation and evolution in circular particle accelerators is crucial for the design of current and future rings, particularly those utilizing superconducting magnets such as the CERN Large Hadron Collider (LHC), its luminosity upgrade HL-LHC, and the proposed Future Circular Hadron Collider (FCC-hh). A recent diffusive framework, which describes the evolution of the beam distribution by means of a Fokker-Planck equation, with diffusion coefficient derived from the Nekhoroshev theorem, has been proposed to describe the long-term behaviour of beam dynamics and particle losses. In this thesis, we discuss the theoretical foundations of this framework, and propose the implementation of an original measurement protocol based on collimator scans in view of measuring the Nekhoroshev-like diffusive coefficient by means of beam loss data. The available LHC collimator scan data, unfortunately collected without the proposed measurement protocol, have been successfully analysed using the proposed framework. This approach is also applied to datasets from detailed measurements of the impact on the beam losses of so-called long-range beam-beam compensators also at the LHC. Furthermore, dynamic indicators have been studied as a tool for exploring the phase-space properties of realistic accelerator lattices in single-particle tracking simulations. By first examining the classification performance of known and new indicators in detecting the chaotic character of initial conditions for a modulated Hénon map and then applying this knowledge to study the properties of realistic accelerator lattices, we tried to identify a connection between the presence of chaotic regions in the phase space and Nekhoroshev-like diffusive behaviour, providing new tools to the accelerator physics community.