Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras

In this paper we propose a fuzzy method to detect free paths in real-time using digital stereo images. It is based on looking for linear variations of depth in disparity maps, which are obtained by processing a pair of rectified images from two stereo cameras. By applying least-squares fitting over groups of disparity maps columns to a linear model, free paths are detected by giving a certainty using a fuzzy rule. Experimental results on real outdoor images are also presented.Nuria Ortigosa acknowledges the support of Universidad Polit'ecnica de Valencia under grant FPI-UPV 2008. Samuel Morillas acknowledges the support of Spanish Ministry of Education and Science under grant MTM 2009-12872-C02-01.Ortigosa Araque, N.; Morillas Gómez, S.; Peris Fajarnes, G.; Dunai Dunai, L. (2012). Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras. 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Computer Vision and Image Understanding, 80(1), 21-41. doi:10.1006/cviu.2000.0872Moreno-Garcia, J., Rodriguez-Benitez, L., Fernández-Caballero, A., & López, M. T. (2010). Video sequence motion tracking by fuzzification techniques. Applied Soft Computing, 10(1), 318-331. doi:10.1016/j.asoc.2009.08.002Morillas, S., Gregori, V., & Hervas, A. (2009). Fuzzy Peer Groups for Reducing Mixed Gaussian-Impulse Noise From Color Images. IEEE Transactions on Image Processing, 18(7), 1452-1466. doi:10.1109/tip.2009.2019305Poloni, M., Ulivi, G., & Vendittelli, M. (1995). Fuzzy logic and autonomous vehicles: Experiments in ultrasonic vision. Fuzzy Sets and Systems, 69(1), 15-27. doi:10.1016/0165-0114(94)00237-2Alonso, J. M., Magdalena, L., Guillaume, S., Sotelo, M. A., Bergasa, L. M., Ocaña, M., & Flores, R. (2007). Knowledge-based Intelligent Diagnosis of Ground Robot Collision with Non Detectable Obstacles. Journal of Intelligent and Robotic Systems, 48(4), 539-566. doi:10.1007/s10846-006-9125-6McFetridge, L., & Ibrahim, M. Y. (2009). A new methodology of mobile robot navigation: The agoraphilic algorithm. Robotics and Computer-Integrated Manufacturing, 25(3), 545-551. doi:10.1016/j.rcim.2008.01.008Sun, H., & Yang, J. (2001). Obstacle detection for mobile vehicle using neural network and fuzzy logic. Neural Network and Distributed Processing. doi:10.1117/12.441696Ortigosa, N., Morillas, S., & Peris-Fajarnés, G. (2010). Obstacle-Free Pathway Detection by Means of Depth Maps. Journal of Intelligent & Robotic Systems, 63(1), 115-129. doi:10.1007/s10846-010-9498-4Picton, P. D., & Capp, M. D. (2008). Relaying scene information to the blind via sound using cartoon depth maps. Image and Vision Computing, 26(4), 570-577. doi:10.1016/j.imavis.2007.07.005Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330-1334. doi:10.1109/34.888718Scharstein, D., & Szeliski, R. (2002). International Journal of Computer Vision, 47(1/3), 7-42. doi:10.1023/a:1014573219977Felzenszwalb, P. F., & Huttenlocher, D. P. (2006). Efficient Belief Propagation for Early Vision. International Journal of Computer Vision, 70(1), 41-54. doi:10.1007/s11263-006-7899-4Qingxiong Yang, Liang Wang, Ruigang Yang, Stewenius, H., & Nister, D. (2009). Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3), 492-504. doi:10.1109/tpami.2008.99Zitnick, C. L., & Kang, S. B. (2007). Stereo for Image-Based Rendering using Image Over-Segmentation. International Journal of Computer Vision, 75(1), 49-65. doi:10.1007/s11263-006-0018-8Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision. doi:10.1017/cbo9780511811685Lee, C. C. (1990). Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 404-418. doi:10.1109/21.52551C. Fodor, J. (1993). A new look at fuzzy connectives. Fuzzy Sets and Systems, 57(2), 141-148. doi:10.1016/0165-0114(93)90153-9Nalpantidis, L., & Gasteratos, A. (2010). Stereo vision for robotic applications in the presence of non-ideal lighting conditions. Image and Vision Computing, 28(6), 940-951. doi:10.1016/j.imavis.2009.11.011BOHANNON, R. W. (1997). Comfortable and maximum walking speed of adults aged 20—79 years: reference values and determinants. Age and Ageing, 26(1), 15-19. doi:10.1093/ageing/26.1.1

