Sviluppo di software per centralina di contollo motore tramite Simulink e Real-Time Workshop Embedded Coder

Quando si lavora con sistemi embedded real-time le tematiche da affrontare e sviscerare per ottenere dei buoni risultati sono molteplici. Il lavoro di tesi parte analizzando la fase di produzione del codice da parte del Real-Time Workshop per finire con la realizzazione di un target custom ottenuto modificando il target per Infineon C166 esistente. Nella tesi viene mostrata l’architettura software dell’ambiente di cross-sviluppo Keil e l’architettura hardware della famiglia di microcontrollori Infineon C166. Vengono poi sviluppati diversi blocchetti Simulink per ottenere il controllo delle periferiche integrate nel microcontrollore al fine di utilizzarle per realizzare il software di centralina. La tesi mostra quindi la possibilit&agrave di generare il codice di controllo della centralina interamente attraverso Simulink senza dover scrivere nessuno spezzone di codice a mano. Questo permette di simulare e modificare a piacimento il modello senza dover ricorrere ad una programmazione a basso livello per ottenere l’applicazione richiesta

A 6-DOF haptic manipulation system to verify assembly procedures on CAD models

During the design phase of products and before going into production, it is necessary to verify the presence of mechanical plays, tolerances, and encumbrances on production mockups. This work introduces a multi-modal system that allows verifying assembly procedures of products in Virtual Reality starting directly from CAD models. Thus leveraging the costs and speeding up the assessment phase in product design. For this purpose, the design of a novel 6-DOF Haptic device is presented. The achieved performance of the system has been validated in a demonstration scenario employing state-of-the-art volumetric rendering of interaction forces together with a stereoscopic visualization setup

Welding Defect Detection with Deep Learning Architectures

Welding automation is a fundamental process in manufacturing industries. Production lines integrate welding quality controls to reduce wastes and optimize the production chain. Early detection is fundamental as defects at any stage could determine the rejection of the entire product. In the last years, following the industry 4.0 paradigm, industrial automation lines have seen the introduction of modern technologies. Although the majority of the inspection systems still rely on traditional sensing and data processing, especially in the computer vision domain, some initiatives have been taken toward the employment of machine learning architectures. This chapter introduces deep neural networks in the context of welding defect detection, starting by analyzing common problems in the industrial applications of such technologies and discussing possible solutions in the specific case of quality checks in fuel injectors welding during the production stage

confined spaces industrial inspection with micro aerial vehicles and laser range finder localization

This work addresses the problem of semi-automatic inspection and navigation in confined environments. A system that overcomes many challenges at the state of the art is presented. It comprises a mu..

Multimodal Grasp Planner for Hybrid Grippers in Cluttered Scenes

Grasping a variety of objects is still an open problem in robotics, especially for cluttered scenarios. Multimodal grasping has been recognized as a promising strategy to improve the manipulation capabilities of a robotic system. This work presents a novel grasp planning algorithm for hybrid grippers that allows for multiple grasping modalities. In particular, the planner manages two-finger grasps, single or double suction grasps, and magnetic grasps. Grasps for different modalities are geometrically computed based on the cuboid and the material properties of the objects in the clutter. The presented framework is modular and can leverage any 6D pose estimation or material segmentation network as far as they satisfy the required interface. Furthermore, the planner can be applied to any (hybrid) gripper, provided the gripper clearance, finger width, and suction diameter. The approach is fast and has a low computational burden, as it uses geometric computations for grasp synthesis and selection. The performance of the system has been assessed with an experimental campaign in three manipulation scenarios of increasing difficulty using the objects of the YCB dataset and the DLR hybrid-compliant gripper

Design and Development of a Hand Exoskeleton Robot for Active and Passive Rehabilitation

The present work, which describes the mechatronic design and development of a novel rehabilitation robotic exoskeleton hand, aims to present a solution for neuromusculoskeletal rehabilitation. It presents a full range of motion for all hand phalanges and was specifically designed to carry out position and force-position control for passive and active rehabilitation routines. System integration and preliminary clinical tests are also presented

The Cluttered Environment Picking Benchmark (CEPB) for Advanced Warehouse Automation

Autonomous and reliable robotic grasping is a desirable functionality in robotic manipulation and is still an open problem. Standardized benchmarks are important tools for evaluating and comparing robotic grasping and manipulation systems among different research groups and also for sharing with the community the best practices to learn from errors. An ideal benchmarking protocol should encompass the different aspects underpinning grasp execution, including the mechatronic design of grippers, planning, perception, and control to give information on each aspect and the overall problem. This article gives an overview of the benchmarks, datasets, and competitions that have been proposed and adopted in the last few years and presents a novel benchmark with protocols for different tasks that evaluate both the single components of the system and the system as a whole, introducing an evaluation metric that allows for a fair comparison in highly cluttered scenes taking into account the difficulty of the clutter. A website dedicated to the benchmark containing information on the different tasks, maintaining the leaderboards, and serving as a contact point for the community is also provided

Is Deep Learning ready to satisfy Industry needs?

The impact that Artificial Intelligence is having in modern society is undeniable. Many companies are now using AI to improve the throughput and automate their processes. But the challenge is that Artificial Intelligence is both a source of enthusiasm and skepticism for industries. The manuscript points out the main causes of skepticism giving at the same time some possible technical solutions to exploit at the best the potentialities of AI even in those conditions in which the data are imbalanced and the object classes are not well separated. This work also emphasizes the delicate relationship between artificial intelligence, researchers, and industries, and tries to give an overview of a possible trade-off between the two parties. The document ends up proposing an 'interpretable learning' approach that can be exploited as a common language between the two players. The desirable practice would be to make AI explainable, provable, and easily understandable by the companies