This paper proposes swarm intelligence based approach for the real time coordination of groups of UAVs (Unmanned Aerial Vehicles) in tasks where values that are sensed from the aerial platform can be used to qualify the individuals. In particular, as an example application, here we consider environmental monitoring UAV teams. Their function is to monitor an area and when some undesired environmental condition arises, coordinate themselves to find the source as fast as possible. The swarm based algorithm has been extensively tested using a 3D simulation platform and validated with real UAVs flying over an industrial area.

This paper is devoted to a detailed presentation of the current state of the Multilevel Darwinist Brain (MDB) cognitive architecture for lifelong learning in real robots. This architecture follows the cognitive developmental robotics approach and it is based on concepts like embodiment, open-ended lifelong learning, autonomous knowledge acquisition or adaptive behaviors and motivations. In addition, this version of the MDB architecture incorporates several improvements related with more practical issues, which are the result of the experience gained through several experiments with real robots in the last few years. The MDB uses evolutionary algorithms in the knowledge acquisition process, which implies the need of paying attention to the efficiency of the computational implementation. Here, we first describe the cognitive model on which the basic operation of the architecture is based and, secondly, we detail the main aspects and working of the current version of the MDB. Finally, we have designed a very simple but illustrative real robot lifelong learning example, where we can show how to set up an experiment using the MDB. Hence, with this simple example we show the successful behavior of the MDB cognitive developmental robotics principles.

Human interaction environments (HIE) must be understood as any place where people carry out their daily life, including their work, family life, leisure and social life, interacting with technology to enhance or facilitate the experience. The integration of technology in these environments has been achieved in a disorderly and incompatible way, with devices operating in isolated islands with artificial edges delimited by the manufacturers. In this paper we are presenting the UniDA framework, an integral solution for the development of systems that require the integration and interoperation of devices and technologies in HIEs. It provides developers and installers with a uniform conceptual framework capable of modelling an HIE, together with a set of libraries, tools and devices to build distributed instrumentation networks with support for transparent integration of other technologies. A series of use case examples and a comparison to many of the existing technologies in the field has been included in order to show the benefits of using UniDA.

A framework based on a mixed reality simulator for coordinating teams of autonomous Unmanned Aerial Vehicles (UAVs) is been developed. This framework...