Gervasio Varela

Swarm intelligence based approach for real time UAV team coordination in search operations

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.

UniDA: Uniform Device Access Framework for Human Interaction Environments

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.

Autonomous UAV based search operations using Constrained Sampling Evolutionary Algorithms

This paper introduces and studies the application of Constrained Sampling Evolutionary Algorithms in the framework of an UAV based search and rescue scenario. These algorithms have been developed as a way to harness the power of Evolutionary Algorithms (EA) when operating in complex, noisy, multimodal optimization problems and transfer the advantages of their approach to real time real world problems that can be transformed into search and optimization challenges. These types of problems are denoted as Constrained Sampling problems and are characterized by the fact that the physical limitations of reality do not allow for an instantaneous determination of the fitness of the points present in the population that must be evolved. A general approach to address these problems is presented and a particular implementation using Differential Evolution as an example of CS-EA is created and evaluated using teams of UAVs in search and rescue missions. The results are compared to those of a Swarm Intelligence based strategy in the same type of problem as this approach has been widely used within the UAV path planning field in different variants by many authors.

Autonomous adaptation of user interfaces to support mobility in ambient intelligence systems

The work presented in this paper is focused on building Ambient Intelligence (AmI) applications capable of moving from one environment to another, while their user interface keeps adapting itself, autonomously, to the variable environment conditions and the available interaction resources. AmI applications are expected to interact with users naturally and transparently, therefore, most of their interaction relies on embedded devices that obtain information from the user and environment. This work implements a framework for AmI systems that elevates those embedded devices to the class of interaction resources. It does so by providing a new level of abstraction that decouples applications, conceptually and physically, from the different specific interaction resources available and their underlying heterogeneous technologies. In order to drive the adaptation process to environment changes, the system makes use of a set of models that describe the user, environment conditions and devices, and algorithms for context-aware selection of the interaction devices.

A cognitive developmental robotics architecture for lifelong learning by evolution in real robots

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.

The Robobo Project: Bringing Educational Robotics Closer to Real-World Applications

The Robobo Project is a STEM-based project that aims to bring educational robotics, in primary and high school, closer to real-world applications. It is based on the use of a smartphone-based robotic platform called Robobo, a very flexible programming environment, and a set of lessons to integrate them. The smartphone provides high-level hardware capabilities in terms of sensors, communications and processing capabilities that allow to create more practical and realistic lessons that exploit human-robot interaction, with a small investment. In this paper, we present the main elements of The Robobo Project in terms of hardware and software, and two illustrative educational projects that can be developed within it.

DAAF: A Device Abstraction and Aggregation Framework for Smart Environments

Largely motivated by the ubiquitous computing vision, computers have been steadily becoming integrated in peoples life, expanding the ability of software applications to interact with the environment and the users. This has led to the creation of smart environments that support people in their daily life by exploiting information acquired using sensor networks. These systems are usually populated by multiple non-interoperable technologies that make the development, deployment and reutilization of applications a complex and repetitive task. This paper presents DAAF, a Device Abstraction and Aggregation Framework that provides a common conceptual model of a device network, and a set of tools, which allow a flexible integration of heterogeneous sensor networks, enabling fast development and reconfiguration of smart environment applications.

Some Issues and Extensions of JADE to Cope with Multi-agent Operation in the Context of Ambient Intelligence

Ambient Intelligence (AmI) can be taken as one of the principal test beds for the integration of Systems, Humans and Cybernetics. Multi-agent based Ambient Intelligence solutions have proliferated in the last few years in order to cope with the intrinsically distributed and complex interaction based nature of the problem. Different multi-agent platforms have been used in this regard, being JADE one of the standard solutions adopted due the variety of services it provides and to its FIPA compliance. Nevertheless, JADE was designed as a general Multi-agent platform and not for the particular demands of AmI systems. Consequently, its behavior in this realm is not as optimal as it could be. In this paper, we analyze some of the problems JADE presents for its application to AmI, especially in terms of communications, and describe some of the extensions we have developed in order to solve them. The resulting system exhibits enhanced communication capabilities, promotes the division of tasks into decoupled components and makes component reutilization easy. This platform constitutes an environment with tools for the development and deployment of AmI applications.

Improving the performance of evolutionary algorithms by soft-constraining their sampling capabilities

In this paper we argue that to produce good optimization performances, the exploration of the solution space does not need to be carried out in the unorderly fashion most evolutionary algorithms use. Other strategies that seek to minimize the cost involved in successive evaluation processes should be explored. This does not imply a fundamental change on how evolutionary algorithms work, but rather, it brings some structure onto how solution spaces are explored by contemplating decoding cost as one of the elements to be minimized when sampling. The traditional implementations of most evolutionary algorithms assume that any point in the solution space can be evaluated any time and at no cost. However, this is not always the case and often each step of the process only part of the solution space is available for evaluation giving rise to a class of problems we have called Constrained Sampling optimization problems over which evolutionary algorithms are quite inefficient. To address these problems we have proposed a modification of the general strategy of evolutionary algorithms to address these constraints efficiently. Here, we study the effects of this approach when applied to problems that are not constrained, thus modifying the way the solution space is explored. This study is carried out to determine how these modification impact the performance of a set of popular evolutionary algorithms over a representative set of benchmark functions corresponding to fitness landscapes with a variety of characteristics. We show that by restricting the sampling capabilities of most algorithms, the cost of the optimization procedure is reduced for most types of fitness landscapes without affecting their results.

Differential Evolution in Constrained Sampling Problems

This work proposes a set of modifications to the Differential Evolution algorithm in order to make it more efficient in solving a particular category of problems, the so called Constrained Sampling problems. In this type of problems, which are usually related to the on-line real-world application of evolution, it is not always straightforward to evaluate the fitness landscapes due to the computational cost it implies or to physical constraints of the specific application. The fact is that the sampling or evaluation of the offspring points within the fitness landscape generally requires a decoding phase that implies physical changes over the parents or elements used for sampling the landscape, whether through some type of physical migration from their locations or through changes in their configurations. Here we propose a series of modifications to the Differential Evolution algorithm in order to improve its efficiency when applied to this type of problems. The approach is compared to a standard DE using some common real-coded benchmark functions and then it is applied to a real constrained sampling problem through a series of real experiments where a set of Unmanned Aerials Vehicles is used to find shipwrecked people.