Diseño e implementación software de un robot humanoide.

[ES]Este documento tiene la intención de presentar un Trabajo de Fin de Grado (TFG). Este proyecto consiste en una serie de herramientas que permitan el diseño, implementación y desarrollo del software de control de un robot humanoide. El proyecto se centra en la mejora de la efectividad, robustez, rendimiento y fiabilidad del software. Los cambios propuestos introducen mejoras sobre el robot comercial robo nova. En concreto la capacidad de ser modular, permitiendo de esta forma el uso total o parcial de las soluciones escogidas, ahorrando tiempo y dinero en futuros desarrollos de esta plataforma

BODIPY koloratzaile berrien karakterizazio fotofisikoa eta egitura-simulazioa, aplikazio fotonikoetarako

133 p. + anexos

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.