Some geometrical methods for constructing contradiction measures on Atanassov's intuitionistic fuzzy sets

Trillas et al. (1999, Soft computing, 3 (4), 197–199) and Trillas and Cubillo (1999, On non-contradictory input/output couples in Zadeh's CRI proceeding, 28–32) introduced the study of contradiction in the framework of fuzzy logic because of the significance of avoiding contradictory outputs in inference processes. Later, the study of contradiction in the framework of Atanassov's intuitionistic fuzzy sets (A-IFSs) was initiated by Cubillo and Castiñeira (2004, Contradiction in intuitionistic fuzzy sets proceeding, 2180–2186). The axiomatic definition of contradiction measure was stated in Castiñeira and Cubillo (2009, International journal of intelligent systems, 24, 863–888). Likewise, the concept of continuity of these measures was formalized through several axioms. To be precise, they defined continuity when the sets ‘are increasing’, denominated continuity from below, and continuity when the sets ‘are decreasing’, or continuity from above. The aim of this paper is to provide some geometrical construction methods for obtaining contradiction measures in the framework of A-IFSs and to study what continuity properties these measures satisfy. Furthermore, we show the geometrical interpretations motivating the measures

Fuzzy Partial Metric Spaces

"This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of General Systems on 01 Dec 2018, available online: https://doi.org/10.1080/03081079.2018.1552687"[EN] In this paper we provide a concept of fuzzy partial metric space (X, P, ¿) as an extension to fuzzy setting in the sense of Kramosil and Michalek, of the concept of partial metric due to Matthews. This extension has been defined using the residuum operator ¿¿ associated to a continuous t-norm ¿ and without any extra condition on ¿. Similarly, it is defined the stronger concept of GV -fuzzy partial metric (fuzzy partial metric in the sense of George and Veeramani). After defining a concept of open ball in (X, P, ¿), a topology TP on X deduced from P is constructed, and it is showed that (X, TP) is a T0-space.Valentin Gregori acknowledges the support of the Ministry of Economy and Competitiveness of Spain under Grant MTM2015-64373-P (MINECO/Feder, UE). Juan Jose Minana acknowledges the partially support of the Ministry of Economy and Competitiveness of Spain under Grant TIN2016-81731-REDT (LODISCO II) and AEI/FEDER, UE funds, by the Programa Operatiu FEDER 2014-2020 de les Illes Balears, by project ref. PROCOE/4/2017 (Direccio General d'Innovacio i Recerca, Govern de les Illes Balears), and by project ROBINS. The latter has received research funding from the European Union framework under GA 779776. This publication reflects only the authors views and the European Union is not liable for any use that may be made of the information contained therein.Gregori Gregori, V.; Miñana, J.; Miravet-Fortuño, D. (2018). Fuzzy Partial Metric Spaces. International Journal of General Systems. https://doi.org/10.1080/03081079.2018.1552687SBukatin, M., Kopperman, R., & Matthews, S. (2014). Some corollaries of the correspondence between partial metrics and multivalued equalities. Fuzzy Sets and Systems, 256, 57-72. doi:10.1016/j.fss.2013.08.016Camarena, J.