Autonomous location system based on propioceptive sensor fusion for mobile robots
Keywords:
Robótica, Localización, Fusión Sensorial, Robot Móvil, Localización de un Robot Móvil, Filtro de Kalman Extendido, Estimación de PosiciónAbstract
The main objective of this study is to develop an autonomous localization system capable of delivering better position estimates compared to an exclusively odometer system by means of a sensor fusion algorithm. A mobile robot travels a pre-programmed path to provide sensory data to the system. A fusion architecture is define that works with odometers, accelerometers and gyroscope data. The robot movement model, the measurement model and the sensory data are using an Extended Kalman Filter. The results show that in all the cases that were evaluated the system records an improvement of 38% compared to a standard deterministic localization system. The data show that the θ variable is the most influential in the process. In conclusion, the results satisfy the stated objective, nevertheless, it can be improved by incorporating additional sensors and adjusting the uncertainty matrices R and Q.Article Metrics
Abstract: 672 PDF (Español (España)): 1857References
S. Thrun, W. Burgard, y D. Fox. "Probabilistic Robotics". MIT press. Massachusetts, EEUU. 647p. 2006.
F. E. White. "Data fusion lexicon". The Joint Directors of Laboratories, Technical panel for C3 NOSC. 1991.
H. Boström, S. F. Andler, M. Brohede, R. Johansson, A. Karlsson, J. Van Laere, y T. Ziemke. "On the definition of information fusion as a field of research". University of Skövde, School of Humanities and Informatics. 2007.
J. R. Raol. "Multi-Sensor Data Fusion with MATLAB". CRC Press. Boca Raton, EEUU. 570p. 2010.
J. Z. Sasiadek, A. Monjazeb, y D. Necsulescu. "Navigation of an autonomous mobile robot using EKF-SLAM and FastSLAM". Control and Automation, 2008 16th Mediterranean Conference, IEEE, 517-522. 2008.
H. Zhou, y S. Sakane. "Sensor Planning for Mobile Robot Localization, A Hierarchical Approach Using a Bayesian Network and a Particle Filter". Robotics, IEEE Transactions on, 24(2), 481-487. 2008.
L. Teslic, I. Skrjanc, y G. Klancar. "EKF-based localization of a wheeled mobile robot in structured environments". Journal of Intelligent and Robotic Systems, 62(2), 187-203. 2011.
J. A. López. "Integración y fusión multisensorial en robots móviles autónomos". Tesis de Doctorado. Universidad Complutense de Madrid, España. 370p. 1998.
L. Marín. "Navegación de un robot móvil de configuración diferencial basada en fusión sensorial". Tesis de Maestría. Universidad Politécnica de Valencia. España. 84p. 2012.
R. Negenborn. "Robot localization and Kalman filters - On finding your position in a noisy world". Tesis de Maestría. Utrecht University. Holanda. 156p. 2003.
Khaleghi, B., Khamis, A., Karray, F. O., y Razavi, S. N. "Multisensor data fusion: A review of the state-of-the-art." Information Fusion. 2013.
Yoon, S. W., Park, S. B., & Kim, J. S. “Kalman Filter Sensor Fusion for Mecanum Wheeled Automated Guided Vehicle Localization”. Journal of Sensors. 2015.
Zhou, B., Qian, K., Fang, F., Ma, X., & Dai, X. “Multi-sensor fusion robust localization for indoor mobile robots based on a set-membership estimator”. In Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on (pp. 157-162). IEEE.
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