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A Controller Design for the Khepera Robot : A Rough Set Approach

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Języki publikacji
EN
Abstrakty
EN
The Khepera robot belongs to the family of miniature mobile robots of the K-Team firm. It is used in a number of places for scientific and educational purposes. Considering its advantages (such as small size, precision of movement, ease of control), it is applied to testing different approaches in the domain of artificial intelligence. This paper describes the methodology of a control system design for the Khepera robot based on a rough set approach. The proposed approach entails a study of robot behaviour insofar as its movements are influenced by measurements from its sensors and the choice of actions that make it possible for the robot to achieve its system goals. The constructed controller concerns the realization of some tasks such as avoiding the obstacles, reaching a target, following an obstacle, finding the way out of a labyrinth. The proposed controller has been tested on both a robot simulator and on a real robot. Our experimental results show that the proposed rough set methodology can be applied to the design of a controller for the Khepera robot.
Wydawca
Rocznik
Strony
219--231
Opis fizyczny
Bibliogr. 40 poz., wykr.
Twórcy
autor
  • Chair of Computer Science Foundations University of Information Technology and Management, Rzeszow, Poland
autor
  • Department of Electrical and Computer Engineering University of Manitoba, Canada
  • Institute of Mathematics, Rzeszow University, Poland
Bibliografia
  • [1] Alpigini, J. J., Peters, J. F., Skowron, A., Zhong, N., Eds.: Proceedings of 3rd Int. Conf. on Rough Sets and Current Trends in Computing (RSCTC2002), Malvern, PA, 14–16 Oct. 2002. Lecture Notes in Artificial Intelligence, Springer-Verlag, Berlin, 2002.
  • [2] Czogala, E., Mrozek, A., Pawlak, Z.: The idea of a rough fuzzy controller and its application in the stabilization of a pendulum-car system, Fuzzy Sets and Systems 72, 1995, pp.61–63.
  • [3] Furuhashi, T., Yamamoto, H., Peters, J.F., Pedrycz, W.: A stability analysis of fuzzy control systems using generalized fuzzy Petri net model, International Journal of Advanced Computational Intelligence 3, 2, 1999, pp. 99–106.
  • [4] Furuhashi, T., Zaaiei, K., Ramanna, S., Agbonifoh, E.S.: Adaptive fuzzy rough approximate-time control system design methodology: Concepts, Petri net model, and application, in: Proc. Systems, Man and Cybernetics (SMC’98), San Diego, California, 1998, pp. 2101–2106.
  • [5] Grochowalski, P., Matula, P.: A fuzzy control for Khepera mobile robot into Matlab’s and KT Project’s environments, M.S. Thesis, Rzeszow University of Technology, Rzeszow 2002 (in Polish).
  • [6] Khepera User Manual Version 5.02, K-Team S.A., Lausanne, 1999.
  • [7] Li, H., Gupta, M. (Eds.): Fuzzy Logic and Intelligent Systems. Dordrecht, The Netherlands, 1995.
  • [8] Lin, T.Y.: Fuzzy controllers: an integrated approach based on fuzzy logic, rough sets, and evolutionary computing, in: T.Y. Lin, N. Cercone (eds.), Rough Sets and Data Mining. Analysis of Imprecise Data, Kluwer Academic Publishers, Boston 1997, pp. 123–138.
  • [9] Mrozek, A., Plonka, L., Winiarczyk, R., Majtan, J.: Rough sets for controller synthesis, in: T.Y. Lin (ed.), Proc. of the Third Int. Workshop on Rough Sets and Soft Computing (RSSC’94), San Jose, California, 10-12 November, 1994, pp. 498–505.
  • [10] Munakata, T.: Rough control: a perspective, in: T.Y. Lin, N. Cercone (eds.), Rough Sets and Data Mining. Analysis of Imprecise Data, Kluwer Academic Publishers, Boston 1997, pp. 77–88.
  • [11] Pal, S. K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing. Techniques for Computing with Words. Springer-Verlag, Heidelberg 2004.
  • [12] Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Dordrecht 1991.
  • [13] Pawlak, Z.: Rough sets. International J. Comp. Inform. Science. 11 1982, pp. 341–356.
  • [14] Pawlak, Z.: Rough sets and decision tables. Lecture Notes in Computer Science, 208, Springer Verlag, Berlin 1985, pp. 186–196.
  • [15] Pawlak, Z.: On rough dependency of attributes in information systems. Bulletin Polish Acad. Sci. Tech. 33 1985, pp. 551–599.
  • [16] Pawlak, Z.: On decision tables. Bulletin Polish Acad. Sci. Tech., 34 1986, pp. 553–572.
  • [17] Pawlak, Z.: Decision tables—a rough set approach, Bulletin ETACS, 33 1987, pp. 85–96.
  • [18] Pawlak, Z.: Elementary rough set granules: Toward a rough set processor. In: [11], 2004, pp. 5–14.
