Autonomous 3D geometry reconstruction through robot-manipulated optical sensors

Many industrial sectors face increasing production demands and the need to reduce costs, without compromising the quality. The use of robotics and automation has grown significantly in recent years, but versatile robotic manipulators are still not commonly used in small factories. Beside of the investments required to enable efficient and profitable use of robot technology, the efforts needed to program robots are only economically viable in case of large lot sizes. Generating robot programs for specific manufacturing tasks still relies on programming trajectory waypoints by hand. The use of virtual simulation software and the availability of the specimen digital models can facilitate robot programming. Nevertheless, in many cases, the virtual models are not available or there are excessive differences between virtual and real setups, leading to inaccurate robot programs and time-consuming manual corrections. Previous works have demonstrated the use of robot-manipulated optical sensors to map the geometry of samples. However, the use of simple user-defined robot paths, which are not optimized for a specific part geometry, typically causes some areas of the samples to not be mapped with the required level of accuracy or to not be sampled at all by the optical sensor. This work presents an autonomous framework to enable adaptive surface mapping, without any previous knowledge of the part geometry being transferred to the system. The novelty of this work lies in enabling the capability of mapping a part surface at the required level of sampling density, whilst minimizing the number of necessary view poses. Its development has also led to an efficient method of point cloud down-sampling and merging. The article gives an overview of the related work in the field, a detailed description of the proposed framework and a proof of its functionality through both simulated and experimental evidences

Evolution of domain-specific languages depending on external libraries

L'ingénierie dirigée par les modèles est une approche qui s'appuie sur l'abstraction pour exprimer davantage les concepts du domaine. Ainsi, les ingénieurs logiciels développent des langages dédiés (LD) qui encapsulent la structure, les contraintes et le comportement du domaine. Comme tout logiciel, les LDs évoluent régulièrement. Cette évolution peut se produire lorsque l'un de ses composants ou le domaine évolue. L'évolution du domaine ainsi que l'évolution des composants du LD et l'impact de cette évolution sur ceux-ci ont été largement étudiés. Cependant, un LD peut également dépendre sur d'éléments externes qui ne sont pas modélisées. Par conséquent, l'évolution de ces dépendances externes affecte le LD et ses composants. Actuellement, les ingénieurs logiciels doivent évoluer le LD manuellement lorsque les dépendances externes évoluent. Dans ce mémoire, nous nous concentrons sur l'évolution des librairies externes. Plus spécifiquement, le but de cette thèse est d'aider les ingénieurs logiciels dans la tâche d'évolution. À cette fin, nous proposons une approche qui intègre automatiquement les changements des librairies externes dans le LD. De plus, nous offrons un LD qui supporte l'évolution des librairies Arduino. Nous évaluons également notre approche en faisant évoluer un éditeur de modélisation interactif qui dépend d'un LD. Cette étude nous permet de montrer la faisabilité et l'utilité de notre approche.Model-driven engineering (MDE) is an approach that relies on abstraction to further express domain concepts. Hence, language engineers develop domain-specific languages (DSLs) that encapsulates the domain structure, constraints, and behavior. Like any software, DSLs evolve regularly. This evolution can occur when one of its components or the domain evolves. The domain evolution as well as the evolution of DSL components and the impact of such evolution on them has been widely investigated. However, a DSL may also rely on external dependencies that are not modeled. As a result, the evolution of these external dependencies affects the DSL and its components. This evolution problem has yet to be addressed. Currently, language engineers must manually evolve the DSL when the external dependencies evolve. In this thesis, we focus on the evolution of external libraries. More specifically, our goal is to assist language engineers in the task of evolution. To this end, we propose an approach that automatically integrates the changes of the external libraries into the DSL. In addition, we offer a DSL that supports the evolution of the Arduino libraries. We also evaluate our approach by evolving an interactive modeling editor that depends on a DSL. This study allows us to demonstrate the feasibility and usefulness of our approach

Feature Encoding Strategies for Multi-View Image Classification

Machine vision systems can vary greatly in size and complexity depending on the task at hand. However, the purpose of inspection, quality and reliability remains the same. This work sets out to bridge the gap between traditional machine vision and computer vision. By applying powerful computer vision techniques, we are able to achieve more robust solutions in manufacturing settings. This thesis presents a framework for applying powerful new image classification techniques used for image retrieval in the Bag of Words (BoW) framework. In addition, an exhaustive evaluation of commonly used feature pooling approaches is conducted with results showing that spatial augmentation can outperform mean and max descriptor pooling on an in-house dataset and the CalTech 3D dataset. The results for the experiments contained within, details a framework that performs classification using multiple view points. The results show that the feature encoding method known as Triangulation Embedding outperforms the Vector of Locally Aggregated Descriptors (VLAD) and the standard BoW framework with an accuracy of 99.28%. This improvement is also seen on the public Caltech 3D dataset where the improvement over VLAD and BoW was 5.64% and 12.23% respectively. This proposed multiple view classification system is also robust enough to handle the real world problem of camera failure and still classify with a high reliability. A missing camera input was simulated and showed that using the Triangulation Embedding method, the system could perform classification with a very minor reduction in accuracy at 98.89%, compared to the BoW baseline at 96.60% using the same techniques. The presented solution tackles the traditional machine vision problem of object identification and also allows for the training of a machine vision system that can be done without any expert level knowledge

