Análise de métodos de otimização avançados em projeto mecânico

Advanced optimization methods are widely applied to mechanical design, mainly for its abilities to solve complex problems that traditional optimization techniques such as gradient-based methods do not present. With its increasing popularity, the number of algorithms found in the literature is vast. In this work three algorithms are implemented, namely Particle Swarm Optimization (PSO), Differential Evolution (DE) and Teaching-Learning- Based Optimization (TLBO). Firstly, the application of these algorithms is analyzed for a composition function benchmark and three mechanical design minimization problems (the weight of a speed reducer, the volume of a three-bar truss and the area of a square plate with a cut-out hole). Furthermore, as the scope of available algorithms increases, the choice of programming tools to implement them is also vast, and generally made considering subjective criteria or difficulties in using enhancing strategies such as parallel processing. Thereby an analysis of programming tools applied to metaheuristic algorithms is carried out using four programming languages with distinct characteristics: Python, MATLAB, Java and C++. The selected algorithms and problems are coded using each programming language, which are initially compared in a sequential processing implementation. Additionally, in order to analyze potential gains in performance, parallel processing procedures are implemented using features of each programming language. The application of the algorithms to the mechanical design problems demonstrates good results in the achieved solutions. In what concerns to the computational time, sequential and processing results present considerable differences between programming languages while the implementation of parallel processing procedures demonstrates significant benefits for complex problems.Métodos avançados de otimização têm sido amplamente aplicados ao projeto mecânico, principalmente pela sua capacidade de resolver problemas complexos que técnicas tradicionais de otimização como os métodos baseados em gradiente não apresentam. Devido à sua crescente popularidade, o número de algoritmos encontrados na literatura é vasto. Neste trabalho são implementados três algoritmos distintos, Otimização por Bando de Partículas (PSO), Evolução Diferencial (DE) e Otimização Baseada no Ensino-Aprendizagem (TLBO). Inicialmente, a aplicação destes algoritmos é analisada numa função composta e em três problemas de minimização de projeto mecânico (o peso de um redutor de velocidade, o volume de uma estrutura de três barras e a área de uma placa quadrada com um furo circular). Além disso, com o aumento do número de algoritmos existentes, a escolha de ferramentas de programação para implementá-los também é vasta e geralmente feita considerando critérios subjetivos ou dificuldades no uso de estratégias de melhoria como processamento paralelo. Deste modo, no presente trabalho é realizada uma análise de ferramentas de programação aplicadas a algoritmos metaheurísticos, utilizando linguagens de programação com distintas características: Python, MATLAB, Java e C++. Os algoritmos e problemas selecionados são programados em cada linguagem de programação, e inicialmente comparados numa implementação de processamento sequencial. Além disso, de forma a analisar possíveis ganhos de desempenho, são implementados procedimentos de processamento paralelo utilizando recursos de cada linguagem de programação. A aplicação dos algoritmos aos problemas de projeto mecânico demonstra bons resultados nas soluções obtidas. Os resultados, em termos de tempo computacional, de processamento sequencial e paralelo, apresentam diferenças consideráveis entre as linguagens de programação. A implementação de procedimentos de processamento paralelo demonstra benefícios significativos em problemas complexos.Mestrado em Engenharia Mecânic

Navigational Strategies for Control of Underwater Robot using AI based Algorithms

Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robot’s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

