Monte Carlo localization algorithm based on particle swarm optimization

In wireless sensor networks, Monte Carlo localization for mobile nodes has a large positioning error and slow convergence speed. To address the challenges of low sampling efficiency and particle impoverishment, a time sequence Monte Carlo localization algorithm based on particle swarm optimization (TSMCL-BPSO) is proposed in this paper. Firstly, the sampling region is constructed according to the overlap of the initial sampling region and the Monte Carlo sampling region. Then, particle swarm optimization (PSO) strategy is adopted to search the optimum position of the target node. The velocity of particle swarm is updated by adaptive step size and the particle impoverishment is improved by distributed estimation and particle replication, which avoids the local optimum caused by the premature convergence of particles. Experiment results indicate that the proposed algorithm improves the particle fitness, increases the particle searching efficiency, and meanwhile the lower positioning error can be obtained at the node\u27s maximum speed of 70 m/s

The Application of PSO in Structural Damage Detection: An Analysis of the Previously Released Publications (2005–2020)

The structural health monitoring (SHM) approach plays a key role not only in structural engineering but also in other various engineering disciplines by evaluating the safety and performance monitoring of the structures. The structural damage detection methods could be regarded as the core of SHM strategies. That is because the early detection of the damages and measures to be taken to repair and replace the damaged members with healthy ones could lead to economic advantages and would prevent human disasters. The optimization-based methods are one of the most popular techniques for damage detection. Using these methods, an objective function is minimized by an optimization algorithm during an iterative procedure. The performance of optimization algorithms has a significant impact on the accuracy of damage identification methodology. Hence, a wide variety of algorithms are employed to address optimization-based damage detection problems. Among different algorithms, the particle swarm optimization (PSO) approach has been of the most popular ones. PSO was initially proposed by Kennedy and Eberhart in 1995, and different variants were developed to improve its performance. This work investigates the objectives, methodologies, and results obtained by over 50 studies (2005-2020) in the context of the structural damage detection using PSO and its variants. Then, several important open research questions are highlighted. The paper also provides insights on the frequently used methodologies based on PSO, the computational time, and the accuracy of the existing methodologies

Automated optimization of reconfigurable designs

Currently, the optimization of reconfigurable design parameters is typically done manually and often involves substantial amount effort. The main focus of this thesis is to reduce this effort. The designer can focus on the implementation and design correctness, leaving the tools to carry out optimization. To address this, this thesis makes three main contributions. First, we present initial investigation of reconfigurable design optimization with the Machine Learning Optimizer (MLO) algorithm. The algorithm is based on surrogate model technology and particle swarm optimization. By using surrogate models the long hardware generation time is mitigated and automatic optimization is possible. For the first time, to the best of our knowledge, we show how those models can both predict when hardware generation will fail and how well will the design perform. Second, we introduce a new algorithm called Automatic Reconfigurable Design Efficient Global Optimization (ARDEGO), which is based on the Efficient Global Optimization (EGO) algorithm. Compared to MLO, it supports parallelism and uses a simpler optimization loop. As the ARDEGO algorithm uses multiple optimization compute nodes, its optimization speed is greatly improved relative to MLO. Hardware generation time is random in nature, two similar configurations can take vastly different amount of time to generate making parallelization complicated. The novelty is efficient use of the optimization compute nodes achieved through extension of the asynchronous parallel EGO algorithm to constrained problems. Third, we show how results of design synthesis and benchmarking can be reused when a design is ported to a different platform or when its code is revised. This is achieved through the new Auto-Transfer algorithm. A methodology to make the best use of available synthesis and benchmarking results is a novel contribution to design automation of reconfigurable systems.Open Acces

Global Localization based on Evolutionary Optimization Algorithms for Indoor and Underground Environments