  • [19] Pawlak, Z.: Rough real functions and rough controllers, in: T.Y. Lin, N. Cercone (eds.), Rough Sets and Data Mining. Analysis of Imprecise Data, Kluwer Academic Publishers, Boston 1997, pp. 139–147.
  • [20] Pawlak, Z., Skowron, A.: Rough membership functions, in: R. Yager et al., eds., Advances in Dempster Shafer Theory of Evidence.Wiley, N.Y. 1994, pp. 251–271.
  • [21] Pawlak, Z.: Some issues on rough sets. Transactions on Rough Sets I 2004, pp. 1–58.
  • [22] Pawlak, Z.: In pursuit of patterns in data reasoning from data – The rough set way. In: [1], 2002, pp. 1–9.
  • [23] Pawlak, Z.: Rough sets and decision algorithms, in: [40], 2001, pp. 30–45.
  • [24] Pawlak, Z.: Flow graphs and decision algorithms, in: [39], 2003, pp. 1–10.
  • [25] Pawlak, Z., Munakata, T.: Rough Control: Application of Rough Set Theory to Control, in: Proc. of the Fourth European Congress on Intelligent Techniques and Soft Computing (EUFIT’96), 2-5 September, 1996, Germany, Verlag Mainz, 1, pp. 209–218.
  • [26] Pedrycz,W.: Fuzzy Control and Fuzzy Systems, 2ˆnd Extended Ed., Taunton, Somerset, UK, 1993.
  • [27] Pedrycz, W., Peters, J.F.: Hierarchical fuzzy controllers: Fuzzy gain scheduling, in: Proc. IEEE Int. Conf. Systems, Man and Cybernetics (SMC’97), Orlando, Florida, 1997, pp. 1139–1143.
  • [28] Pedrycz, W., Peters, J.F., Ramanna, S.: Hierarchical fuzzy neural attitude control for satellites, in: Proc. IEEE Aerospace Conference, Snowmass at Aspen, Colorado, 1997, pp. 385–400.
  • [29] Peters, J. F., Ahn, T. C., Borkowski, M., Degtyaryov, V., Ramanna, S.: Line-crawling robot navigation: A rough neurocomputing approach, in: C. Zhou, D. Maravall, D. Ruan (eds.), Autonomous Robotic Systems. Studies in Fuzziness and Soft Computing 116. Physica-Verlag, Heidelberg, 2003, pp. 141–164.
  • [30] Peters, J.F., Feng, H., Ramanna, S.: Adaptive granular control of an HVDC system: A rough set approach, in: G. Wang, Q. Liu, Y. Yao, A. Skowron (eds.), Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC), LNAI 2639, Berlin, Springer-Verlag, 2003, pp. 213–220.
  • [31] Peters, J.F., Skowron, A., Suraj, Z.: An Application of Rough Set Method to Control Design, Fundamenta Informaticae, Vol. 43, Nos. 1-4, 2000, pp. 269–290.
  • [32] Peters, J.F., Ziaei, K., Ramanna, S.: Approximate time rough control: Concepts and application to satellite attitude control, in: L. Polkowski, A. Skowron (eds.), Rough Sets and Current Trends in Computing, LNAI 1424, Springer-Verlag, 1998, pp. 491–498.
  • [33] Peters, J.F., Ramanna, S.: Framework for approximate time rough control systems: An integrated fuzzy setsrough sets approach, in: Proc. 7th Int. Symposium on Artificial Intelligence in Real-Time Control (AIRTC98), Grand Canyon, Arizona, 1998, pp. 1–8.
  • [34] Peters, J.F., Ziaei, K.: Generating rules iln selecting controller gains: A combined rough sets/fuzzy sets approach, in: Proc. of the Canadian Conf. on Electrical & Computer Engineering (CCECE’98), Waterloo, Ontario, 1998, pp. 233–236.
  • [35] Polkowski, L., Skowron, A.: Introducing rough mereological controllers: rough quality control, in: T.Y. Lin, A.M. Wildberger (eds.), Soft Computing 1995, pp. 240–243.
  • [36] RSES [http://logic.mimuw.edu.pl/ rses/start.html]
  • [37] Rutkowska, D., Pilinski, M., Rutkowski, L.: Neuron networks, genetic algorithms and fuzzy systems, PWN, Warsaw 1997, pp. 97 –115 (in Polish).
  • [38] Tanaka, K.: An Introduction to Fuzzy Logic for Practical Applications, Springer, Berlin 1997.
  • [39] Wang, G. Liu, Q., Yao, Y., Skowron, A. (eds.), Proceedings 9th Int. Conf. on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC2003). Lecture Notes in Artificial Intelligence 2639. Springer-Verlag, Berlin 2003.
  • [40] Ziarko, W., Yao, Y., eds.: Proceedings of 2nd Int. Conf. on Rough Sets and Current Trends in Computing (RSCTC2000), Banff, Canada, 16-19 Oct. 2000. Lecture Notes in Artificial Intelligence, Springer-Verlag, Berlin 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS2-0008-0023
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