Software similarity measurements using UML diagrams: A systematic literature review

Every piece of software uses a model to derive its operational, auxiliary, and functional procedures. Unified Modeling Language (UML) is a standard displaying language for determining, recording, and building a software product. Several algorithms have been used by researchers to measure similarities between UML artifacts. However, there no literature studies have considered measurements of UML diagram similarities. This paper presents the results of a systematic literature review concerning similarity measurements between the UML diagrams of different software products. The study reviews and identifies similarity measurements of UML artifacts, with class diagram, sequence diagram, statechart diagram, and use case diagram being UML diagrams that are widely used as research objects for measuring similarity. Measuring similarity enables resolution of the problem domains of software reuse, similarity measurement, and clone detection. The instruments used to measure similarity are semantic and structural similarity. The findings indicate opportunities for future research regarding calculating other UML diagrams, compiling calculation information for each diagram, adapting semantic and structural similarity calculation methods, determining the best weight for each item in the diagram, testing novel proposed methods, and building or finding good datasets for use as testing material

Human-Robot Collaboration in Automotive Assembly

In the past decades, automation in the automobile production line has significantly increased the efficiency and quality of automotive manufacturing. However, in the automotive assembly stage, most tasks are still accomplished manually by human workers because of the complexity and flexibility of the tasks and the high dynamic unconstructed workspace. This dissertation is proposed to improve the level of automation in automotive assembly by human-robot collaboration (HRC). The challenges that eluded the automation in automotive assembly including lack of suitable collaborative robotic systems for the HRC, especially the compact-size high-payload mobile manipulators; teaching and learning frameworks to enable robots to learn the assembly tasks, and how to assist humans to accomplish assembly tasks from human demonstration; task-driving high-level robot motion planning framework to make the trained robot intelligently and adaptively assist human in automotive assembly tasks. The technical research toward this goal has resulted in several peer-reviewed publications. Achievements include: 1) A novel collaborative lift-assist robot for automotive assembly; 2) Approaches of vision-based robot learning of placing tasks from human demonstrations in assembly; 3) Robot learning of assembly tasks and assistance from human demonstrations using Convolutional Neural Network (CNN); 4) Robot learning of assembly tasks and assistance from human demonstrations using Task Constraint-Guided Inverse Reinforcement Learning (TC-IRL); 5) Robot learning of assembly tasks from non-expert demonstrations via Functional Objective-Oriented Network (FOON); 6) Multi-model sampling-based motion planning for trajectory optimization with execution consistency in manufacturing contexts. The research demonstrates the feasibility of a parallel mobile manipulator, which introduces novel conceptions to industrial mobile manipulators for smart manufacturing. By exploring the Robot Learning from Demonstration (RLfD) with both AI-based and model-based approaches, the research also improves robots’ learning capabilities on collaborative assembly tasks for both expert and non-expert users. The research on robot motion planning and control in the dissertation facilitates the safety and human trust in industrial robots in HRC

Modeling variation in multi-station compliant assembly using parametric space envelope

Non-rigid compliant parts are widely used in industries. One of the biggest challenges facing industries is geometric variation management of these compliant parts which can directly impact product quality and functionality. Existing rigid body-based variation modeling approaches are not suitable for compliant assembly while finite element analysis based methods have the disadvantage of requiring heavy computation efforts and detailed design information which is unavailable during preliminary design phase. Hence, this paper develops a novel methodology to evaluate geometric variation propagation in multi-station compliant assembly based on parametric space envelope (i.e. variation tool constructed from parametric curves). Three sources of variation: location-led positional variation, assembly deformation-induced variation and station transition caused variation are analyzed. In this study, geometric variations are modeled indirectly through a compact set of boundary control points. Compared with existing methods where geometric variation is modeled by tracking key feature points on the manufacturing part, the proposed approach brings many benefits. It can handle arbitrary complex compliant part, and it lowers computation requirement in many real applications. The method is illustrated and verified through an industrial case study on a multi-station compliant panel assembly. The developed method provides industries a new way to manage geometric variation from compliant assembly