Mención Internacional en el título de doctorA fully autonomous robot is defined by its capability to sense, understand and move within the environment to perform a specific task. These qualities are included within the concept of navigation. However, among them, a basic transcendent one is localization, the capacity of the system to know its position regarding its surroundings. Therefore, the localization issue could be defined as searching the robot’s coordinates and rotation angles within a known environment. In this thesis, the particular case of Global Localization is addressed, when no information about the initial position is known, and the robot relies only on its sensors. This work aims to develop several tools that allow the system to locate in the two most usual geometric map representations: occupancy maps and Point Clouds. The former divides the dimensional space into equally-sized cells coded with a binary value distinguishing between free and occupied space. Point Clouds define obstacles and environment features as a sparse set of points in the space, commonly measured through a laser sensor. In this work, various algorithms are presented to search for that position through laser measurements only, in contrast with more usual methods that combine external information with motion information of the robot, odometry. Therefore, the system is capable of finding its own position in indoor environments, with no necessity of external positioning and without the influence of the uncertainty that motion sensors typically induce. Our solution is addressed by implementing various stochastic optimization algorithms or Meta-heuristics, specifically those bio-inspired or commonly known as Evolutionary Algorithms. Inspired by natural phenomena, these algorithms are based on the evolution of a series of particles or population members towards a solution through the optimization of a cost or fitness function that defines the problem. The implemented algorithms are Differential Evolution, Particle Swarm Optimization, and Invasive Weed Optimization, which try to mimic the behavior of evolution through mutation, the movement of swarms or flocks of animals, and the colonizing behavior of invasive species of plants respectively. The different implementations address the necessity to parameterize these algorithms for a wide search space as a complete three-dimensional map, with exploratory behavior and the convergence conditions that terminate the search. The process is a recursive optimum estimation search, so the solution is unknown. These implementations address the optimum localization search procedure by comparing the laser measurements from the real position with the one obtained from each candidate particle in the known map. The cost function evaluates this similarity between real and estimated measurements and, therefore, is the function that defines the problem to optimize. The common approach in localization or mapping using laser sensors is to establish the mean square error or the absolute error between laser measurements as an optimization function. In this work, a different perspective is introduced by benefiting from statistical distance or divergences, utilized to describe the similarity between probability distributions. By modeling the laser sensor as a probability distribution over the measured distance, the algorithm can benefit from the asymmetries provided by these divergences to favor or penalize different situations. Hence, how the laser scans differ and not only how much can be evaluated. The results obtained in different maps, simulated and real, prove that the Global Localization issue is successfully solved through these methods, both in position and orientation. The implementation of divergence-based weighted cost functions provides great robustness and accuracy to the localization filters and optimal response before different sources and noise levels from sensor measurements, the environment, or the presence of obstacles that are not registered in the map.Lo que define a un robot completamente autónomo es su capacidad para percibir el entorno, comprenderlo y poder desplazarse en ´el para realizar las tareas encomendadas. Estas cualidades se engloban dentro del concepto de la navegación, pero entre todas ellas la más básica y de la que dependen en buena parte el resto es la localización, la capacidad del sistema de conocer su posición respecto al entorno que lo rodea. De esta forma el problema de la localización se podría definir como la búsqueda de las coordenadas de posición y los ángulos de orientación de un robot móvil dentro de un entorno conocido. En esta tesis se aborda el caso particular de la localización global, cuando no existe información inicial alguna y el sistema depende únicamente de sus sensores. El objetivo de este trabajo es el desarrollo de varias herramientas que permitan que el sistema encuentre la localización en la que se encuentra respecto a los dos tipos de mapa más comúnmente utilizados para representar el entorno: los mapas de ocupación y las nubes de puntos. Los primeros subdividen el espacio en celdas de igual tamaño cuyo valor se define de forma binaria entre espacio libre y ocupado. Las nubes de puntos definen los obstáculos como una serie dispersa de puntos en el espacio comúnmente medidos a través de un láser. En este trabajo se presentan varios algoritmos para la búsqueda de esa posición utilizando únicamente las medidas de este sensor láser, en contraste con los métodos más habituales que combinan información externa con información propia del movimiento del robot, la odometría. De esta forma el sistema es capaz de encontrar su posición en entornos interiores sin depender de posicionamiento externo y sin verse influenciado por la deriva típica que inducen los sensores de movimiento. La solución se afronta mediante la implementación de varios tipos de algoritmos estocásticos de optimización o Meta-heurísticas, en concreto entre los denominados bio-inspirados o comúnmente conocidos como Algoritmos Evolutivos. Estos algoritmos, inspirados en varios fenómenos de la naturaleza, se basan en la evolución de una serie de partículas o población hacia una solución en base a la optimización de una función de coste que define el problema. Los algoritmos implementados en este trabajo son Differential Evolution, Particle Swarm Optimization e Invasive Weed Optimization, que tratan de imitar el comportamiento de la evolución por mutación, el movimiento de enjambres o bandas de animales y la colonización por parte de especies invasivas de plantas respectivamente. Las distintas implementaciones abordan la necesidad de parametrizar estos algoritmos para un espacio de búsqueda muy amplio como es un mapa completo, con la necesidad de que su comportamiento sea muy exploratorio, así como las condiciones de convergencia que definen el fin de la búsqueda ya que al ser un proceso recursivo de estimación la solución no es conocida. Estos algoritmos plantean la forma de buscar la localización ´optima del robot mediante la comparación de las medidas del láser en la posición real con lo esperado en la posición de cada una de esas partículas teniendo en cuenta el mapa conocido. La función de coste evalúa esa semejanza entre las medidas reales y estimadas y por tanto, es la función que define el problema. Las funciones típicamente utilizadas tanto en mapeado como localización mediante el uso de sensores láser de distancia son el error cuadrático medio o el error absoluto entre distancia estimada y real. En este trabajo se presenta una perspectiva diferente, aprovechando las distancias estadísticas o divergencias, utilizadas para establecer la semejanza entre distribuciones probabilísticas. Modelando el sensor como una distribución de probabilidad entorno a la medida aportada por el láser, se puede aprovechar la asimetría de esas divergencias para favorecer o penalizar distintas situaciones. De esta forma se evalúa como difieren las medias y no solo cuanto. Los resultados obtenidos en distintos mapas tanto simulados como reales demuestran que el problema de la localización se resuelve con éxito mediante estos métodos tanto respecto al error de estimación de la posición como de la orientación del robot. El uso de las divergencias y su implementación en una función de coste ponderada proporciona gran robustez y precisión al filtro de localización y gran respuesta ante diferentes fuentes y niveles de ruido, tanto de la propia medida del sensor, del ambiente y de obstáculos no modelados en el mapa del entorno.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Fabio Bonsignorio.- Secretario: María Dolores Blanco Rojas.- Vocal: Alberto Brunete Gonzále