-G., Gregori, V., Morillas, S., & Sapena, A. (2010). Two-step fuzzy logic-based method for impulse noise detection in colour images. Pattern Recognition Letters, 31(13), 1842-1849. doi:10.1016/j.patrec.2010.01.008Demirci, M. (2012). The order-theoretic duality and relations between partial metrics and local equalities. Fuzzy Sets and Systems, 192, 45-57. doi:10.1016/j.fss.2011.04.014George, A., & Veeramani, P. (1994). On some results in fuzzy metric spaces. Fuzzy Sets and Systems, 64(3), 395-399. doi:10.1016/0165-0114(94)90162-7Grabiec, M. (1988). Fixed points in fuzzy metric spaces. Fuzzy Sets and Systems, 27(3), 385-389. doi:10.1016/0165-0114(88)90064-4Grečova, S., & Morillas, S. (2016). Perceptual similarity between color images using fuzzy metrics. Journal of Visual Communication and Image Representation, 34, 230-235. doi:10.1016/j.jvcir.2015.04.003Gregori, V., Miñana, J.-J., & Morillas, S. (2012). Some questions in fuzzy metric spaces. Fuzzy Sets and Systems, 204, 71-85. doi:10.1016/j.fss.2011.12.008Gregori, V., Morillas, S., & Sapena, A. (2010). On a class of completable fuzzy metric spaces. Fuzzy Sets and Systems, 161(16), 2193-2205. doi:10.1016/j.fss.2010.03.013Gregori, V., & Romaguera, S. (2000). Some properties of fuzzy metric spaces. Fuzzy Sets and Systems, 115(3), 485-489. doi:10.1016/s0165-0114(98)00281-4Gregori, V., & Sapena, A. (2002). On fixed-point theorems in fuzzy metric spaces. Fuzzy Sets and Systems, 125(2), 245-252. doi:10.1016/s0165-0114(00)00088-9Gutiérrez García, J., Rodríguez-López, J., & Romaguera, S. (2018). On fuzzy uniformities induced by a fuzzy metric space. Fuzzy Sets and Systems, 330, 52-78. doi:10.1016/j.fss.2017.05.001Höhle, U., & Klement, E. P. (Eds.). (1995). Non-Classical Logics and their Applications to Fuzzy Subsets. doi:10.1007/978-94-011-0215-5Klement, E. P., Mesiar, R., & Pap, E. (2000). Triangular Norms. Trends in Logic. doi:10.1007/978-94-015-9540-7MATTHEWS, S. G. (1994). Partial Metric Topology. Annals of the New York Academy of Sciences, 728(1 General Topol), 183-197. doi:10.1111/j.1749-6632.1994.tb44144.xMenger, K. (1942). Statistical Metrics. Proceedings of the National Academy of Sciences, 28(12), 535-537. doi:10.1073/pnas.28.12.535Miheţ, D. (2008). Fuzzy -contractive mappings in non-Archimedean fuzzy metric spaces. Fuzzy Sets and Systems, 159(6), 739-744. doi:10.1016/j.fss.2007.07.006Schweizer, B., & Sklar, A. (1960). Statistical metric spaces. Pacific Journal of Mathematics, 10(1), 313-334. doi:10.2140/pjm.1960.10.313Shukla, S., Gopal, D., & Roldán-López-de-Hierro, A.-F. (2016). Some fixed point theorems in 1-M-complete fuzzy metric-like spaces. International Journal of General Systems, 45(7-8), 815-829. doi:10.1080/03081079.2016.1153084Ying, M. (1991). A new approach for fuzzy topology (I). Fuzzy Sets and Systems, 39(3), 303-321. doi:10.1016/0165-0114(91)90100-5Yue, Y. (2015). Separated ▵+-valued equivalences as probabilistic partial metric spaces. Journal of Intelligent & Fuzzy Systems, 28(6), 2715-2724. doi:10.3233/ifs-15154

