Contact aware robust semi-autonomous teleoperation of mobile manipulators

In the context of human-robot collaboration, cooperation and teaming, the use of mobile manipulators is widespread on applications involving unpredictable or hazardous environments for humans operators, like space operations, waste management and search and rescue on disaster scenarios. Applications where the manipulator's motion is controlled remotely by specialized operators. Teleoperation of manipulators is not a straightforward task, and in many practical cases represent a common source of failures. Common issues during the remote control of manipulators are: increasing control complexity with respect the mechanical degrees of freedom; inadequate or incomplete feedback to the user (i.e. limited visualization or knowledge of the environment); predefined motion directives may be incompatible with constraints or obstacles imposed by the environment. In the latter case, part of the manipulator may get trapped or blocked by some obstacle in the environment, failure that cannot be easily detected, isolated nor counteracted remotely. While control complexity can be reduced by the introduction of motion directives or by abstraction of the robot motion, the real-time constraint of the teleoperation task requires the transfer of the least possible amount of data over the system's network, thus limiting the number of physical sensors that can be used to model the environment. Therefore, it is of fundamental to define alternative perceptive strategies to accurately characterize different interaction with the environment without relying on specific sensory technologies. In this work, we present a novel approach for safe teleoperation, that takes advantage of model based proprioceptive measurement of the robot dynamics to robustly identify unexpected collisions or contact events with the environment. Each identified collision is translated on-the-fly into a set of local motion constraints, allowing the exploitation of the system redundancies for the computation of intelligent control laws for automatic reaction, without requiring human intervention and minimizing the disturbance of the task execution (or, equivalently, the operator efforts). More precisely, the described system consist in two different building blocks. The first, for detecting unexpected interactions with the environment (perceptive block). The second, for intelligent and autonomous reaction after the stimulus (control block). The perceptive block is responsible of the contact event identification. In short, the approach is based on the claim that a sensorless collision detection method for robot manipulators can be extended to the field of mobile manipulators, by embedding it within a statistical learning framework. The control deals with the intelligent and autonomous reaction after the contact or impact with the environment occurs, and consist on an motion abstraction controller with a prioritized set of constrains, where the highest priority correspond to the robot reconfiguration after a collision is detected; when all related dynamical effects have been compensated, the controller switch again to the basic control mode

Learning the dynamics of articulated tracked vehicles

In this work, we present a Bayesian non-parametric approach to model the motion control of ATVs. The motion control model is based on a Dirichlet Process-Gaussian Process (DP-GP) mixture model. The DP-GP mixture model provides a flexible representation of patterns of control manoeuvres along trajectories of different lengths and discretizations. The model also estimates the number of patterns, sufficient for modeling the dynamics of the ATV

Point Cloud Structural Parts Extraction based on Segmentation Energy Minimization

In this work we consider 3D point sets, which in a typical setting represent unorganized point clouds. Segmentation of these point sets requires first to single out structural components of the unknown surface discretely approximated by the point cloud. Structural components, in turn, are surface patches approximating unknown parts of elementary geometric structures, such as planes, ellipsoids, spheres and so on. The approach used is based on level set methods computing the moving front of the surface and tracing the interfaces between different parts of it. Level set methods are widely recognized to be one of the most efficient methods to segment both 2D images and 3D medical images. Level set methods for 3D segmentation have recently received an increasing interest. We contribute by proposing a novel approach for raw point sets. Based on the motion and distance functions of the level set we introduce four energy minimization models, which are used for segmentation, by considering an equal number of distance functions specified by geometric features. Finally we evaluate the proposed algorithm on point sets simulating unorganized point clouds

