Daniel Souto

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...

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.

Hyperspectral image segmentation through evolved cellular automata

Segmenting multidimensional images, in particular hyperspectral images, is still an open subject. Two are the most important issues in this field. On one hand, most methods do not preserve the multidimensional character of the signals throughout the segmentation process. They usually perform an early projection of the hyperspectral information to a two dimensional representation with the consequent loss of the large amount of spectral information these images provide. On the other hand, there is usually very little and dubious ground truth available, making it very hard to train and tune appropriate segmentation and classification strategies. This paper describes an approach to the problem of segmenting and classifying regions in multidimensional images that performs a joint two-step process. The first step is based on the application of cellular automata (CA) and their emergent behavior over the hyperspectral cube in order to produce homogeneous regions. The second step employs a more traditional SVM in order to provide labels for these regions to classify them. The use of cellular automata for segmentation in hyperspectral images is not new, but most approaches to this problem involve hand designing the rules for the automata and, in general, average out the spectral information present. The main contribution of this paper is the study of the application of evolutionary methods to produce the CA rule sets that result in the best possible segmentation properties under different circumstances without resorting to any form of projection until the information is presented to the user. In addition, we show that the evolution process we propose to obtain the rules can be carried out over RGB images and then the resulting automata can be used to process multidimensional hyperspectral images successfully, thus avoiding the problem of lack of appropriately labeled ground truth images. The procedure has been tested over synthetic and real hyperspectral images and the results are very competitive.

An evolution friendly modular architecture to produce feasible robots

This paper proposes the use of a modular robotic architecture in order to produce feasible robots through evolution. To this end, the main requirements the architecture must fulfill are analyzed and a top-down methodology is employed to obtain the different types of modules that make it up. Specifically, the problem of how to increase the evolvability or evolution friendliness of the system is addressed by considering a heterogeneous modular architecture with a large number of connection faces per module. Afterwards, a prototypical implementation of these modules with the required features is described and different experiments provide an indication of how versatile the architecture is for evolving robot morphologies and control for specific tasks and how easy it is to build them. We present a modular architecture to produce feasible robots through evolution.The architecture is based on a set of a heterogeneous modules.The modules contain a large number of connection faces per module.The design and the implementation of prototype modules is described in detail.Different experiments show its potential for evolving robot morphologies and control.

Towards an evolutionary design of modular robots for industry

We are interested in the next generation of industrial robots, those that are able to operate in dynamic and unstructured environments and, consequently, that are able to adapt to changing circumstances or to work on different tasks in an autonomous way. In this sense, multirobot systems and, in particular, modular systems present several features like scalability, fault tolerance, low maintenance or reconfiguration capabilities that make them highly suitable for this kind of environments. The work presented here is concerned with the problem of automatically obtaining the morphology and control structure for this type of modular systems. In this line, we present the first results produced using a newly designed constructive evolutionary approach that takes into account the extreme difficulty of the tremendously deceptive and uninformative search space this type of applications are faced with. As an example, the algorithm is used to design the morphology and the distributed control parameters for a typical benchmark problem, that of moving as far as possible in a straight line, for a heterogeneous modular robotic system developed by our group.

Morphologically intelligent underactuated robot for underwater hull cleaning

In this paper we discuss a new type of robot for underwater hull cleaning on ships with non-magnetic hulls. This robot is based on the concept that cleaning hulls regularly, without waiting to take them out of the water, will improve the efficiency of the ships and will permit a reduction in the use of the chemicals that are usually employed to prevent the growth of marine life on the hull and which are generally harmful to the environment. The robot described in this paper is an underactuated morphologically adapted robot that through an appropriate morphology and making use of the forces and constraints of the environment solves the most difficult problems that arise when moving along hulls. Some of these are changing planes, negotiating appendices, avoiding portholes, passing corners, and other elements. This greatly simplifies the control mechanisms that are required for its operation making it an ideal candidate for completely autonomous operation. A description of the design of the robot as well as a series of examples of its operation are provided.

Addressing the training problem in cellular automata based hyperspectral image segmentation

Two important issues are still open within the field of hyperspectral image segmentation. On one hand, most methods usually perform an early projection of the hyperspectral information onto a less informative two dimensional representation. On the other hand, there is usually very little and dubious ground truth available, making it very hard to train and tune appropriate segmentation and classification strategies. This paper describes an approach to address these problems by considering the application of evolved cellular automata (CA) over the hyperspectral cube in order to produce homogeneous regions that allow to easily perform the segmentation task. This homogenization process is carried out without resorting to any form of projection while the CA is operating, thus preserving this way the spectral character ofthe information in the segmentation process. We also show that the evolution process we propose for obtaining the rules can be carried out over RGB images and the resulting automata used to process multidimensional hyperspectral images successfully, thus avoiding the problem of lack of appropriately labeled ground truth images. The procedure has been tested over synthetic and real hyperspectral images and the results are very competitive.

Designing a modular robotic architecture for industrial applications

This paper considers the issue of increasing the number of robots working in sectors characterized by dynamic and unstructured environments. Specifically, the paper deals with a new approach, based on modular robotics, to allow the fast deployment of robots to solve specific tasks. Some authors have proposed modular architectures, mostly in laboratory settings, but their design was usually based and what could be built instead of what was necessary for industrial operations. Here we consider the problem by defining the industrial settings the architecture is aimed at and extract the main features that would be required from a modular robotic architecture to operate successfully in these kinds of environments. These requirements are then taken into account to design a particular heterogeneous modular robotic architecture and a laboratory implementation of it is built in order to test its capabilities and show its versatility using a set of different configurations including manipulators, climbers and walkers.

Time in hyperspectral processing: A temporal based classification approach

This paper deals with the problem of classifying processes using the temporal information in the sequence of hyperspectral images that are obtained as they take place. That is, taking into account the temporal evolution of the process in the discrimination that must be made. To this end we have considered a particular type of artificial neural networks with trainable delays in their synapses. The classification scheme is studied and applied to the case of resin curing processes. Several test cases involving different proportions of resin components as well as certain environmental conditions such as humidity were created and the system was tested over them producing very promising results.