The overall objectives of this project are to study the perceptual, representational, reasoning and learning capabilities of embodied robots in human centred environments. The project will develop methods and technologies for the construction of such cognitive robots able to evolve and grow their capacities in close interaction with humans in an open-ended fashion. Expected results are basic methods, algorithms and architectures and their integration and long-term experimentation and scientific evaluation on embodied robotic systems in different settings and situations.
In the focus of this research endeavour is the development of a robot whose ultimate task is to serve humans as a companion in their daily life. The robot is not only considered as a ready-made device but as an artificial creature, which improves its capabilities in a continuous process of acquiring new knowledge and skills. Besides the necessary functions for sensing, moving and acting, such a robot will exhibit the cognitive capacities enabling it to focus its attention, to understand the spatial and dynamic structure of its environment and to interact with it, to exhibit a social behaviour and communicate with other agents and with humans at the appropriate level of abstraction according to context.
The design of the cognitive functions of this artificial creature and the study and development of the continuous learning, training and education process in the course of which it will mature to a true companion, are the central research themes of the proposed project.
The operational complexity and performance requirements of modern racing yachts demand the use of advanced applications, such as a decision support system (DSS) able to assist crew members during navigation. In this article, the authors describe a near-time computational solver as the main piece of a DSS which analyses and monitors the behaviour of sails and rigging. The solver is made up of two different interconnected tools: an iterative Fluid-Structure Interaction algorithm and an advanced Wireless Sensor Network to monitor rigging. The real-time DSS quantifies crew manoeuvres in physical terms, which are reproduced by a simulation program. It can be used in the design phase of sailing yachts and as an aid for real-time boat performance optimisation and accident prevention. This novel DSS is a useful tool for navigation, especially in races.
Garcia, J.; Ortigosa, I.; Fernandez, A. Revista internacional de métodos numéricos para cálculo y diseño en ingeniería Vol. 31, num. 3, p. 146-153 DOI: 10.1016/j.rimni.2014.04.002 Date of publication: 2015-01-01 Journal article
In this paper, the conception and design of a new monitoring system for a racing yachts rig is presented. The sensors developed are able to process the measured strain data, by applying artificial neural networks (ANN) algorithms, and then evaluate the load acting on an element and identify the direction of the action of that force. This way, it is possible to identify the actual operating conditions of the yacht rig. The required data for ANN training is generated from the results obtained from different finite element method (FEM) computational models of the device. Furthermore, during the design phase of the system, different experimental campaigns were carried out. The experimental tests were designed to serve as proof of concept, as well as to validate the different procedures used in the system development and application.; The developed monitoring system is wireless, low-intrusive and easily adaptable to any yacht configuration. This work also presents the integration of the monitoring system into a coupled fluid-structure computation model for the sails and rig of a boat. The resulting system is an efficient tool for evaluating performance and decision support in the adjustment of a sailboat rig. (C) 2014 CIMNE (Universitat Politecnica de Catalunya). Published by Elsevier Espana, S.L.U. All rights reserved.
The Holtrop & Mennen method is widely used at the initial design stage of ships for estimating the resistance of the ship (Holtrop and Mennen, 1982). The Holtrop & Mennen method provide a prediction of the total resistance’s components. In this work we present a neural network model which performs the same task as the Holtrop & Mennem’s method, for two of the total resistance’s components. A multilayer perceptron has been therefore trained to learn the relationship between the input (length-displacement ratio, prismatic coefficient, longitudinal position of the centre of buoyancy, after body form and Froude number) and the target variables (form factor and wave-making and wave-breaking resistance per unit weight of displacement). The network architecture with best generalization properties was obtained through an exhaustive validation analysis (Bishop, 1995). The results of this model have been compared against those provided by the Holtrop & Mennen method, and it was found that the quality of the prediction is improved over the entire range of data. The neural network provides an accurate estimation of two total resistance’s components with Froude number and hull geometry coefficients as variables.
The model presented in this paper is part of the development and integration of a System to Support Decision (SAD) for assistance in the design and actuation of sails. The model is based on the union of a wireless sensor system, continuously connected to a calculation/simulation system. This system will predict the structural behavior and
performance of the different configurations of the rigging of a sailing ship.
The simulation software will be communicated in real time with wireless sensors and
interfaces through suitable filters. The information gathered by the sensors, which are integrated into the structure of the boat, will be used in numerical algorithms to determine the efforts that are suffering the Structure and evaluate the overall performance of the sailboat.
The system of calculation / simulation is made up of a model of structural calculation based on a quasi-static method with a formulation for large displacement typical of the finite element method, and includes models of membranes, cables and bars. And the starting point of the simulation tool of fluid dynamics is the method of vortices (contour elements). The algorithms are adapted so that they can make real-time data that offers the monitoring system. The software also has algorithms simulation maneuver with which you can change the
position of the sails.