A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain

[EN] The evaluation of regional innovation performance through composite innovation indices can serve as a valuable tool for policy-making. While discussion on the best methodology to construct composite innovation indices continues, we are interested in deepening the use of reference levels and the aggregation issue. So far, additive aggregation methods are, largely, the most widespread aggregation rule, thus allowing for full compensability among single indicators. In this paper, we present an integrated assessment methodology to evaluate regional innovation performance using the Multi-Reference Point based Weak and Strong Composite Indicator (MRP-WSCI) approach, which allows defining reference levels and different degrees of compensability. As an example of application to the Regional Innovation Scoreboard, the proposed technique is developed to measure the innovation performance of Spain¿s regions taking into account Spanish and European reference levels. The main features of the proposed approach are: (i) absolute or relative reference levels could be previously defined by the decision maker; (ii) by establishing the reference levels, the resulting composite innovation index is an easy-to-interpret measure; and (iii) the non-compensatory strong composite indicator provides an additional layer of information for policy-making (iv) a visualization tool called Light-Diagram is proposed to track the specific strengths and weaknesses of the regions' innovation performance.This research has been partially supported by the Spanish Ministry of Economy and Competitiveness (Project ECO2016-76567-C4-4-R), by the Regional Government of Andalucia (research group SEJ-417), and by the ERDF funds (Project UMA18-FEDERJA-065).Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2020). A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain. Mathematics. 8(5):1-21. https://doi.org/10.3390/math8050797S12185Hauser, C., Siller, M., Schatzer, T., Walde, J., & Tappeiner, G. (2018). Measuring regional innovation: A critical inspection of the ability of single indicators to shape technological change. Technological Forecasting and Social Change, 129, 43-55. doi:10.1016/j.techfore.2017.10.019Makkonen, T., & van der Have, R. P. (2012). Benchmarking regional innovative performance: composite measures and direct innovation counts. Scientometrics, 94(1), 247-262. doi:10.1007/s11192-012-0753-2Asheim, B. T., Smith, H. L., & Oughton, C. (2011). Regional Innovation Systems: Theory, Empirics and Policy. Regional Studies, 45(7), 875-891. doi:10.1080/00343404.2011.596701Buesa, M., Heijs, J., & Baumert, T. (2010). The determinants of regional innovation in Europe: A combined factorial and regression knowledge production function approach. Research Policy, 39(6), 722-735. doi:10.1016/j.respol.2010.02.016Di Cagno, D., Fabrizi, A., Meliciani, V., & Wanzenböck, I. (2016). The impact of relational spillovers from joint research projects on knowledge creation across European regions. Technological Forecasting and Social Change, 108, 83-94. doi:10.1016/j.techfore.2016.04.021Capello, R., & Lenzi, C. (2012). Territorial patterns of innovation: a taxonomy of innovative regions in Europe. The Annals of Regional Science, 51(1), 119-154. doi:10.1007/s00168-012-0539-8Navarro, M., Gibaja, J. J., Bilbao-Osorio, B., & Aguado, R. (2009). Patterns of Innovation in EU-25 Regions: A Typology and Policy Recommendations. Environment and Planning C: Government and Policy, 27(5), 815-840. doi:10.1068/c0884rPinto, H. (2009). The Diversity of Innovation in the European Union: Mapping Latent Dimensions and Regional Profiles. European Planning Studies, 17(2), 303-326. doi:10.1080/09654310802553571Ruiz, F., El Gibari, S., Cabello, J. M., & Gómez, T. (2020). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega, 95, 102060. doi:10.1016/j.omega.2019.04.003Hollenstein, H. (1996). A composite indicator of a firm’s innovativeness. An empirical analysis based on survey data for Swiss manufacturing. Research Policy, 25(4), 633-645. doi:10.1016/0048-7333(95)00874-8Gu *, W., & Tang, J. (2004). Link between innovation and productivity in Canadian manufacturing industries. Economics of Innovation and New Technology, 13(7), 671-686. doi:10.1080/1043890410001686806Tang, J., & Le, C. D. (2007). Multidimensional Innovation and Productivity. Economics of Innovation and New Technology, 16(7), 501-516. doi:10.1080/10438590600914585Kumar, S., Haleem, A., & Sushil. (2019). Assessing innovativeness of manufacturing firms using an intuitionistic fuzzy based MCDM framework. Benchmarking: An International Journal, 26(6), 1823-1844. doi:10.1108/bij-12-2017-0343Grupp, H., & Mogee, M. E. (2004). Indicators for national science and technology policy: how robust are composite indicators? Research Policy, 33(9), 1373-1384. doi:10.1016/j.respol.2004.09.007Schibany, A., & Streicher, G. (2008). The European Innovation Scoreboard: drowning by numbers? Science and Public Policy, 35(10), 717-732. doi:10.3152/030234208x398512Kozłowski, J. (2015). Innovation indices: the need for positioning them where they properly belong. Scientometrics, 104(3), 609-628. doi:10.1007/s11192-015-1632-4Carayannis, E. G., Goletsis, Y., & Grigoroudis, E. (2018). Composite innovation metrics: MCDA and the Quadruple Innovation Helix framework. Technological Forecasting and Social Change, 131, 4-17. doi:10.1016/j.techfore.2017.03.008Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61-94. doi:10.1007/s11205-017-1832-9El Gibari, S., Gómez, T., & Ruiz, F. (2018). Building composite indicators using multicriteria methods: a review. Journal of Business Economics, 89(1), 1-24. doi:10.1007/s11573-018-0902-zRuiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189-197. doi:10.1057/jors.2009.187Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313-325. doi:10.1016/j.ejor.2013.11.031Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building Ease-of-Doing-Business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130-140. doi:10.1016/j.techfore.2017.06.005El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235-1250. doi:10.1016/j.joi.2018.10.003Mazziotta, M., & Pareto, A. (2017). Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices. Social Indicators Research, 136(3), 967-976. doi:10.1007/s11205-017-1577-5Munda, G., & Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting. Applied Economics, 41(12), 1513-1523. doi:10.1080/00036840601019364Cabello, J. M., Navarro, E., Prieto, F., Rodríguez, B., & Ruiz, F. (2014). Multicriteria development of synthetic indicators of the environmental profile of the Spanish regions. Ecological Indicators, 39, 10-23. doi:10.1016/j.ecolind.2013.11.01

