Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2007.2.2 | Award Amount: 25.84M | Year: 2009
The European robotics industry plays a key role in maintaining our continents industrial base. The robotics industry is strong, but fragmented and dispersed. In the future, cutting-edge technology resulting from top-level research will be the decisive factor for success. Europe not only has a powerful robotics industry, but can also boast superb research. By drawing on these resources, ECHORD aims at producing new knowledge through advancing the state of the art in selected research foci and developing novel technology from which new products can be derived. Within ECHORD, opportunities for knowledge advancement and technology transfer between academia and industry will be created across the whole continent. This will be achieved through the solicitation of focused, small-size RTD projects, so-called experiments, which can be rapidly negotiated, funded and executed. Via these experiments, ECHORD will bring about a large-scale introduction of robotic equipment into research institutions. This is expected to result in both tangible and measurable out-comes in terms of the accelerated development of technologies, as well as the deployment of robotics technology into new scenarios for the direct application of research results. For ECHORD, three such scenarios have been defined: human-robot co-working, hyper flexible cells, and cognitive factories. The foremost purpose of the scenarios is to define an environment that is both scientifically challenging to research institutions and commercially relevant to robot manufacturers.
Agency: European Commission | Branch: H2020 | Program: IA | Phase: ICT-24-2015 | Award Amount: 4.77M | Year: 2016
Despite the high degree of industrial automation, robotic solutions are not yet prevalent in construction and demolition industry due to the complex environment and demanding requirements. For hazardous tasks, like the removal of asbestos contamination from a flat (rehabilitation site), manual performance is very inefficient, due to the intense safety measures. This leads to the main objective of Bots2ReC: Introducing, testing and validating an operational process for the automated removal of asbestos contamination at a real world rehabilitation site using a robotic system. After the necessary clearing from furniture and containment, the site is less complex and separated from human workers. In this environment safe automated robotic operations can be realized with current state of the art technology. The proposed robotic system will consist of multiple robotic units (lightweight robotic arms with abrasive tools and aspiration on a mobile platform) and a central process control system. Optical and radar sensors will allow environmental perception and navigation and local monitoring of the asbestos-removal, even in dusty conditions. The developed control system allows the user to specify and supervise automated tasks (e.g. disk grinding of contaminated paint from a wall), supported by a virtual representation of the site. The process and robotic system will be developed and enhanced in several iterations, supported by permanent testing on a test rehabilitation site benchmarked on a real life rehabilitation site at project end. Bots2ReC will have strong impacts to the robotics industry and society in Europe: It provides a step change towards a system prototype demonstrated in the operational environment of the demolition and construction industry and will strengthen the European robotic industry by later commercialisation of the system. Avoiding exposure to highly dangerous asbestos fibres will sustainably help protect the health of humans in Europe and worldwide.
Blatman G.,French Institute for Advanced Mechanics |
Blatman G.,Électricité de France |
Sudret B.,French Institute for Advanced Mechanics |
Sudret B.,Center dAffaires du Zenith
Journal of Computational Physics | Year: 2011
Polynomial chaos (PC) expansions are used in stochastic finite element analysis to represent the random model response by a set of coefficients in a suitable (so-called polynomial chaos) basis. The number of terms to be computed grows dramatically with the size of the input random vector, which makes the computational cost of classical solution schemes (may it be intrusive (i.e. of Galerkin type) or non intrusive) unaffordable when the deterministic finite element model is expensive to evaluate.To address such problems, the paper describes a non intrusive method that builds a sparse PC expansion. First, an original strategy for truncating the PC expansions, based on hyperbolic index sets, is proposed. Then an adaptive algorithm based on least angle regression (LAR) is devised for automatically detecting the significant coefficients of the PC expansion. Beside the sparsity of the basis, the experimental design used at each step of the algorithm is systematically complemented in order to avoid the overfitting phenomenon. The accuracy of the PC metamodel is checked using an estimate inspired by statistical learning theory, namely the corrected leave-one-out error. As a consequence, a rather small number of PC terms are eventually retained (sparse representation), which may be obtained at a reduced computational cost compared to the classical " full" PC approximation. The convergence of the algorithm is shown on an analytical function. Then the method is illustrated on three stochastic finite element problems. The first model features 10 input random variables, whereas the two others involve an input random field, which is discretized into 38 and 30 - 500 random variables, respectively. © 2010 Elsevier Inc.