Algoritmos óptimos para la generación de imágenes fotorrealistas.

La presente tesis aborda la propuesta y desarrollo de una metodolog´ıa de extracci ´on de par´ametros obtenidos a partir de im´agenes de objetos reales con una c´amara digital para, posteriormente, usar dichos par´ametros como elementos de un modelo de iluminaci´on en la renderizaci´on de im´agenes generadas por computadora, que se aproximen lo m´as posible a im´agenes de objetos reales. Se propone una metodolog´ıa para que, a partir de una imagen tomada de un objeto real; se obtengan los par´ametros ´opticos que produzcan una imagen con un algoritmo de renderizaci´on, el cual tomar´a par´ametros de un algoritmo biosinspirado y gradualmente generar´a im´agenes que se aproximen cada vez m´as a la imagen del objeto real. A diferencia de las metodolog´ıas anteriores que usan un ciclo abierto para la estimaci´on de los par´ametros, la metodolog´ıa propuesta usa un ciclo cerrado para la estimaci´on de dichos par´ametros de renderizaci´on por medio del uso de una heur´ıstica bioinspirada no supervisada. En el trabajo se observa que el nivel de exactitud de los par´ametros obtenidos es directamente dependiente de la exactitud del modelo de iluminaci´on usado, con un modelo sencillo se obtienen par´ametros inexactos, con un mejor modelo se obtienen par´ametros con una mejor aproximaci´on. El presente estudio tambi´en aporta una funci´on de la medida de similitud entre dos im´agenes, as´ı como un conjunto de puntos de referencia para aproximar a una imagen con otra, estos puntos son parte de los par´ametros ´opticos obtenidos por la metodolog´ıa. Finalmente como parte del uso de una arquitectura paralela para mejorar el desempe ˜no en velocidad, se demuestra que la implantaci´on de heur´ısticas bioins-pirada es paralelizable, lo que mejora respecto al nivel de granuralizaci´on de la implantaci´on usada y a que tanto del c´odigo a ejecutar se programa en el GPU

Algoritmos óptimos para la generación de imágenes fotorrealistas

Doctorado en Ciencias de la Computació

Algoritmos óptimos para la generación de imágenes fotorrealistas

La presente tesis aborda la propuesta y desarrollo de una metodología de extracción de parámetros obtenidos a partir de imágenes de objetos reales con una cámara digital para, posteriormente, usar dichos parámetros como elementos de un modelo de iluminación en la renderización de imágenes generadas por computadora, que se aproximen lo más posible a imágenes de objetos reales. A diferencia de las metodologías anteriores que usan un ciclo abierto para la estimación de los parámetros, la metodología propuesta usa un ciclo cerrado para la estimación de dichos parámetros de renderización por medio del uso de una heurística bioinspirada no supervisada. El presente estudio también aporta una función de la medida de similitud entre dos imágenes, así como un conjunto de puntos de referencia para aproximar a una imagen con otra, estos puntos son parte de los parámetros ópticos obtenidos por la metodología

Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm

Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, and nonexistent tools. Therefore, the present research is conducted to minimize the dangers in actual tests, increase the documentation through a platform repository, and solve the autonomous trajectory of a maze with obstacles. The methodology consisted of: replicating a scenario with the parrot robot in the gazebo simulator; then the computational resources, the mechanism, and the available commands of the robot were studied; subsequently, it was determined that the genetic micro-algorithm met the minimum requirements of the robot; in the last part, it was programmed in simulation and the solution was validated in the natural environment. The results were satisfactory and it was possible to create a parrot robot in a simulation environment analogous to the typical specifications. The genetic micro-algorithm required only 100 generations to converge; therefore, the demand for computational resources did not affect the execution of the essential tasks of the robot. Finally, the maze problem could be solved autonomously in a real environment from the simulations with an error of less than 10% and without damaging the robot

Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm

Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, and nonexistent tools. Therefore, the present research is conducted to minimize the dangers in actual tests, increase the documentation through a platform repository, and solve the autonomous trajectory of a maze with obstacles. The methodology consisted of: replicating a scenario with the parrot robot in the gazebo simulator; then the computational resources, the mechanism, and the available commands of the robot were studied; subsequently, it was determined that the genetic micro-algorithm met the minimum requirements of the robot; in the last part, it was programmed in simulation and the solution was validated in the natural environment. The results were satisfactory and it was possible to create a parrot robot in a simulation environment analogous to the typical specifications. The genetic micro-algorithm required only 100 generations to converge; therefore, the demand for computational resources did not affect the execution of the essential tasks of the robot. Finally, the maze problem could be solved autonomously in a real environment from the simulations with an error of less than 10% and without damaging the robot

Performance Comparisons of Bio-Micro Genetic Algorithms on Robot Locomotion

This paper presents a comparison of four algorithms and identifies the better one in terms of convergence to the best performance for the locomotion of a quadruped robot designed. Three algorithms found in the literature review: a standard Genetic Algorithm (GA), a micro-Genetic Algorithm ( μ GA), and a micro-Artificial Immune System ( μ AIS); the fourth algorithm is a novel micro-segmented Genetic Algorithm ( μ sGA). This research shows how the computing time affects the performance in different algorithms of the gait on the robot physically; this contribution complements other studies that are limited to simulation. The μ sGA algorithm uses less computing time since the individual is segmented into specific bytes. In contrast, the use of a computer and the high demand in computational resources for the GA are avoided. The results show that the performance of μ sGA is better than the other three algorithms (GA, μ GA and μ AIS). The quadruped robot prototype guarantees the same conditions for each test. The structure of the platform was developed by 3D printing. This structure was used to accommodate the mechanisms, sensors and servomechanisms as actuators. It also has an internal battery and a multicore Embedded System (mES) to process and control the robot locomotion. The computing time was reduced using an mES architecture that enables parallel processing, meaning that the requirements for resources and memory were reduced. For example, in the experiment of a one-second gait cycle, GA uses 700% of computing time, μ GA (76%), μ AIS (32%) and μ sGA (13%). This research solves the problem of quadruped robot’s locomotion and gives a feasible solution (Central Pattern Generators, (CPGs)) with real performance parameters using a μ sGA bio-micro algorithm and a mES architecture