A Hierarchical Hybrid Architecture for Mission-Oriented Robot Control

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-03413-3_26In this work is presented a general architecture for a multi physical agent network system based on the coordination and the behaviour management. The system is organised in a hierarchical structure where are distinguished the individual agent actions and the collective ones linked to the whole agent network. Individual actions are also organised in a hybrid layered system that take advantages from reactive and deliberative control. Sensing system is involved as well in the behaviour architecture improving the information acquisition performance.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-C02-02, under coordinated project High Integrity Partitioned Embedded Systems (Hi-PartES): TIN2011-28567-C03-03, and under the collaborative research project supported by the European Union MultiPARTES Project: FP7-ICT 287702. 2011-14.Muñoz Alcobendas, M.; Munera Sánchez, E.; Blanes Noguera, F.; Simó Ten, JE. (2013). A Hierarchical Hybrid Architecture for Mission-Oriented Robot Control. En ROBOT2013: First Iberian Robotics Conference: Advances in Robotics, Vol. 1. Springer. 363-380. https://doi.org/10.1007/978-3-319-03413-3_26S363380Aragues, R.: Consistent data association in multi-robot systems with limited communications. 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Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work