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Localization is the challenge of determining the robot's pose in a mapped environment. This is done by implementing a probabilistic algorithm to filter noisy sensor measurements and track the robot's position and orientation. This paper focuses on localizing a robot in a known mapped environment using Adaptive Monte Carlo Localization or Particle Filters method and send it to a goal state. ROS, Gazebo and RViz were used as the tools of the trade to simulate the environment and programming two robots for performing localization.

To accommodate future demands in air traffic management, this article qualitatively elaborates the multi-aircraft conflict resolution relying on the concept of an airborne ecosystem, as a set of autonomously operating aircraft whose trajectories are causally involved in a tactically detected conflict. The methodology provides two types of solutions: Air Traffic Control-based resolution that is considered as one from a set of compulsory avoidance maneuvers at a certain time instance, and the multi-agent simulated resolution as a product of the aircraft negotiation interactions and agreement on the avoidance maneuvers for the conflict state removal. The article further analyses a flight efficiency of the ecosystem resolution, in both distance and time, by comparing the compulsory against the negotiated solutions. From the total amount of tested trajectories and identified conflict patterns, three ecosystem scenarios have been randomly selected and efficiently quantified. Finally, the results have shown the significant savings in favor of the multi-agent solution approach.

Dhatrik Nikhil Kumar's CV

Shivam Singh's CV

En la actualidad, el estudio y análisis de los sistemas de control automático representan un campo de aplicación bastante extendido, esto debido a las imperantes necesidades por mejorar los procesos productivos, optimizar los recursos e incluso para predecir comportamientos, desde entornos educativos, investigativos o industriales, hasta entornos económicos, sociales y demás. En efecto, esto ha impulsado a los desarrolladores a buscar herramientas o alternativas que permitan aumentar las capacidades de cómputo al momento de desarrollar un sistema de control automático. Ahora bien, la instrumentación industrial desde hace muchos años viene comandada por los Autómatas Programables (PLC), dispositivos útiles e ideales para ejecutar las tareas de control de una planta, independientemente de la escala. El presente documento presenta el diseño e implementación de un controlador PID en una planta didáctica conformada con equipos industriales, haciendo uso de OPC y algunos conceptos de la teoría de control. Mediante la herramienta computacional MATLAB se realiza la identificación de la planta obteniendo su función de transferencia, y haciendo uso de algunas herramientas disponibles en este software, se determinan los parámetros de sintonía de un controlador tipo PID, para posteriormente sintonizar la estrategia de control implementada en un autómata programable de gama alta.

\(y=\frac{c}{x}\)

I have shown here how to derive the summation of a convergent Arithmetic series and get two results as answers

Shane Thirkell's CV

«Multiport conversions between S, Z, Y, h, ABCD, and T parameters.» Integrated Nonlinear Microwave and Millimetre-wave Circuits (INMMIC 2018), Brive-la-gaillarde, France, July 2018. Article: http://www.microwave.fr/publications/151.pdf Poster: http://www.microwave.fr/publications/151p.pdf