SIMILARITY METRICS APPLIED TO GRAPH BASED DESIGN MODEL AUTHORING

Model reuse is typically facilitated by search and retrieval tools, matching the sought model with models in a database. This research aims at providing similar assistance to users authoring design exemplars, a data structure to represent parametric and geometric design problems. The design exemplar represents design problems in the form of a bi-partite graph consisting of entities and relations. Authoring design exemplars for relatively complex design problems can be time consuming and error prone. This forms the motivation of developing a search and retrieval tool, capable of retrieving exemplars that are similar to the exemplar that a user is trying to author, from a database of previously authored exemplars. In order to develop such a tool, similarity measures have been developed to evaluate the similarity between the exemplar that a user is trying to author and target exemplars in the database. Two exemplars can be considered similar based on the number and types of entities and relations shared by them. However, exemplars meant for the same purpose can be authored using different entities and relations. Hence, the two main challenges in developing a search and retrieval tool are to evaluate the similarity between exemplars based on structure and semantics. In this research, four distinct similarity metrics are developed to evaluate the structural similarity between exemplars for exemplar retrieval: entity similarity, relation similarity, attribute similarity, and graph matching similarity. As well, a thorough understanding of semantics in engineering design has been developed. Different types of semantic information found in engineering design have been identified and classified. Design intent and rationale have been proposed as the two main types of semantic information necessary to evaluate the semantic similarity between exemplars. The semantic and structural similarity measures have been implemented as separate modules in an interactive modeling environment. Several experiments have been conducted in order to evaluate the accuracy and effectiveness of the proposed similarity measures. It is found that for most queries, the semantic retrieval module retrieves exemplars that are not retrieved by structural retrieval module and vice versa

Algorithmic and combinatorial problems on multi-UAV systems

Mathematics has always been a fundamental piece in robotics and, research in robotics has played an important role in the development of mathematics. This thesis is motivated by the growing interest on problems that appear in aerial robotics applications, specifically, on cooperative systems of multiple aerial robots or drones. Most of the research works in multi-robot systems have focused primarily on construction and validation of working systems, rather than more general and formal analysis of problems and solutions. By contrast, this thesis focuses on formally solving problems of aerial multi-robot systems from a discrete and combinatorial optimization perspective. Inspired on problems of this area, the thesis introduces some new theoretical models and problems of interest for mathematicians and computer scientists. The following topics are covered in this thesis: (1) synchronization: design of a coordination strategy to allow periodical communication between the members of a cooperative team while performing a task along fixed trajectories in a scenario with limited communication range, (2) robustness: analysis of the detrimental effects in the performance of a synchronized system when one or more robots fail, (3) stochastic strategies: performance analysis of a synchronized system using drones with stochastic decision making, and (4) task allocation: decentralized coordination to perform periodical task allocation in order to maintain a balanced work load for all members of a team with limited communication range. In the first part of the thesis, we study the synchronization problem giving a theoretical characterization of the solutions and, we present an algorithm to build a synchronized system for a given set of covering trajectories. The second part focuses on the study of the robustness in a synchronized system regarding to two key aspects: covering of the working area and communication between the members of the team. We rigorously study several combinatorial problems to measure how robust a system is to deal with drones failures. Connections of theseproblemswithnumbertheory, graphtheory, circulantgraphsandpolynomial multiplication are shown. The third part is devoted to an analysis of synchronized systems using random aerial robots. This topic is closely related to the random walk theory. It is shown that stochastic strategies increase the robustness of a synchronized system. Finally, this thesis introduces the block sharing strategy to addresstheproblemofmaintainingabalancedtaskallocationamongtherobotsby using periodical communications. A proof on the convergence to an optimal task allocation is given and, a case study for structure construction using a cooperative team of aerial robots is presented. All algorithms developed in this thesis have been implemented and extensive experiments have been conducted to analyze and validate the proposed methods.Las matemáticas siempre han sido una pieza fundamental en el desarrollo de la robótica, así como los problemas de robótica han jugado un importante papel en el desarrollo de las matemáticas. Esta tesis está motivada por el creciente interés en problemas que aparecen en aplicaciones de robótica aérea, específicamente, está enfocada en sistemas cooperativos de múltiples robots aéreos o drones. La mayoría de los trabajos de investigación en sistemas de robots se han centrado en la construcción y validación de arquitecturas desde un enfoque empírico. Por el contrario, esta tesis enfoca el estudio de problemas relacionados con tareas para equipos de robots aéreos desde el punto de vista de la optimización discreta y combinatoria. Inspirada en problemas de este campo, esta memoria plantea nuevos modelos teóricos y problemas de interés para las matemáticas aplicadas y la ciencia computacional. Enestatesisse abordanlostemassiguientes: (1) sincronización: diseñodeuna estrategia de coordinación que permita comunicación periódica entre los miembros de un equipo cooperativo mientras ejecutan una tarea sobre trayectorias fijadas, (2) robustez: análisis del efecto que produce el fallo de los agentes en un sistema sincronizado, (3)estrategias estocásticas: análisisdelfuncionamientodeunsistema sincronizado cuando se utilizan drones con toma de decisiones aleatorias, y (4) asignación de tareas: coordinación no centralizada usando asignación periódica de tareas que permita mantener una carga de trabajo balanceada. En la primera parte, se estudia teóricamente el problema de la sincronización, dando condiciones necesarias y suficientes para la existencia de solución y se presenta un algoritmo que construye un sistema sincronizado para un conjunto fijado de trayectorias de vuelo. La segunda parte de la tesis estudia la robustez de un sistema sincronizado teniendo en cuenta dos aspectos fundamentales: el cubrimiento del terreno y la comunicación entre los miembros del equipo. Se estudian de forma rigurosa problemas combinatorios que surgen cuando se requiere saber cómo de robusto es un sistema con respecto a fallos. Se muestran conexiones con áreas matemáticas como la teoría de números, la teoría de grafos, los grafos circulantes o multiplicación de polinomios. En la tercera parte de la tesis, se estudia la robustez del sistema cuando se introducen decisiones aleatorias de los drones. Se prueba la relación de este problema con la teoría de caminatas aleatorias y se muestra que el uso de estrategias estocásticas supone una mejora de la robustez del sistema sincronizado. Por último, se propone la estrategia de coordinación por bloques para la asignación balanceada de tareas. Se prueba la convergencia del método a una asignación óptima y se realiza un estudio de caso para la construcción de una estructura mediante un equipo cooperativo de drones. Todos los algoritmos desarrollados en esta tesis han sido implementados y se han llevado a cabo diversos experimentosque demuestran la validez de los métodos propuestos