A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators

[EN] There is a growing interest in research on the role that space plays in defining and measuring well-being or quality of life. In this paper, we propose to evaluate the regional quality of life using the Multi-Reference Point based Weak Strong Composite Indicator approach, to further enhance the quality of the sub-national analysis. The major motivation is to facilitate assessing the regional quality of life performance at different geographical scales and compensability levels. As an example of application, we compute the composite indicators for 19 Spanish regions to paint a comprehensive picture of the regional quality of life using two different geographical scales: the Spanish and the European ones. Moreover, we provide warning signals to regional, national and European policy-makers on the quality of life dimensions in which each region needs further improvements.This research was partially funded by the Spanish Ministry of Economy and Competitiveness (Project PID2019-104263RB-C42), from the Regional Government of Andalucía (Project P18-RT-1566), and by the EU ERDF operative program (Project UMA18-FEDERJA-065)Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2021). A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators. Social Indicators Research (Online). 1-20. https://doi.org/10.1007/s11205-021-02818-0S120Blancas, F., Caballero, R., González, M., Lozano-Oyola, M., & Pérez, F. (2010). Goal programming synthetic indicators: An application for sustainable tourism in andalusian coastal counties. Ecological Economics, 69(11), 2158–2172.Boggia, A., Massei, G., Pace, E., Rocchi, L., Paolotti, L., & Attard, M. (2018). Spatial multicriteria analysis for sustainability assessment: A new model for decision making. Land Use Policy, 71, 281–292.Booysen, F. (2002). An overview and evaluation of composite indices of development. Social Indicators Research, 59(2), 115–151.Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual fund’s environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313–325.Costa, D. S. (2015). Reflective, causal, and composite indicators of quality of life: A conceptual or an empirical distinction? Quality of Life Research, 24(9), 2057–2065.Durand, M. (2015). The OCDE better life initiative: How’s life? and the measurement of well-being. Review of Income and Wealth, 61(1), 4–17.El Gibari, S., Cabello, J. M., Gómez, T., & Ruiz, F. (2021). Composite indicators as decision making tools: The joint use of compensatory and non-compensatory schemes. International Journal of Information Technology and Decision Making, 20(3), 847–879.El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235–1250.El Gibari, S., Gómez, T., & Ruiz, F. (2019). Building composite indicators using multicriteria methods: A review. Journal of Business Economics, 89(1), 1–24.European Commission: Eurostat quality of life database. (2020). url http://ec.europa.eu/eurostat/data/database.Freudenberg, M. (2003). Composite indicators of country performance.Garcia-Bernabeu, A., Cabello, J. M., & Ruiz, F. (2020). A multi-criteria reference point based approach for assessing regional innovation performance in Spain. Mathematics, 8(5), 797.Goerlich, F. J., & Reig, E. (2021). Quality of life ranking of spanish cities: A non-compensatory approach. Cities, 109, 102979.Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social Indicators Research, 141, 61–94.Greyling, T., & Tregenna, F. (2017). Construction and analysis of a composite quality of life index for a region of South Africa. Social Indicators Research, 131(3), 887–930.Hagerty, M. R., Cummins, R., Ferriss, A. L., Land, K., Michalos, A. C., Peterson, M., et al. (2001). Quality of life indexes for national policy: Review and agenda for research. