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Vol: 61(75) No: 1 / March 2016 Predictive Distributed Formation Control for Swarm Robots Using Mobile Agents Claudiu Radu Pozna Transylvania University Brasov, B-dul Eroilor nr.29, 500036 Brasov, Romania, phone: +36 (96) 613-652 348, e-mail: pozna@sze.hu Erno Horvath Széchenyi István University, Faculty of Computer Engineering, Egyetem sq. 1. H-9026 Győr, Hungary, phone: +36 (96) 613-652 337, e-mail: herno@sze.hu, web: http://www.sze.hu/~herno/ Keywords: LIDAR, laser scanner, Sick S300, mobile robots Abstract The laser scanner is widely used proximity sensor in the filed of autonomous vehicles and robots. The laser scanner works on the time-of-flight principle so it measures the time when a laser beam is emitted and received thus the distance can be calculated. In practice it laser scanner often used as a synonim of lidar which term is created as a portmanteau of \"light\" and \"radar\". In this paper a special kind of laser scanner is used where the measurements are a group of 540 distance measurements obtained from different firing angles. This laser scanner is the Sick S300 which has a 270° scan angle in an angular resolution of 0.5°. This paper describes an abstraction of this group of measurements form 540 to 3 which still can be used in simple navigation tasks. This paper presents the definition of the abstraction and a use-case where the mentioned abstraction can be simulated. References [1] M. Kam, Z. Zhu, P. Kalata, Sensor fusion for mobile robot navigation, Proceedings of the IEEE, vol. 85, pp. 108–119, 1997. [2] J.Z. Sasiadek, P. Hartana, Sensor data fusion using Kalman filter, Proc. 3rd International Conference on Information Fusion (FUSION 2000), Paris, France, 2000, vol. 2, pp. WED5/19–WED5/25. [3] L. Drolet, F. Michaud, J. Cote, Adaptable sensor fusion using multiple Kalman filters, Proc. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000), Takamatsu, Japan, 2000, vol. 2, pp. 1434–1439. [4] M.S. Arulampalam, S. Maskell, N. Gordon, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol. 50, pp. 174–188, 2002. [5] M. Rosencrantz, G. Gordon, S. Thrun, Decentralized sensor fusion with distributed particle filters, Proc. 19th Conference on Uncertainty in Artificial Intelligence (UAI’03), Acapulco, Mexico, 2003, pp. 493–500. [6] C. Pozna, R.-E. Precup, J.K. Tar, I. Škrjanc, S. Preitl, New results in modelling derived from Bayesian filtering, Knowledge-Based Systems 23 (2010) pp. 182–194. [7] G.G. Rigatos, Extended Kalman and particle filtering for sensor fusion in motion control of mobile robots, Mathematics and Computers in Simulation, vol. 81, pp. 590–607, 2010. [8] A. Elfes, Using occupancy grids for mobile robot perception and navigation, Computer, vol. 22 (1989) 46–57. [9] X. Zhou, Y.K. Ho, C.S. Chua, Y. Zou, The localization of mobile robot based on laser scanner, in: Proc. 2000 Canadian Conference on Electrical and Computer Engineering, Halifax, NS, Canada, 2000, vol. 2, pp. 841–845. [10] H. Surmann, A. Nüchter, J. Hertzberg, An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments, Robotics and Autonomous Systems, vol. 45, pp. 181–198, 2003. [11] R.-E. Precup, M.-B. Radac, M.L. Tomescu, E.M. Petriu, S. Preitl, “Stable and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO systems,” Expert Syst. Appl., vol. 40, no. 1, pp. 188–199, Jan. 2013 |