Comparación de estrategias de navegación colaborativa para robótica móvil

En el presente trabajo se comparan dos estrategias de navegación colaborativas, enmarcadas en el contexto de grupos de múltiples robots móviles, aplicados a la búsqueda de rutas óptimas entre un punto de partida y un punto de destino cuya ubicación es desconocida. Se describen las tendencias de la robótica móvil colaborativa y los enfoques de Inteligencia Artificial aplicados, haciendo énfasis en los sistemas de múltiples agentes y las metaheurísticas comúnmente usadas en este ámbito. Posteriormente se hace un análisis de las arquitecturas y se propone una arquitectura inspirada en un sistema nervioso biológico. Finalmente se hace un estudio estadístico que contrasta una estrategia tipo enjambre con una estrategia tipo multiagente, a partir de simulaciones de sistemas multi-robot, con el fin de determinar cuál de estas presenta mejor desempeño para ejecución de tareas colectivas, en ambientes simulados aplicadas a sistemas multi-robots.In the present work, two collaborative navigation strategies are compared, framed in the context of groups of multiple mobile robots, applied to the search for optimal routes between a starting point and a destination point whose location is unknown. The trends in collaborative mobile robotics and applied Artificial Intelligence approaches are described, with an emphasis on multi-agent systems and metaheuristics commonly used in this area. Subsequently, an analysis of the architectures is made and an architecture inspired by a biological nervous system is proposed. Finally, a statistical study is carried out that contrasts a swarm-type strategy with a multi-agent type strategy, based on simulations of multi-robot systems, in order to determine which of these presents better performance for the execution of collective tasks, in simulated environments applied to multi-robot systems

On Self Organising Cyberdynamic Policy

The de facto model of what it means to be effectively organised, hence cybernetically viable, is Stafford Beer’s Viable System Model (VSM). Many studies attest to the efficacy of what the VSM proposes, however, these appear to be largely confined to human based organisations of particular types e.g. businesses of assorted sizes and governmental matters. The original contribution to the body of knowledge that this work makes, in contrast, has come from an unconventional source i.e. football (soccer) teams. The unique opportunity identified was to use the vast amounts of football player spatial data, as captured by match scanning technology, to obtain simultaneously the multi-recursive policy characteristics of a real viable system operating in real time under highly dynamical load (threat/opportunity) conditions. It accomplishes this by considering player movement as being representative of the output of the policy function of the viable system model that they, hence their whole team, are each mapped to. As each player decides what they must do at any moment, or might need to do in the immediate future, this is set against their capabilities to deliver against that. This can be said of every player during every stage of any match. As such, their actions (their policies as viable systems) inform, and are informed by, the actions of others. This results in the teams of players behaving in a self-organising manner. Accordingly, in spatially varying player location, one has a single metric that characterises player, hence team function, and ultimately whole team policy as the policy of a viable system, that is amenable to analysis. A key behavioural characteristic of a self-organising system is a power law. Accordingly, by searching for, and obtaining, a power law associated with player movement one thereby obtains the output of the policy function of that whole team as a viable system, and hence the viable system model that the team maps to. At the heart of such activity is communication between the players as they proceed to do what they need to do at any given time during a match. This has offered another unique opportunity to measure the amount of spatially underpinned Information exhibited by the opposing teams in their entirety and to set those in juxtaposition with their respective power law characteristics and associated match outcomes. This meant that the power law characteristic that represents the policy of the viable system, and the amount of Information associated with that could be, and was, examined in the context of success or failure outcomes (as criteria of viability) to discern if some combinations of both were more profitable than not. This was accomplished in this work by using player position data from an anonymous member of the English Premier Football League playing in an unknown season to provide a quantitative analysis accordingly