A memetic approach to the inverse kinematics problem for robotic applications

The inverse kinematics problem of an articulated robot system refers to computing the joint configuration that places the end-effector at a given position and orientation. To overcome the numerical instability of the Jacobian-based algorithms around singular joint configurations, the inverse kinematics is formulated as a constrained minimization problem in the configuration space of the robot. In previous works this problem has been solved for redundant and non-redundant robots using evolutionary-based algorithms. However, despite the flexibility and accuracy of the direct search approach of evolutionary algorithms, these algorithms are not suitable for most robot applications given their low convergence speed rate and the high computational cost of their population-based approach. In this thesis, we propose a memetic variant of the Differential Evolution (DE) algorithm to increase its convergence speed on the kinematics inversion problem of articulated robot systems. With the aim to yield an efficient trade-off between exploration and exploitation of the search space, the memetic approach combines the global search scheme of the standard DE with an independent local search mechanisms, called discarding. The proposed scheme is tested on a simulation environment for different benchmark serial robot manipulators and anthropomorphic robot hands. Results show that the memetic differential evolution is able to find solutions with high accuracy in less generations than the original DE. -----------------------------------------------------------La cinemática inversa de los robots manipuladores se refiere al problema de calcular las coordenadas articulares del robot a partir de coordenadas conocidas de posición y orientación de su extremo libre. Para evitar la inestabilidad numérica de los métodos basados en la inversa de la matriz Jacobiana en la vecindad de configuraciones singulares, el problema de cinemática inversa es definido en el espacio de configuraciones del robot manipulador como un problema de optimización con restricciones. Este problema de optimización ha sido previamente resuelto con métodos evolutivos para robots manipuladores, redundantes y no redundantes, obteniéndose buenos resultados; sin embargo, estos métodos exhiben una baja velocidad de convergencia no adecuada para aplicaciones robóticas. Para incrementar la velocidad de convergencia de estos algoritmos, se propone un método memético de evolución differencial. El enfoque de búsqueda directa propuesto combina el esquema estándar de evolución diferencial con un mecanismo independiente de refinamiento local, llamado discarding o descarte. El desempeño del método propuesto es evaluado en un entorno de simulación para diferentes robot manipuladores y manos robóticas antropomórficas. Los resultados obtenidos muestran una importante mejora en precisión y velocidad de convergencia en comparación del método DE original.Programa en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Pedro M. Urbano de Almeida Lima; Vocal: Cecilia Elisabet García Cena; Secretario: Mohamed Abderrahim Fichouch

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Multiobjective differential evolution based on fuzzy performance feedback: Soft constraint handling and its application in antenna designs

The recently emerging Differential Evolution is considered one of the most powerful tools for solving optimization problems. It is a stochastic population-based search approach for optimization over the continuous space. The main advantages of differential evolution are simplicity, robustness and high speed of convergence. Differential evolution is attractive to researchers all over the world as evidenced by recent publications. There are many variants of differential evolution proposed by researchers and differential evolution algorithms are continuously improved in its performance. Performance of differential evolution algorithms depend on the control parameters setting which are problem dependent and time-consuming task. This study proposed a Fuzzy-based Multiobjective Differential Evolution (FMDE) that exploits three performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution process. We apply the fuzzy inference rules to these metrics in order to adaptively adjust the associated control parameters of the chosen mutation strategy used in this algorithm. The proposed FMDE is evaluated on the well known ZDT, DTLZ, and WFG benchmark test suites. The experimental results show that FMDE is competitive with respect to the chosen state-of-the-art multiobjective evolutionary algorithms. The advanced version of FMDE with adaptive crossover rate (AFMDE) is proposed. The proof of concept AFMDE is then applied specifically to the designs of microstrip antenna array. Furthermore, the soft constraint handling technique incorporates with AFMDE is proposed. Soft constraint AFMDE is evaluated on the benchmark constrained problems. AFMDE with soft constraint handling technique is applied to the constrained non-uniform circular antenna array design problem as a case study

Evolutionary-based global localization and mapping of three dimensional environments