Application of fuzzy logic in performance management: a literature review

[EN] Performance management has become in a key success factor for any organization. Traditionally, performance management has focused uniquely in financial measures, mainly using quantitative measures, but two decades ago they were extended towards an integral view of the organization, appearing qualitative measures. This type of extended view and associated measures have a degree of uncertainty that needs to be bounded. One of the essential tools for uncertainty bounding is the fuzzy logic and, therefore,the main objective of this paper is the analysis of the literature about the application of fuzzy logic in performance measurement systems operating within uncertainty environments with the aim of categorizing, conceptualizing and classifying the works written so far. Finally, three categories are defined according to the different uses of fuzzy logic within performance management concluding that the most important application of fuzzy logic that counts with a higher number of studies is uncertainty bounding.Gurrea Montesinos, V.; Alfaro Saiz, JJ.; Rodríguez Rodríguez, R.; Verdecho Sáez, MJ. (2014). Application of fuzzy logic in performance management: a literature review. International Journal of Production Management and Engineering. 2(2):93-100. doi:10.4995/ijpme.2014.1859SWORD9310022Amini, S., & Jochem, R. (2011). A Conceptual Model Based on the Fuzzy Set Theory to Measure and Evaluate the Performance of Service Processes. 2011 IEEE 15th International Enterprise Distributed Object Computing Conference Workshops. doi:10.1109/edocw.2011.25Ammar, S. & Wright, R. (1995), "A Fuzzy Logic Approach to Performance Evaluation". Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95., pp. 246 - 251Ammar, S., & Wright, R. (2000). Applying fuzzy-set theory to performance evaluation. Socio-Economic Planning Sciences, 34(4), 285-302. doi:10.1016/s0038-0121(00)00004-5Arango, M.D., Jaimes, W.A. & Zapata, J.A. (2010) "Gestion cadena de abastecimiento - Logistica con indicadores bajo incertidumbre, caso aplicado sector panificador palmira" Ciencia e Ingeniería Neogranadina, Vol. 20-1, pp. 97-115.Beheshti, H. M., & Lollar, J. G. (2008). Fuzzy logic and performance evaluation: discussion and application. International Journal of Productivity and Performance Management, 57(3), 237-246. doi:10.1108/17410400810857248Behrouzi, F., & Wong, K. Y. (2011). Lean performance evaluation of manufacturing systems: A dynamic and innovative approach. Procedia Computer Science, 3, 388-395. doi:10.1016/j.procs.2010.12.065Chan, T.S., Ql, H.J. (2003), "An innovative performance measurement method for supply chain management". Sup-ply Chain Management: An International Journal Volume 8 Number 3, pp. 209-223.Chan, F. T. S., Qi, H. J., Chan, H. K., Lau, H. C. W., & Ip, R. W. L. (2003). A conceptual model of performance measurement for supply chains. Management Decision, 41(7), 635-642. doi:10.1108/00251740310495568Chen, C.-T., Lin, C.-T., & Huang, S.-F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289-301. doi:10.1016/j.ijpe.2005.03.009Cheng, S., Hsu, B., & Shu, M. (2007). Fuzzy testing and selecting better processes performance. Industrial Management & Data Systems, 107(6), 862-881. doi:10.1108/02635570710758761Ferreira, A., Azevedo,S. &Fazendeiro, P. (2012) "A Linguistic Approach to Supply Chain Performance Assessment". IEEE International Conference on Fuzzy Sistems, pp.1-5.Lau, H. C. W., Kai Pang, W., & Wong, C. W. Y. (2002). Methodology for monitoring supply chain performance: a fuzzy logic approach. Logistics Information Management, 15(4), 271-280. doi:10.1108/09576050210436110Lalmazloumian M. & Yew K., (2012), "A Review of Modelling Approaches for Supply Chain Planning Under Un-certainty". 9th International Conference on Service Systems and Service Management (ICSSSM), pp. 197-203.Liao, M.-Y., & Wu, C.-W. (2010). Evaluating process performance based on the incapability index for measurements with uncertainty. Expert Systems with Applications, 37(8), 5999-6006. doi:10.1016/j.eswa.2010.02.005Lu, C. & Wei li, X. (2006), "Supply Chain Modeling Using Fuzzy Sets and Possibility Theory in an Uncertain Envi-ronment". The Sixth World Congress on Intelligent Control and Automation, Vol.2, pp. 3608-3612.Mahnam, M., Yadollahpour, M. R., Famil-Dardashti, V., & Hejazi, S. R. (2009). Supply chain modeling in uncertain environment with bi-objective approach. Computers & Industrial Engineering, 56(4), 1535-1544. doi:10.1016/j.cie.2008.09.038Muñoz, M. J., Rivera, J. M., & Moneva, J. M. (2008). Evaluating sustainability in organisations with a fuzzy logic approach. Industrial Management & Data Systems, 108(6), 829-841. doi:10.1108/02635570810884030Olugu, E. U., & Wong, K. Y. (2012). An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry. Expert Systems with Applications, 39(1), 375-384. doi:10.1016/j.eswa.2011.07.026Tabrizi, B. H., & Razmi, J. (2013). Introducing a mixed-integer non-linear fuzzy model for risk management in designing supply chain networks. Journal of Manufacturing Systems, 32(2), 295-307. doi:10.1016/j.jmsy.2012.12.001Theeranuphattana, A., & Tang, J. C. S. (2007). A conceptual model of performance measurement for supply chains. Journal of Manufacturing Technology Management, 19(1), 125-148. doi:10.1108/17410380810843480Unahabhokha, C., Platts, K., & Hua Tan, K. (2007). Predictive performance measurement system. Benchmarking: An International Journal, 14(1), 77-91. doi:10.1108/14635770710730946Van der Vorst, J. G. A. J., & Beulens, A. J. M. (2002). Identifying sources of uncertainty to generate supply chain redesign strategies. International Journal of Physical Distribution & Logistics Management, 32(6), 409-430. doi:10.1108/09600030210437951Wei, C., Liou, T., & Lee, K. (2008). An ERP performance measurement framework using a fuzzy integral approach. Journal of Manufacturing Technology Management, 19(5), 607-626. doi:10.1108/17410380810877285Xu Xiao Xia, L., Ma, B. & Lim, R. (2008) "Supplier Performance Measurement in a Supply Chain". 6th IEEE Inter-national Conference on Industrial Informatics, pp. 877-881