Addressing Tasks Through Robot Adaptation

Developing flexible, broadly capable systems is essential for robots to move out of factories and into our daily lives, functioning as responsive agents that can handle whatever the world throws at them. This dissertation focuses on two kinds of robot adaptation. Modular self-reconfigurable robots (MSRR) adapt to the requirements of their task and environments by transforming themselves. By rearranging the connective structure of their component robot modules, these systems can assume different morphologies: for example, a cluster of modules might configure themselves into a car to maneuver on flat ground, a snake to climb stairs, or an arm to pick and place objects. Conversely, environment augmentation is a strategy in which the robot transforms its environment to meet its own needs, adding physical structures that allow it to overcome obstacles. In both areas, the presented work includes elements of hardware design, algorithms, and integrated systems, with the common goal of establishing these methods of adaptation as viable strategies to address tasks. The research takes a systems-level view of robotics, placing particular emphasis on experimental validation in hardware

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

View generated database

This document represents the final report for the View Generated Database (VGD) project, NAS7-1066. It documents the work done on the project up to the point at which all project work was terminated due to lack of project funds. The VGD was to provide the capability to accurately represent any real-world object or scene as a computer model. Such models include both an accurate spatial/geometric representation of surfaces of the object or scene, as well as any surface detail present on the object. Applications of such models are numerous, including acquisition and maintenance of work models for tele-autonomous systems, generation of accurate 3-D geometric/photometric models for various 3-D vision systems, and graphical models for realistic rendering of 3-D scenes via computer graphics

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

The Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992)

This document contains papers presented at the Space Operations, Applications, and Research Symposium (SOAR) hosted by the U.S. Air Force (USAF) on 4-6 Aug. 1992 and held at the JSC Gilruth Recreation Center. The symposium was cosponsored by the Air Force Material Command and by NASA/JSC. Key technical areas covered during the symposium were robotic and telepresence, automation and intelligent systems, human factors, life sciences, and space maintenance and servicing. The SOAR differed from most other conferences in that it was concerned with Government-sponsored research and development relevant to aerospace operations. The symposium's proceedings include papers covering various disciplines presented by experts from NASA, the USAF, universities, and industry