Novel DV-hop Method Based on Krill Swarm Algorithm Used for Wireless Sensor Network Localization

Wireless sensor network (WSN) is self-organizing network; it consists of a large number of sensor nodes with perception, calculation ability and communication ability. As we all know, the floor, walls or people moving has an effect on indoor localization, so it will result in multi-path phenomena and decrease signal strength, also the received signal strength indicator (RSSI) is unable to gain higher accuracy of positioning. When using multilateral measurement method to calculate the unknown node coordinates, it will generate big error in range-free distance vector-hop (DV-hop) localization algorithm of WSN. In order to improve the WSN positioning accuracy in indoor condition, more reasonable distribute network resources, in this paper, we firstly propose krill swarm algorithm used for WSN localization. First, we detailed analyze the multilateral measurement method in DV-hop localization algorithm. The position problem can be transformed into a global optimization problem. Then, we adequately utilize the advantage of calculating optimization problem. We apply the krill swarm algorithm into the stage of estimating unknown node coordinates in DV-hop algorithm to realize localization. Finally, the simulation experience results show that the localization with krill swarm algorithm has an obviously higher positioning precision and accuracy stability with different anchor node proportion and nodes. We also make comparison with DV-hop algorithm and the newest localization algorithm

Energy-Based Acoustic Localization by Improved Elephant Herding Optimization

UIDB/EEA/50008/2020The present work proposes a new approach to address the energy-based acoustic localization problem. The proposed approach represents an improved version of evolutionary optimization based on Elephant Herding Optimization (EHO), where two major contributions are introduced. Firstly, instead of random initialization of elephant population, we exploit particularities of the problem at hand to develop an intelligent initialization scheme. More precisely, distance estimates obtained at each reference point are used to determine the regions in which a source is most likely to be located. Secondly, rather than letting elephants to simply wander around in their search for an update of the source location, we base their motion on a local search scheme which is found on a discrete gradient method. Such a methodology significantly accelerates the convergence of the proposed algorithm, and comes at a very low computational cost, since discretization allows us to avoid the actual gradient computations. Our simulation results show that, in terms of localization accuracy, the proposed approach significantly outperforms the standard EHO one for low noise settings and matches the performance of an existing enhanced version of EHO (EEHO). Nonetheless, the proposed scheme achieves this accuracy with significantly less number of function evaluations, which translates to greatly accelerated convergence in comparison with EHO and EEHO. Finally, it is also worth mentioning that the proposed methodology can be extended to any population-based metaheuristic method (it is not only restricted to EHO), which tackles the localization problem indirectly through distance measurements.publishersversionpublishe