Cooperative maneuvers applied to automated vehicles in real and virtual environments

[ES] En los últimos años los Sistemas Inteligentes de Transporte, ITS (del inglés, Intelligent Transportation System) se han convertido en una realidad dentro de la sociedad, aportando soluciones y beneficios a la conducción. Con el fin de contribuir a su desarrollo, el presente trabajo describe un marco cooperativo híbrido capaz de validar maniobras entre múltiples vehículos (virtuales y reales), con el fin de disminuir los costos, tiempos y riesgos asociados al ajuste de los controladores. Para su validación se presentan 3 casos de estudios. El primero consiste en utilizar dos vehículos virtuales para realizar un Control de Crucero Adaptativo, ACC (del inglés, Adaptive Cruise Control) con seguidor de trayectoria. El segundo, emplea un coche real como seguidor y un coche virtual como líder para la maniobra de Stop & Go. Finalmente, se utilizan dos vehículos reales para el ACC. Los algoritmos de seguimiento empleados para las maniobras cooperativas están basados en controladores de lógi[EN] In recent years, Intelligent Transportation Systems (ITS) have become a reality within society, by providing benefits and solutions to the conduction. With the aim of contributing with the development of the ITS, the present work describes a hybrid cooperative framework for the validation of maneuvers between multiple vehicles (virtual and real), in order to reduce cost, time and risks associated with the controllers adjustment. For its validation three case of studies are presented. The first one consists of using two virtual vehicles to perform an Adaptive Cruise Control (ACC) with trajectory tracker. The second one, in using a real car as the follower and a virtual vehicle as the lider to perform a Stop & Go. And finally, two real cars are used to carry out an ACC. The tracker algorithms employed for the cooperative maneuvers are based in fuzzy logic controllers. The results show the versatility of the proposed framework, which was able to correctly execute the maneuvers in eachProyecto SerIoT H2020 (Con número de concesiòn 780139)Hidalgo, C.; Marcano, M.; Fernández, G.; Pérez, JM. (2020). Maniobras cooperativas aplicadas a vehículos automatizados en entornos virtuales y reales. Revista Iberoamericana de Automática e Informática industrial. 17(1):56-65. https://doi.org/10.4995/riai.2019.111555665171Cuadrado, J., Vilela, D., Iglesias, I., Martín, A., Peña, A., 2013. A multibody model to assess the effect of automotive motor in-wheel configuration on vehicle stability and comfort. ECCOMAS Multibody Dynamics.Da Cunha, F. D., Boukerche, A., Villas, L., Viana, A. C., Loureiro, A. A., 2014. Data communication in vanets: a survey, challenges and applications. Ph.D. thesis, INRIA Saclay; INRIA. https://doi.org/10.1016/j.adhoc.2016.02.017Driankov, D., Hellendoorn, H., Reinfrank, M., 1996. Introduction. In: An Introduction to Fuzzy Control. Springer, pp. 1-36. https://doi.org/10.1007/978-3-662-03284-8During, M., Lemmer, K., 2016. Cooperative maneuver planning for cooperative driving. IEEE Intelligent Transportation Systems Magazine 8 (3), 8-22. https://doi.org/10.1109/MITS.2016.2549997Española, D. G. D. T., Jul. 2015. Seguridad vial. http://www.dgt.es/es/seguridad-vial/.Fernandez, S., Ito, T., nov 2016. Driver classification for intelligent transportation systems using fuzzy logic. