Individuazione di punti salienti in dati 3D mediante rappresentazioni strutturate

Questa tesi si inserisce nel filone di ricerca dell'elaborazione di dati 3D, e in particolare nella 3D Object Recognition, e delinea in primo luogo una panoramica sulle principali rappresentazioni strutturate di dati 3D, le quali rappresentano una prerogativa necessaria per implementare in modo efficiente algoritmi di processing di dati 3D, per poi presentare un nuovo algoritmo di 3D Keypoint Detection che è stato sviluppato e proposto dal Computer Vision Laboratory dell'Università di Bologna presso il quale ho effettuato la mia attività di tesi.

Analysis of Local Search Methods for 3D Data

In questa tesi sono stati analizzati alcuni metodi di ricerca per dati 3D. Viene illustrata una panoramica generale sul campo della Computer Vision, sullo stato dell’arte dei sensori per l’acquisizione e su alcuni dei formati utilizzati per la descrizione di dati 3D. In seguito è stato fatto un approfondimento sulla 3D Object Recognition dove, oltre ad essere descritto l’intero processo di matching tra Local Features, è stata fatta una focalizzazione sulla fase di detection dei punti salienti. In particolare è stato analizzato un Learned Keypoint detector, basato su tecniche di apprendimento di machine learning. Quest ultimo viene illustrato con l’implementazione di due algoritmi di ricerca di vicini: uno esauriente (K-d tree) e uno approssimato (Radial Search). Sono state riportate infine alcune valutazioni sperimentali in termini di efficienza e velocità del detector implementato con diversi metodi di ricerca, mostrando l’effettivo miglioramento di performance senza una considerabile perdita di accuratezza con la ricerca approssimata.

Development of a robotic system for switchgear cabling

Industrial robots are an inalienable part of modern automated production. Typical applications of robots include welding, painting, (dis)assembly, packaging, labeling, palletizing, pick and place and others. Many of that applications includes object manipulation. If the shape and position of the object are known in advance, it is possible to design the trajectory of the robot’s end-effector to take and place. Such a strategy is applicable for rigid objects and widely used in the manufacturing field. But flexible (deformable) objects can change their shape and position upon contact with the robot’s end-effector or environment. That is the reason why the general approach is unacceptable. It means that the robot can fail to grasp such an object and can’t place it in the desired position. This thesis has addressed the problem of cable manipulation by bilateral robotic setup for the industrial manufacturing of electrical switchgear. The considered solution is based on the idea of tensioned cable. If the cable was grasped by the ends and tensioned, it has a line shape. Since the position of the robot’s end-effectors known, the position of the cable is known as well. Such an approach is capable to place cable in cable ducts of switchgear. The considered solution has been tested experimentally on a real bilateral robotic setup.

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.

OGRE: analisi di un motore grafico 3D

In questa tesi descriveremo e analizzeremo il motore grafico OGRE, acronimo di Object-Oriented Graphics Rendering Engine. La scelta di analizzare proprio questo motore grafico è legata a diverse considerazioni. Innanzitutto, OGRE è rilasciato con licenza open source e quindi rende disponibile il suo codice sorgente. Questo è molto importante, in un contesto di studio e sperimentazione come quello universitario, perché permette di comprendere e analizzare anche il funzionamento interno del motore grafico. Inoltre, OGRE è un progetto maturo e stabile con una vasta comunità di sviluppatori e utilizzatori alle spalle. Esiste molta documentazione a riguardo, tra wiki, libri e manuali, e un forum molto attivo per la richiesta di aiuto e consigli. A conferma, sia della bontà del progetto che delle ottime prestazioni del motore grafico, basta dire che OGRE è utilizzato anche da applicazioni commerciali, come videogame, editor 3D e simulatori. Infine, la caratteristica che contraddistingue OGRE da tutti gli altri motori grafici è il fatto di essere "solamente" un motore di rendering puro. Ciò significa che qualsiasi funzionalità non direttamente legata al rendering, come ad esempio la gestione degli input dell'utente, non è supportata da OGRE. Anche se questo può sembrare un difetto, in realtà ciò permetterà di concentrarci solamente sugli aspetti legati al rendering che, in un motore grafico, costituiscono la parte fondamentale.

Optimized methods to digital survey and 3D modelling of cultural heritage assets

Currently making digital 3D models and replicas of the cultural heritage assets play an important role in the preservation and having a high detail source for future research and intervention. In this dissertation, it is tried to assess different methods for digital surveying and making 3D replicas of cultural heritage assets in different scales of size. The methodologies vary in devices, software, workflow, and the amount of skill that is required. The three phases of the 3D modelling process are data acquisition, modelling, and model presentation. Each of these sections is divided into sub-sections and there are several approaches, methods, devices, and software that may be employed, furthermore, the selection process should be based on the operation's goal, available facilities, the scale and properties of the object or structure to be modeled, as well as the operators' expertise and experience. The most key point to remember is that the 3D modelling operation should be properly accurate, precise, and reliable; therefore, there are so many instructions and pieces of advice on how to perform 3D modelling effectively. It is an attempt to compare and evaluate the various ways of each phase in order to explain and demonstrate their differences, benefits, and drawbacks in order to serve as a simple guide for new and/or inexperienced users.

Robotic Manipulation of DLOs for Wiring Harness Assembly: a Machine Learning Approach

In recent times, a significant research effort has been focused on how deformable linear objects (DLOs) can be manipulated for real world applications such as assembly of wiring harnesses for the automotive and aerospace sector. This represents an open topic because of the difficulties in modelling accurately the behaviour of these objects and simulate a task involving their manipulation, considering a variety of different scenarios. These problems have led to the development of data-driven techniques in which machine learning techniques are exploited to obtain reliable solutions. However, this approach makes the solution difficult to be extended, since the learning must be replicated almost from scratch as the scenario changes. It follows that some model-based methodology must be introduced to generalize the results and reduce the training effort accordingly. The objective of this thesis is to develop a solution for the DLOs manipulation to assemble a wiring harness for the automotive sector based on adaptation of a base trajectory set by means of reinforcement learning methods. The idea is to create a trajectory planning software capable of solving the proposed task, reducing where possible the learning time, which is done in real time, but at the same time presenting suitable performance and reliability. The solution has been implemented on a collaborative 7-DOFs Panda robot at the Laboratory of Automation and Robotics of the University of Bologna. Experimental results are reported showing how the robot is capable of optimizing the manipulation of the DLOs gaining experience along the task repetition, but showing at the same time a high success rate from the very beginning of the learning phase.