A Cartographic Workflow Manual for Endangered Species Conservation

In response to global consumer demand for rare and exotic wildlife products, poaching of endangered species has become pervasive around the world (Eliason 1999). Despite the enactment of CITES, and other international efforts to protect vulnerable species from overexploitation, the global market for illegal wildlife products is estimated as high as $20-billion a year industry (Wyler 2008). Within important wildlife habitat sites, law enforcement struggle to curb rampant poaching that threatens the ultimate survival of many endangered species (Jachmann 2008; Rowcliffe 2004). Law-enforcement agencies responsible for protecting wildlife from poachers often lack geospatial tools that could greatly improve the effectiveness of their efforts. These tools include accurate topographic maps with the appropriate scale and the level of detail necessary for navigating in difficult and dangerous terrain, and GIS base data needed to monitor and evaluate the effectiveness of patrols (Pickles 2015). Recently, collaborations between the University of Montana (UM) and the large cat advocacy group Panthera, have enabled the production of geospatial packages for four protected areas of concern. These packages include printed topographic map series, GPS base-maps and comprehensive GIS base data. Throughout the creation of these packages, UM faculty and students have developed a nuanced workflow for this process using GIS and graphic design software. Until 2018, this workflow had yet to be fully documented. This document presents this workflow in the form of a cartographic manual, including step-by-step methods for creating appropriate geospatial packages. The goal of this document is to increase the efficiency of future cartographic collaborations between UM and conservation-minded groups, while providing valuable educational resources for UM students in GIS and cartography

Automatische Codegenerierung für Massiv Parallele Applikationen in der Numerischen Strömungsmechanik

Solving partial differential equations (PDEs) is a fundamental challenge in many application domains in industry and academia alike. With increasingly large problems, efficient and highly scalable implementations become more and more crucial. Today, facing this challenge is more difficult than ever due to the increasingly heterogeneous hardware landscape. One promising approach is developing domain‐specific languages (DSLs) for a set of applications. Using code generation techniques then allows targeting a range of hardware platforms while concurrently applying domain‐specific optimizations in an automated fashion. The present work aims to further the state of the art in this field. As domain, we choose PDE solvers and, in particular, those from the group of geometric multigrid methods. To avoid having a focus too broad, we restrict ourselves to methods working on structured and patch‐structured grids. We face the challenge of handling a domain as complex as ours, while providing different abstractions for diverse user groups, by splitting our external DSL ExaSlang into multiple layers, each specifying different aspects of the final application. Layer 1 is designed to resemble LaTeX and allows inputting continuous equations and functions. Their discretization is expressed on layer 2. It is complemented by algorithmic components which can be implemented in a Matlab‐like syntax on layer 3. All information provided to this point is summarized on layer 4, enriched with particulars about data structures and the employed parallelization. Additionally, we support automated progression between the different layers. All ExaSlang input is processed by our jointly developed Scala code generation framework to ultimately emit C++ code. We particularly focus on how to generate applications parallelized with, e.g., MPI and OpenMP that are able to run on workstations and large‐scale cluster alike. We showcase the applicability of our approach by implementing simple test problems, like Poisson’s equation, as well as relevant applications from the field of computational fluid dynamics (CFD). In particular, we implement scalable solvers for the Stokes, Navier‐Stokes and shallow water equations (SWE) discretized using finite differences (FD) and finite volumes (FV). For the case of Navier‐Stokes, we also extend our implementation towards non‐uniform grids, thereby enabling static mesh refinement, and advanced effects such as the simulated fluid being non‐Newtonian and non‐isothermal

Supporting Scientific Research Through Machine and Deep Learning: Fluorescence Microscopy and Operational Intelligence Use Cases

Although the debate of what data science is has a long history and has not reached a complete consensus yet, Data Science can be summarized as the process of learning from data. Guided by the above vision, this thesis presents two independent data science projects developed in the scope of multidisciplinary applied research. The first part analyzes fluorescence microscopy images typically produced in life science experiments, where the objective is to count how many marked neuronal cells are present in each image. Aiming to automate the task for supporting research in the area, we propose a neural network architecture tuned specifically for this use case, cell ResUnet (c-ResUnet), and discuss the impact of alternative training strategies in overcoming particular challenges of our data. The approach provides good results in terms of both detection and counting, showing performance comparable to the interpretation of human operators. As a meaningful addition, we release the pre-trained model and the Fluorescent Neuronal Cells dataset collecting pixel-level annotations of where neuronal cells are located. In this way, we hope to help future research in the area and foster innovative methodologies for tackling similar problems. The second part deals with the problem of distributed data management in the context of LHC experiments, with a focus on supporting ATLAS operations concerning data transfer failures. In particular, we analyze error messages produced by failed transfers and propose a Machine Learning pipeline that leverages the word2vec language model and K-means clustering. This provides groups of similar errors that are presented to human operators as suggestions of potential issues to investigate. The approach is demonstrated on one full day of data, showing promising ability in understanding the message content and providing meaningful groupings, in line with previously reported incidents by human operators