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE. https://doi.org/10.1109/ITSC.2016.7795711Festag, A., 2014. Cooperative intelligent transport systems standards in europe. IEEE communications magazine 52 (12), 166-172. https://doi.org/10.1109/MCOM.2014.6979970González, D., Pérez, J., Milanés, V., Nashashibi, F., 2016. A review of motion planning techniques for automated vehicles. IEEE Transactions on Intelligent Transportation Systems 17 (4), 1135-1145. https://doi.org/10.1109/TITS.2015.2498841Iancu, I., 2012. A mamdani type fuzzy logic controller. In: Fuzzy Logic-Controls, Concepts, Theories and Applications. InTech. https://doi.org/10.5772/36321Jena, K. S., Joseph, A. V., Senapati, P. R. R., 2016. Fuzzy logic based approach for controlling of a vehicle in its longitudinal motion. Middle-East Journal of Scientific Research 24 (S1), 346-352. https://doi.org/10.1017/S1047198700014406Lattarulo, R., González, L., Martí, E., Matute, J., Marcano, M., Pérez, J., 2018. Urban motion planning framework based on n-bézier curves considering comfort and safety. Journal of Advanced Transportation 2018. https://doi.org/10.1155/2018/6060924Lattarulo, R., Marcano, M., Pérez, J., 2017a. Overtaking maneuver for automated driving using virtual environments. In: International Conference on Computer Aided Systems Theory. Springer, pp. 446-453. https://doi.org/10.1007/978-3-319-74727-9Lattarulo, R., Pérez, J., Dendaluce, M., 2017b. A complete framework for developing and testing automated driving controllers. IFAC-PapersOnLine 50 (1), 258-263. https://doi.org/10.1016/j.ifacol.2017.08.043Lin, W.-Y., Li, M.-W., Lan, K.-C., Hsu, C.-H., 2010. A comparison of 802.11 a and 802.11 p for v-to-i communication: A measurement study. In: International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness. Springer, pp. 559-570. https://doi.org/10.1007/978-3-642-29222-4Marcano, M., Matute, J. A., Lattarulo, R., Martí, E., Pérez, J., 2018. Low speed longitudinal control algorithms for automated vehicles in simulation and real platforms. Complexity 2018. https://doi.org/10.1155/2018/7615123Marzbanrad, J., Moghaddam, T.-z., et al., 2015. Prediction of drivers accelerating behavior in the stop and go maneuvers using genetic algorithm-artificial neural network hybrid intelligence. International Journal of Automotive Engineering 5 (2), 986-998.Milanés, V., Villagrá, J., Godoy, J., González, C., 2012. Comparing fuzzy and intelligent pi controllers in stop-and-go manoeuvres. IEEE Transactions on Control Systems Technology 20 (3), 770-778. https://doi.org/10.1109/TCST.2011.2135859Milanes, V., Villagra, J., Godoy, J., Simo, J., Pérez, J., Onieva, E., 2012. An intelligent v2i-based traffic management system. IEEE Transactions on Intelligent Transportation Systems 13 (1), 49-58. https://doi.org/10.1109/TITS.2011.2178839Morcillo, C. G., 2011. Lógica difusa. una introducción práctica. Universidad de Castilla-La Mancha.Pérez, J., Milanés, V., Alonso, J., Onieva, E., De Pedro, T., 2010. Adelantamiento con vehículos autónomos en carreteras de doble sentido. Revista Iberoamericana de Automática e Informática Industrial RIAI 7 (3), 25-33. https://doi.org/10.1016/S1697-7912(10)70039-XRastelli, J. P., Peñas, M. S., 2015. Fuzzy logic steering control of autonomous vehicles inside roundabouts. Applied Soft Computing 35, 662-669. https://doi.org/10.1016/j.asoc.2015.06.030Rizvi, R., Kalra, S., Gosalia, C., Rahnamayan, S., 2014. Fuzzy adaptive cruise control system with speed sign detection capability. In: Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on. IEEE, pp. 968-976. https://doi.org/10.1109/FUZZ-IEEE.2014.6891748Schmied, R., Moser, D.,Waschl, H., del Re, L., 2016. Scenario model predictive control for robust adaptive cruise control in multi-vehicle traffic situations. In: Intelligent Vehicles Symposium (IV), 2016 IEEE. IEEE, pp. 802-807. https://doi.org/10.1109/IVS.2016.7535479Sriranjan, S., Lattarulo, R., Pérez-Rastelli, J., Ibanez-Guzman, J., Pena, A., 2017. Lateral controllers using neuro-fuzzy systems for automated vehicles: A comparative study.Sugeno, M., 1985. An introductory survey of fuzzy control. 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Multi-robot task planning problem with uncertainty in game theoretic framework

