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Vol: 51(65) No: 3 / September 2006 Circular Scanning Approach for Robot State Representation Anca Petrisor University of Craiova, Faculty of Electromechanical Engineering, Bd. Decebal, nr.107, 200440 Craiova, Romania, phone: +40-251-435255, e-mail: apetrisor@em.ucv.ro Sonia Degeratu University of Craiova, Faculty of Engineering and Management of Technological Systems, Florin Ravigan University of Craiova, Faculty of Electromechanical Engineering, Bd. Decebal, nr.107, 200440 Craiova, Romania Daniela Coman Faculty of Engineering and Management of Technological Systems, Drobeta Turnu-Severin, Drobeta Turnu-Severin, Romania Dumitru Cazacu University of Pitesti, Faculty of Electronics, Telecommunications and Computer Science, Targul din vale, 110040, Pitesti, Romania Keywords: mobile robot, local and recent space, control algorithms. Abstract This paper presents a method to plan the robot evolution trajectory among obstacles, based on circular scanning. It is taken to consideration the mathematical model of the mobile robot in absolute coordinates, incorporated in the local and recent geometry determined by the evolution in an environment. The uncertainty is modeled considering the fuzzy description of the mobile robot evolution. It is also considered an evolution scene where there are a lot of objects represented by cells area of objects. Each number is represented by a set of complex number. By a circular scanning of the evolution environment it is obtained the signature in local and recent geometry. Based on this signature it follows to establish the evolution trajectory. A package of programs in MATLAB environment is used to obtain the signature that gives the robot position in local and recent geometry. References [1] A. Petrisor, “Algorithms for walking robots control under the existence of uncertain dynamic models” PhD-Thesis, Craiova, 2004. [2] C. Marin and A. Petrisor, “Fuzzy dynamic systems for mobile robots control”, Proc. 2nd National Workshop on Mobile Robots WMRC-2001, Craiova, 2001. [3] C. Marin and A. Petrişor, “Fuzzy functional sets for mobile robot control”, Conferinţa Naţională de Robotică, Craiova, 2002. [4] R.A. Brooks, “Intelligence without representation”, Proc. Workshop on the Foudations of AI, MIT, Cambridge, MA, 1987. [5] J.R. Firby, “An investigation into reactive planning in complex domains”, Proc. AAAI Conf., 1987. [6] M.J. Schoppers “Universal plans for reactive robots in unpredictable environments”, In Procs. of the Int. Joint Conf. on Artificial Intelligence, pages 1039-1046, 1987. [7] A. Saffiotti, E.H. Ruspini and K. Konolige, “Integrating reactivity and goal-directedness in a fuzzy controller”, Proc. 2nd Fuzzy IEEE Conference, San Francisco, CA, 1993. [8] A. Saffiotti, E.H. Ruspini and K. Konolige, “Robust Execution of Robot Plans Using Fuzzy Logic”, Proceedings of the IJCAI-93 Workshop on Fuzzy Logic in Artificial Intelligence (IJCAI93_FUZZYLOGIC), Chamberry, France, 1993. 24-37 [9] A. Saffiotti, E.H. Ruspini and K. Konolige, “A fuzzy controller for flakey, an autonomous mobile robot”, Technical Report 529, SRI Artificial Intelligence Center, Menlo Park, California, 1993. [10] J.R. Firby, “Adaptive execution in complex dynamic worlds”, Technical Report 672, Dept. Of Computer Science, Yale University, 1989.McDermott D., Planning reactive behavior: A progress report, In Proceedings of the DARPA Workshop on Innovaive Approaches to Planning Scheduling, and Control1990. [11] J. Connell, “Minimalist Mobile Robotics: A Colony-style Architecture for an Artificial Creature”, Academic Press, 1990. |