Abraham Prieto

A comparison of gaussian based ANNs for the classification of multidimensional hyperspectral signals

This paper is concerned with the comparison of three types of Gaussian based Artificial Neural Networks in the very high dimensionality classification problems found in hyperspectral signal processing. In particular, they have been compared for the spectral unmixing problem given the fact that the requirements for this type of classification are very different from other realms in two aspects: there are usually very few training samples leading to networks that are very easily overtrained, and these samples are not usually representative in terms of sampling the whole input-output space. The networks selected for comparison go from the classical Radial Basis Function (RBF) network to the more complex Gaussian Synapse Based Network (GSBN) considering an intermediate type, the Radial Basis Function with Multiple Deviation (RBFMD). The comparisons were carried out when processing a benchmark set of synthetic hyperspectral images containing mixtures of spectra from materials found in the US Geological Service database.

A Complex Systems Based Tool for Collective Robot Behavior Emergence and Analysis

This paper presents a Complex Systems Theory based methodology and tool for the automatic design of multiagent or multirobot collective behaviors for the optimized execution of a given task. The main goal of this methodology is the representation of a generic task to be optimally performed in a Complex Systems simulator called WASPBED and the subsequent analysis of the emergent states thus obtained. This way, by tweaking environmental parameters in the system, the behaviors of the different collective behaviors obtained can be studied. The example used to test the methodology deals with collective behaviors for optimized routing in unknown environments.

Adaptively Coordinating Heterogeneous Robot Teams through Asynchronous Situated Coevolution

Adapting to changing situations and objectives and selforganazing without a central controller in order to achieve an objective has become one of the main challenges in the design and operation of multirobot systems. The Asynchronous Situated Coevolution (ASiCO) algorithm has been successfully applied in surveillance tasks defined by just one global objective. In this paper we present the results obtained with ASiCO in more complex multirobot problems with several objectives that require a heterogeneous population of robot controllers that autonomously distribute the tasks. The paper focuses on the benefits of evolving an affinity coefficient that characterizes the individual genotypes.

Solving a heterogeneous fleet vehicle routing problem with time windows through the asynchronous situated Coevolution algorithm

In this work we present the practical application of the Asynchronous Situated Coevolution (ASiCo) algorithm to a special type of vehicle routing problem, the heterogeneous fleet vehicle routing problem with time windows (HVRPTW). It consists in simultaneously determining the composition and the routing of a fleet of heterogeneous vehicles in order to serve a set of time-constrained delivery demands. The ASiCo algorithm performs a situated coevolution process inspired on those typical of the Artificial Life field that has been improved with a strategy to guide the evolution towards a design objective. This strategy is based on the principled evaluation function selection for evolving coordinated multirobot systems developed by Agogino and Tumer. ASiCo has been designed to solve dynamic, distributed and combinatorial optimization problems in a completely decentralized way, resulting in an alternative approach to be applied to several engineering optimization domains where current algorithms perform unsatisfactorily.

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.

Spatio-temporal cellular automata-based filtering for image sequence denoising: Application to fluoroscopic sequences

This work presents a novel spatio-temporal cellular automata-based filtering (STCAF) for image sequence denoising. Most of the methods using cellular automata (CA) for image denoising involve the manual design of the rules that define the behaviour of the automata. This is a complex and not straightforward operation. In order to tackle this problem, this paper proposes to use evolutionary methods to obtain the CA set of rules which produces the best possible denoising under different noise models or/and image sources. This is implemented using a spatio-temporal neighbourhood for each pixel, which significantly improves the results with respect to simple spatio or temporal set of neighbours. The proposed method is tested to reduce the noise in low-dose X-ray image sequences. These data have a severe signal-dependent noise that must be reduced avoiding artifacts while preserving structures of interest for a medical inspection. The proposed method outperforms several state-of-the-art algorithms on both simulated and real sequences.

Asynchronous Situated Coevolution and Embryonic Reproduction as a Means to Autonomously Coordinate Robot Teams

One of the main challenges in the operation of multirobot systems is to find ways for them to adapt to changing situations and even objectives without any type of central control. In this work we propose a real time coevolutionary strategy based on Embodied Evolution (EE) approaches that provides a means to achieve this end. The main inspiration for this approach comes from the field of artificial life combined with some of the notions on the distribution of utility functions as proposed by the multiagent systems literature. The solution has been tested on different real life problems involving robot teams. In particular, in this paper the work is aimed at the coordination of sets of robots for performing monitoring and surveillance operations such as the ones required on ship tanks and hulls. Nevertheless, the approach is general enough to be applied to many other tasks in several fields.

An Adaptive Visual Gesture Based Interface for Human Machine Interaction in Intelligent Workspaces

This paper describes the implementation of an artificial neural network based adaptive hand positioning and gesture recognition system for multimodal interfaces in intelligent workspaces. The system is based on a stereo vision system and provides a series of mechanisms for an efficient segmentation of the arm-hand structure over the screen and its description in terms of moments that can be used by a neural network based structure in order to provide position and gesture classification results. These can be adapted to the users desires through an interaction and feedback mechanism permitting a natural mutual learning process between the system and the human

Spatio-temporal cellular automata-based filtering for image sequence denoising

This work describes a novel spatio-temporal cellular automata-based filtering algorithm (st-CAF) intended for performing image sequence denoising processes. The approach presents several advantages over more traditional single frame denoising techniques presented in the literature or even over their adaptation to sequences. Especially the fact that the cellular automaton used is able to contemplate information concerning the type of noise through the use of specific sequences to tune the algorithm, as well as temporal information by means of a spatio-temporal neighborhood when processing each pixel of the sequence. These two elements lead to significant improvements in the results with respect to simple spatial or temporal sets of neighbors.

Evolutionary Tool for the Incremental Design of Controllers for Collective Behaviors

In this paper we present a software tool for the automatic design of collective behaviors in animated feature films. The most successful existing commercial solutions used in animation studios require an explicit knowledge by the designer of the AI or other techniques and involve the hand design of many parameters. Our main motivation consists in developing a design tool that permits creating the behaviors of the characters from a high level perspective, using general concepts related to the final desired objectives, and to judge these behaviors from a visual point of view, thus abstracting the designer from the computational techniques in the system core. In this case, a bioinspired approach has been followed consisting in the incremental generation of controllers for simulated agents using evolution. An example of flocking activity is created with the system.