A fully autonomous robot must obtain and interpret information about the environment to execute several tasks. The mobile robot mapping or SLAM problem is closely related to these abilities. It consists of interpreting the information perceived by its sensors in order to build map and localize itself in it. There are many other robot skills that depend on this task; thus, it is one of the most important problems to be solved by a truly autonomous robot. The objective of this work is to design various specific tools related to the mapping problem in order to improve the autonomy of MANFRED-2, which is a mobile robot fully developed by the Robotics Lab research group of the Systems Engineering and Automation Department of the Carlos III University of Madrid. The localization problem in mobile robotics can be defined as the search of the robot's coordinates in a known environment. If there is no information about the initial location, we are talking about global localization. In this work, we have developed an algorithm that solves this problem in a three-dimensional environment using Differential Evolution, which is a particle-based evolutionary algorithm that evolves in time to the solution that yields the cost function lowest value. The proposed method has many features that make it very robust and reliable: thresholding and discarding mechanisms, different cost functions, effective convergence criteria, and so on. The resulting global localization module has been tested in numerous experiments. The high accuracy of the method allows its application in manipulation tasks. If the environment information is given by laser readings, it is essential to correct the local errors between pairs of scans to improve the map quality, which is called registration or scan matching. We have implemented a scan matching algorithm for three-dimensional environments. It is also based on the Differential Evolution method. The high accuracy and computational effi ciency of the proposed method have been demonstrated with experimental results. The last problem addressed here consists of detecting when the robot is navigating through a known place (loop detection). After that, the accumulated error can be minimized to give consistency to the global map (loop closure). We have developed a loop detection method that compares features extracted from two different scans to obtain a loop indicator. This approach allows the introduction of very different characteristics in the descriptor. First, the surface features include the geometric forms of the scan (lines, planes, and spheres). Second, the numerical features describe other several properties (volume, average range, curvature, etc.). The algorithm has been tested with real data to demonstrate its effi ciency. All true loops are correctly detected and no false detections are appreciated when the mobile robot is covering a long trajectory. The results are similar or even better than those obtained by other research groups. In addition, it is a more versatile method because it admits a wide variety of scan properties and different weights in the comparison formula. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Un robot completamente autónomo debe ser capaz de obtener e interpretar la información del entorno para ejecutar diversas tareas. El problema de mapeado o SLAM para robots móviles está estrechamente relacionado con estas habilidades. Consiste en interpretar la infomació percibida por sus sensores para construir un mapa y localizarse. Hay muchas otras tareas que dependen del mapeado, luego este es uno de los problemas más importantes para un robot móvil. El objetivo de este trabajo es el desarrollo de varias herramientas específicas relacionadas con el mapeado de entornos tridimensionales. Con ellas se mejorar a la autonomía del robot manipulador MANFRED-2, que es un robot móvil desarrollado íntegramente en el Robotics Lab del Departamento de Ingeniería de Sistemas y Automática de la Universidad Carlos III de Madrid. El problema de localización para un robot móvil puede ser de nido como la búsqueda de las coordenadas del robot dentro de un entorno conocido. Si no hay información sobre la localización inicial, el problema se denomina localización global. En este trabajo se ha desarrollado un módulo que soluciona este problema para entornos tridimensionales utilizando el algoritmo Differential Evolution, el cual es un filtro evolutivo basado en part culas que evolucionan con el tiempo hacia la solución que tiene asociado un mejor valor para una función de coste dada. El algoritmo desarrollado tiene diversas características que lo hacen muy robusto y fiable: mecanismos de umbralización y descarte, diferentes funciones de coste, criterios de convergencia efectivos, etc. El módulo de localización global se ha probado en m últiples experimentos. La elevada precisión de este método permite que el robot sea utilizado en tareas de manipulación. Si la información del entorno viene dada por barridos de un láser, es muy importante que se pueda corregir el error local entre pares de barridos para mejorar la calidad del mapa. Este proceso se conoce como registro o scan matching. Hemos implementado un algoritmo que resuelve este problema en entornos tridimensionales. Est a tambi en basado en el Differential Evolution. Si se elige la función de forma adecuada es posible resolver el problema de scan matching utilizando este método. La elevada precisión y la eficiencia computacional se han demostrado en los resultados experimentales. El último problema abordado aquí consiste en detectar cuando el robot está navegando por un entorno conocido. Después de esto se podrá minimizar el error acumulado para aumentar la consistencia del mapa. La tarea de detecci on se llama usualmente detección de bucles, mientras que la minimización del error es el cierre del bucle. Se ha desarrollado un algoritmo de detección que extrae las características más importantes de dos barridos del láser para obtener un indicador que es usado como umbral para detectar si el robot está en un lugar que ha visitado previamente. Nuestro método permite tener en cuenta características muy diferentes. Primero, las caractrísticas de superficie permiten incluir las formas geométricas presentes en el barrido (líneas, planos y esferas). Segundo, las características numéricas permiten describir diversas propiedades (volumen, rango medio, curvatura, etc.). El algoritmo ha sido probado con datos reales para demostrar su eficiencia. Todos los bucles son detectados correctamente y no se aprecian falsos positivos cuando el robot está navegando por una trayectoria larga con varios bucles. Los resultados son parecidos o mejores que los que obtienen otros grupos de investigación. Además, este es un m etodo muy versátil pues admite multitud de variables y diferentes pesos en la fórmula de comparación

Evolutionary Neuro-Computing Approaches to System Identification

System models are essentially required for analysis, controller design and future prediction. System identification is concerned with developing models of physical system. Although linear system identification got enriched with several useful classical methods, nonlinear system identification always remained active area of research due to the reason that most of the real world systems are nonlinear in nature and moreover, having non-unique models. Among the several conventional system identification techniques, the Volterra series, Hammerstein-Wiener and polynomial model identification involve considerable computational complexities. The other techniques based on regression models such as nonlinear autoregressive exogenous (NARX) and nonlinear autoregressive moving average exogenous (NARMAX), also suffer from dfficulty in choosing regressors