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 71(1), 58–78.INE: Indicadores de calidad de vida. (2020). url https://cutt.ly/Zj0L0qX.Ivaldi, E., Bonatti, G., Soliani, R., et al. (2014). Composite index for quality of life in italian cities: An application to urbes indicators. Review of Economics and Finance, 4(4)Karagiannis, R., & Karagiannis, G. (2020). Constructing composite indicators with shannon entropy: The case of human development index. Socio-Economic Planning Sciences, 70, 100701.Lagas, P., van Dongen, F., van Rijn, F., & Visser, H. (2015). Regional quality of living in Europe. Region, 2(2), 1–26.Malkina-Pykh, I. G., & Pykh, Y. A. (2008). Quality-of-life indicators at different scales: Theoretical background. Ecological Indicators, 8(6), 854–862.Marchante, A. J., & Ortega, B. (2006). Quality of life and economic convergence across Spanish regions, 1980–2001. Regional Studies, 40(5), 471–483.Mazziotta, M., & Pareto, A. (2016). On a generalized non-compensatory composite index for measuring socio-economic phenomena. Social Indicators Research, 127(3), 983–1003.Mazziotta, M., & Pareto, A. (2020). Composite indices construction: The performance interval approach. Social Indicators Research pp. 1–11.Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2008). Handbook on constructing composite indicators.OECD: Handbook on constructing composite indicators: methodology and user guide. (2008). Paris: OECD publishing.Patil, G.R., & Sharma, G. (2020). Urban quality of life: An assessment and ranking for indian cities. Transport Policy.Royuela, V., Suriñach, J., & Reyes, M. (2003). Measuring quality of life in small areas over different periods of time. Social Indicators Research, 64(1), 51–74.Ruiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189–197.Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building ease-of-doing-business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130–140.Ruiz, F., El Gibari, S., Cabello, J.M., & Gómez, T. (2019). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega.Saisana, M., & Tarantola, S. (2002). State-of-the-art report on current methodologies and practices for composite indicator development. Ispra: Joint Research Centre.Stiglitz, J.E., Sen, A., Fitoussi, J.P., et al. (2009). Report by the commission on the measurement of economic performance and social progress

2001: The Crystal Monolith

In the famous movie “2001: A Space Odyssey”, Stanley Kubrick and Arthur Clarke claim that an extraterrestrial civilization catalyzed the evolution of hominids on our planet. To represent such a powerful civilization, they use a crystal. To date, it seems that we have not been contacted by advanced civilizations and that we are alone to manage our own future. Yet Kubrick and Clarke perhaps intuitively touched a truth about the power of crystals. An argument is developed here that genuine crystals, mainly quartz single crystals, were the earliest catalysts of the abstract thinking, symbolism, and consciousness

Component-wise modeling of articulated objects

We introduce a novel framework for modeling articulated objects based on the aspects of their components. By decomposing the object into components, we divide the problem in smaller modeling tasks. After obtaining 3D models for each component aspect by employing a shape deformation paradigm, we merge them together, forming the object components. The final model is obtained by assembling the components using an optimization scheme which fits the respective 3D models to the corresponding apparent contours in a reference pose. The results suggest that our approach can produce realistic 3D models of articulated objects in reasonable time

ICT: an empowering tool for social enterprises

In recent years new models for organizations working on poverty alleviation have emerged. One of them, the social enterprise, has attracted the attention of both academics and practitioners all over the world. Even if defined in different ways depending on the context, this model has an enormous potential to generate social benefits and to promote local agency and private initiative in poverty alleviation