Fernando López-Peña

Urban pollution monitoring through opportunistic mobile sensor networks based on public transport

The development of an opportunistic sensor network deployed on regular public transport vehicles with the aim of obtaining a flexible pollution monitoring system over large urban areas is presented. Georeferenced pollution data is acquired by a modular autonomous sensing system placed on vehicles which has been developed and is being currently tested. Short and long range communication systems are used to transmit data from the mobile sources to the central data processing and mapping unit. Within this unit an application to represent the geopositioned pollutant measurements has been implemented based on Google Earth. This provides the user with an interface allowing the study of the evolution of the gas concentrations along a given bus route as well as on the whole urban area.

Human motion tracking and gait analysis: a brief review of current sensing systems and integration with intelligent environments

Human motion tracking and gait analysis is becoming a topic of great interest in everyday applications generating a large amount of research into systems that can provide this information in real time at a low cost and with the smallest intrusion level possible. In this line different approaches have been proposed following distinct philosophies usually related to the final application of the system. This paper is aimed on one hand at providing a review of non vision based current systems highlighting how distinct and oriented towards particular applications they are. On the other this paper deals with the problem of integrating such different elements into efficient environmental intelligence applications that can be used as a base for home rehabilitation or automatic elderly assistance. This is addressed by implementing a specific device access layer within a novel distributed agent based ambient intelligence architecture called HI3.

Development of a climbing robot for grit blasting operations in shipyards

This paper deals with the design and construction of a climbing robot for performing grit blasting operations in shipyards. The robot is based on a double sliding platform that uses permanent magnets for attachment. It is lightweight and compact and can move up and along the shipside with any inclination while grit blasting the surface to pre-specified surface quality levels. It can also rotate to compensate for hull curvature and to avoid obstacles while performing its task. The blasting operation is modulated by a vision based quality control system that is used by the mission control system to adapt the blasting parameters in order to attain the desired quality levels while maximizing the surface area the robot strips per unit time.

A Robot for the Unsupervised Grit-Blasting of Ship Hulls

This paper describes the design and the control architecture of an unsupervised robot developed for grit blasting ship hulls in shipyards. Grit blasting is a very common and environmentally unfriendly operation, required for preparing metallic surfaces for painting operations. It also implies very unhealthy and hazardous working conditions for the operators that must carry it out. The robot presented here has been designed to reduce the environmental impact of these operations and completely eliminate the health associated risks for the operators. It is based on a double frame main body with magnetic legs that are able to avoid the accumulation of ferromagnetic dust during its operation. The control system presents a layered structure with four layers that are physically distributed into two separate components in order to facilitate different operational modes as well as to increase the safety requirements of the system. A low-level control component has been implemented on the robotic unit itself, and a...

EDHMoR: Evolutionary designer of heterogeneous modular robots

This paper is devoted to the problem of automatically designing feasible and manufacturable robots made up of heterogeneous modules. Specifically, the coevolution of morphology and control in robots is analyzed and a particular strategy to address this problem is contemplated. To this end, the main issues of this approach such as encoding, evaluation or transfer to reality are studied through the use of heterogeneous modular structures with distributed control. We also propose a constructive evolutionary algorithm based on tree-like representations of the morphology that can intrinsically provide for a type of generative evolutionary approach. The algorithm introduces some new elements to smooth the search space and make finding solutions much easier. The evaluation of the individuals is carried out in simulations and then transferred to real robots assembled from the modules considered. To this end, the extension of the principles proposed by classical authors in traditional evolutionary robotics to brain-body evolution regarding how simulations should be set up so that robust behaviors that can be transferred to reality are obtained is considered here. All these issues are analyzed by means of an evolutionary design system called EDHMoR (Evolutionary Designer of Heterogeneous Modular Robots) that contains all the elements involved in this process. To show practical evidences of the conclusions that have been extracted with this work, two benchmark problems in modular robotics are considered and EDHMoR is tested over them. The first one is focused on solving a linear robot motion mission and the second one on a static task of the robot that does not require displacements.

An Adaptive Approach for the Progressive Integration of Spatial and Spectral Features When Training Ground-Based Hyperspectral Imaging Classifiers

The use of hyperspectrometers as analytical tools for determining surface material properties in ground-based applications introduces the need of integrating spatial and spectral hyperspectral cube components. A neural-network-based approach is presented in this paper with the aim of automatically adapting to the spatiospectral characteristics of samples in a problem domain so that the most efficient classification can be obtained. Its main application would be in inspection and quality control tasks. The system core is an Artificial Neural Network-based hyperspectral processing unit able to perform the online classification of the material based on the spatiospectral patterns provided by a set of pixels. A training adviser is implemented to automate the determination of the minimum spatial window size, as well as the optimum spectrospatial feature set leading to the desired classification in terms of the available ground truth. Several tests have been carried out on synthetic and real data sets. In particular, the proposed approach is used to discriminate samples of synthetic and real materials, where the pixel resolution implies that a material is characterized by spectral patterns of combinations of pixels.

Lappa: A new type of robot for underwater non-magnetic and complex hull cleaning

This paper is concerned with the design and implementation of a new concept of robot to clean the underwater sections of ship hulls without using any magnetic attachment. The use of this type of robots on a regular basis to preserve a clean hull, usually when ships are in port or anchored, will improve the efficiency of the ships and will permit a reduction in the use of chemicals that are harmful to the environment to prevent the growth of marine life on the hull. The main contribution of the robot described in this paper is that it is a completely novel design that through an appropriate morphology solves the problems that arise when moving along hulls, including changing planes, negotiating appendices, portholes, corners, and other elements. It thus provides a basis for completely autonomous operation. The design and implementation of the robot is described and some simulations and tests in real environments are presented.

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.

An integrated system for urban pollution monitoring through a public transportation based opportunistic mobile sensor network

The objective of this paper is to report on new developments in the project we are working on for the development of a mobile sensor based opportunistic urban pollution monitoring network. This work follows from the implementation of a single pollution sensor based sensing node prototype which was used for testing an opportunistic communications network and which was reported elsewhere. Here we concentrate on the extension of the basic sensing system and its modular conversion into a multi pollutant sensing system able to additionally provide temperature, humidity and geo-position information as well as on the software architecture developed around it in order to process the huge amounts of data the system produces. The different prototypes were tested on the public transportation system of the city of Vigo and on multiple test runs around the city of A Coruña in the north-west of Spain producing very promising results.

A Hyperspectral Based Multisensor System for Marine Oil Spill Detection, Analysis and Tracking

In this work we present a new multisensor system which is being currently designed and developed within the framework of a research project founded by the Spanish Ministry of Science and Technology. This system is designed to address the detection of oil, its characterization, and the tracking of oil spills on seawater by means of hyperspectral image segmentation. The system core is an instrument platform combining a hyperspectrometer with a series of positioning sensors for the determination of its geographical position and orientation. The system is completed with GIS and hyperspectral image analysis subsystems.