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-31665-4_6An efficiency of an multi-robot systems depends on proper coordinating tasks of all robots. This paper presents a game theoretic approach to modelling and solving the pick-up and collection problem. The classical form of this problem is modified in order to introduce the aspect of an uncertainty related to an information about the workspace inside of which robots are intended to perform the task. The process of modelling the problem in game theoretic framework, as well as cooperative solution to the problem is discussed in these paper. Results of exemplary simulations are presented to prove the suitability of the approach presented.Skrzypczyk, K.; Mellado Arteche, M. (2013). Multi-robot task planning problem with uncertainty in game theoretic framework. En Advanced Technologies for Intelligent Systems of National Border Security. Springer. 69-80. doi:10.1007/978-3-642-31665-4_6S6980Alami, R., et al.: Toward human-aware robot task planning. In: Proc. of AAAI Spring Symposium, Stanford (USA), pp. 39–46 (2006)Baioletti, M., Marcugini, S., Milani, A.: Task Planning and Partial Order Planning: A Domain Transformation Approach. In: Steel, S. (ed.) ECP 1997. LNCS, vol. 1348. Springer, Heidelberg (1997)Desouky, S.F., Schwartz, H.M.: Self-learning Fuzzy logic controllers for pursuit-evasion differential games. Robotics and Autonomous Systems (2010), doi:10.1016/j.robot.2010.09.006Harmati, I., Skrzypczyk, K.: Robot team coordination for target tracking using fuzzy logic controller in game theoretic framework. Robotics and Autonomous Systems 57(1) (2009)Kaminka, G.A., Erusalimchik, D., Kraus, S.: Adaptive Multi-Robot Coordination: A Game-Theoretic Perspective. In: Proc. of IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA (2002)Kok, J.R., Spaan, M.T.J., Vlassis, N.: Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems 50(2-3), 99–114 (2005)Klusch, M., Gerber, A.: Dynamic coalition formation among rational agents. IEEE Intelligent Systems 17(3), 42–47 (2002)Kraus, S., Winkfeld, J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence 75, 297–345 (1995)Kraus, S.: Negotiation and cooperation in multiagent environments. Artificial Intelligence 94(1-2), 79–98 (1997)Mataric, M., Sukhatme, G., Ostergaard, E.: Multi-Robot Task Allocation in Uncertain Environments. Autonomous Robots (14), 255–263 (2003)Meng, Y.: Multi-Robot Searching using Game-Theory Based Approach. International Journal of Advanced Robotic Systems 5(4) (2008)Jones, C., Mataric, M.: Adaptive Division of Labor in Large-Scale Minimalist Multi-Robot Systems. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, USA, pp. 1969–1974 (2003)Sariel, S., Balch, T., Erdogan, N.: Incremental Multi-Robot Task Selection for Resource Constrained and Interrelated Tasks. In: Proc. of 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA (2007)Schneider-Fontan, M., Mataric, M.J.: Territorial Multi-Robot Task Division. IEEE Transactions on Robotics and Automation 14(5), 815–822 (1998)Song, M., Gu, G., Zhang, R., Wang, X.: A method of multi-robot formation with the least total cost. International Journal of Information and System Science 1(3-4), 364–371 (2005)Cheng, X., Shen, J., Liu, H., Gu, G.-c.: Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4489, pp. 90–97. Springer, Heidelberg (2007

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

A survey of fuzzy control for stabilized platforms

This paper focusses on the application of fuzzy control techniques (fuzzy type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and fuzzy-PID controller) in the area of stabilized platforms. It represents an attempt to cover the basic principles and concepts of fuzzy control in stabilization and position control, with an outline of a number of recent applications used in advanced control of stabilized platform. Overall, in this survey we will make some comparisons with the classical control techniques such us PID control to demonstrate the advantages and disadvantages of the application of fuzzy control techniques

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested