Abstract: Currently, wireless communication networks have been acquired great relevance in our daily life, including data acquisition, data processing, control and analysis in different applications. Therefore, robotic systems cannot be an exception, in such a way, it is an important study that considers the effects caused by inclusion of wireless networks in the control loop of robotic systems, as well as, the designing of intelligent control systems that can deal with such effects in real-time. Hence, this research work focuses on the designing of an on-line intelligent controller that achieves trajectory tracking of a robotic mobile system which is in a networked communication environment. The proposed controller can deal with unknown dynamics, unknown external and internal disturbances, unknown communication delays and packet losses. Such a controller is designed using a discrete-time approach based on an inverse optimal control methodology for trajectory tracking and a recurrent high-order neural network identifier. Applicability of the proposed scheme is shown through real-time results using a tracked robot platform controlled through a wireless network under different network scenarios. Besides, obtained results, show good performance. The designed scheme can be extended to any unknown or uncertain nonlinear system which lies in a networked environment. One of the main advantages of the proposed scheme is the robustness of the proposed intelligent controller to work on networked environments under direct communication channels, as well as through two different communication channels, evidently for more complex configurations of the used communication channel, the performance of the proposed scheme can be deteriorated.

Resumen: Actualmente, las redes de comunicación inalámbricas han adquirido gran relevancia en nuestra vida diaria, incluida la adquisición de datos, su procesamiento, el control y el análisis de datos, en diferentes aplicaciones. Así pues, los sistemas robóticos no pueden ser una excepción, por lo que es importante y relevante estudiar los efectos causados por la inclusión de redes inalámbricas en el lazo de control de sistemas robóticos, así como el diseño de sistemas de control inteligentes que pueden manejar tales efectos en tiempo real. Por tanto, este trabajo de investigación se centra en el diseño de un controlador inteligente en línea que logra el seguimiento de la trayectoria de un sistema móvil robótico que se encuentra en un entorno de comunicación en red. El controlador propuesto puede manejar dinámicas desconocidas, saturación de los actuadores, perturbaciones externas e internas desconocidas, retrasos de comunicación desconocidos y pérdidas de paquetes. Dicho controlador está diseñado utilizando un enfoque de tiempo discreto basado en un control óptimo inverso y un identificador neuronal de alto orden recurrente. La aplicabilidad del esquema propuesto se muestra a través de resultados en tiempo real utilizando una plataforma de robot tipo oruga, controlada a través de una red inalámbrica bajo diferentes escenarios de la red. Además, los resultados obtenidos, muestran un buen desempeño. El esquema diseñado puede extenderse a cualquier sistema no lineal desconocido o incierto que se encuentre en un entorno de red. Una de las principales ventajas del esquema propuesto es la robustez del controlador inteligente para trabajar en entornos de red bajo canales de comunicación directa, así como a través de dos canales de comunicación diferentes, evidentemente entre más compleja sea la configuración del canal de comunicación, el desempeño del esquema propuesto puede deteriorarse.

Resumo: O geoide é a superfície equipotencial de referência altimétrica para levantamentos cartográficos e, portanto, apresenta diversas finalidades práticas para o engenheiro. Nas últimas décadas a comunidade geodésica tem concentrado esforços no desenvolvimento de modelos geoidais altamente acurados por meio de modernas técnicas. Esses modelos são fornecidos por meio de grades regulares das quais os usuários necessitam realizar interpolações. Todavia pouca informação pode ser obtida a respeito do método de interpolação mais adequado para extrair informação da malha regular de modelos geoidais. A utilização de um interpolador que não representa adequadamente a superfície geoidal pode prejudicar a qualidade das ondulações geoidais e consequentemente a conversão de altitudes. Este trabalho tem como objetivo quantificar a magnitude do erro proveniente da interpolação de malhas regulares de modelos geoidais. As análises consistiram na comparação entre os resultados obtidos com a interpolação do programa MAPGEO2015 e os interpoladores: bilinear, spline cúbica e redes neurais Radial Basis Function. De acordo com os experimentos realizados, concluiu-se que 2.5 cm dos 18 cm de erro obtido na validação do MAPGEO2015 é causado pelo uso dos interpoladores na grade 5'x5'.

Abstract: The geoid is an equipotential surface regarded as the altimetric reference for geodetic surveys and it therefore, has several practical applications for engineers. In recent decades the geodetic community has concentrated efforts on the development of highly accurate geoid models through modern techniques. These models are supplied through regular grids which users need to make interpolations. Yet, little information can be obtained regarding the most appropriate interpolation method to extract information from the regular grid of geoidal models. The use of an interpolator that does not represent the geoid surface appropriately can impair the quality of geoid undulations and consequently the height transformation. This work aims to quantify the magnitude of error that comes from a regular mesh of geoid models. The analysis consisted of performing a comparison between the interpolation of the MAPGEO2015 program and three interpolation methods: bilinear, cubic spline and neural networks Radial Basis Function. As a result of the experiments, it was concluded that 2.5 cm of the 18 cm error of the MAPGEO2015 validation is caused by the use of interpolations in the 5'x5' grid.

Determination of the ellipsoidal height by Global Navigation Satellite Systems (GNSS) is becoming better known and used for purposes of leveling with the aid of geoid models. However, the disadvantage of this method is the quality of the geoid models, which degrade heights and limit the application of the method. In order to provide better quality in transforming height using GNSS leveling, this research aims to develop a hybridization methodology of gravimetric geoid models EGM08, MAPGEO2015 and GEOIDSP2014 for the State of São Paulo, providing more consistent models with GNSS technology. Radial Basis Function (RBF) neural networks were used to obtain the corrector surface, based on differences between geoid model undulations and the undulations obtained by GNSS tracking in benchmarks. The experiments showed that the most suitable interpolation for correction modeling is the linear RBF. Checkpoints indicate that the geoid hybrid models feature root mean square deviation ± 0.107, ± 0.104 and ± 0.098 m, respectively. The results shows an improvement of 30 to 40% in consistencies compared with the gravimetric geoids, providing users with better quality in transformation of geometric to orthometric heights.

This work presents an artificial intelligence approach to solve the problem of finding a path and creating a map in unknown environments using Reinforcement Learning (RL) and Simultaneous Localization and Mapping (SLAM) for a differential mobile robot along with an optimal control system. We propose the integration of these approaches (two of the most widely used ones) for the implementation of robot navigation systems with an efficient method of control composed by a neural identifier and an inverse optimal control in order to obtain a robust and autonomous system of navigation in unknown and dynamic environments.