Obtención de Trayectorias Empleando el Marco Strapdown INS/KF: Propuesta Metodológica.

Moises J. Castro-Toscano, Julio C. Rodríguez-Quiñonez, Daniel Hernández-Balbuena, Moises Rivas-Lopez, Oleg Sergiyenko, Wendy Flores-Fuentes

Resumen

El estado del arte de los sistemas de posicionamiento ha demostrado que se requiere de redes complejas de sensores y de visión artificial para localizar con precisión objetos móviles en aplicaciones de navegación autónoma. Este documento presenta la metodología para el seguimiento de posición de objetos móviles utilizando Sistemas de Navegación Inercial con Filtro Kalman (INS/KF), en conjunto con la implementación de los algoritmos Zero Velocity Update y Zero Angle Rate Update (ZUPT/ZARUT). La principal contribución de este documento es la propuesta metodológica en la integración del INS-KF-ZUPT/ZARUT o IKZ al INS Strapdown re-alimentado, proporcionando propiedades restrictivas a los errores de deslice y mejorando significativamente la trayectoria, con una mayor definición al movimiento que fue expuesto el objeto. El IKZ propuesto fue probado con datos en bruto de una IMU MPU-9255 con el fin de analizar los diferentes resultados entre pruebas estáticas y movimientos lineales en los ejes X, Y y Z.

Palabras clave

Filtro de Kalman; Navegación; MEMS; INS

Clasificación por materias

Control de máquinas y motores y mecatrónica; Filtrado, estimación y análisis y tratamiento de señales e imágenes

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Referencias

Abdulrahim, K., Hide, C., Moore, T., Hill, C., 2010. Aiding mems imu with building heading for indoor pedestrian navigation. In: Ubiquitous Positio-ning Indoor Navigation and Location Based Service (UPINLBS), 2010. IEEE, pp. 1-6. https://doi.org/10.1109/UPINLBS.2010.5653986

Basaca, L. C., Rodríguez, J., Sergiyenko, O. Y., Tyrsa, V. V., Hernandez, W., Hipolito, J. I. N., Starostenko, O., 2010. Resolution improvement of dyna-mic triangulation method for 3d vision system in robot navigation task. In: IECON 2010-36th Annual Conference on IEEE Industrial Electronics So-ciety. IEEE, pp. 2886-2891. https://doi.org/10.1109/IECON.2010.5675082

Ben, Y., Huang, L., Yang, X., 2016. A rapid damping method for a marine strapdown ins. Ocean Engineering 114, 259-268. https://doi.org/10.1016/j.oceaneng.2016.01.027

Benzerrouk, H., Nebylov, A., 2012. Integrated navigation system ins/gnss ba-sed on joint application of linear and nonlinear filtering. IFAC Proceedings Volumes 45 (1), 208-213. https://doi.org/10.3182/20120213-3-IN-4034.00039

Benzerrouk, H., Nebylov, A., Salhi, H., Closas, P., 2014. Mems imu/zupt based cubature kalman filter applied to pedestrian navigation system. In: Procee-dings of International Electronic Conference on Sensors and Applications. https://doi.org/10.3390/ecsa-1-e002

Bishop, G., Welch, G., 2001. An introduction to the kalman filter. Proc of SIG-GRAPH, Course 8 (27599-23175), 41.

Bortz, J. E., 1971. A new mathematical formulation for strapdown inertial na-vigation. IEEE transactions on aerospace and electronic systems (1), 61-66. https://doi.org/10.1109/TAES.1971.310252

Castro-Toscano, M. J., Rodríguez-Quiñonez, J. C., Hernandez-Balbuena, D., Lindner, L., Sergiyenko, O., Rivas-Lopez, M., Flores-Fuentes, W., 2017. A methodological use of inertial navigation systems for strapdown navigation task. In: Industrial Electronics (ISIE), 2017 IEEE 26th International Sympo-sium on. IEEE, pp. 1589-1595. https://doi.org/10.1109/ISIE.2017.8001484

Fentanes, J. P., Zalama, E., García-Bermejo, J. G., 2012. Plataforma roboticapara tareas de reconstruccionntridimensional de entornos exteriores. Revista Iberoamericana de Automatica e Informatica Industrial RIAI 9 (1), 81-92. https://doi.org/10.1016/j.riai.2011.11.009

Georges, H. M., Wang, D., Xiao, Z., 2015. Gnss/low-cost mems-ins integration using variational bayesian adaptive cubature kalman smoother and ensemble regularized elm. Mathematical Problems in Engineering 2015. https://doi.org/10.1155/2015/682907

Grewal, M. S., Weill, L. R., Andrews, A. P., 2007. Global positioning systems, inertial navigation, and integration. John Wiley & Sons. https://doi.org/10.1002/0470099720

Jimenez,' A. R., Seco, F., Prieto, J. C., Guevara, J., 2010. Indoor pedestrian navigation using an ins/ekf framework for yaw drift reduction and a foot-mounted imu. In: Positioning Navigation and Communication (WPNC), 2010 7th Workshop on. IEEE, pp. 135-143. https://doi.org/10.1109/WPNC.2010.5649300

Kayton, M., Fried, W. R., 1997. Avionics navigation systems, john wiley and sons. Inc., London (UK) 2. https://doi.org/10.1002/9780470172704

Kumar, V., 2004. Integration of inertial navigation system and global positio-ning system using kalman filtering. Ph.D. thesis, INDIAN INSTITUTE OF TECHNOLOGY, BOMBAY MUMBAI.

Lee, J. G., Park, C. G., Park, H. W., 1993. Multiposition alignment of strapdown inertial navigation system. IEEE Transactions on Aerospace and Electronic systems 29 (4), 1323-1328. https://doi.org/10.1109/7.259535

Li, K., Gao, P., Wang, L., Zhang, Q., 2015a. Analysis and improvement of at-titude output accuracy in rotation inertial navigation system. Mathematical Problems in Engineering 2015. https://doi.org/10.1155/2015/768174

Li, Q., Ban, Y., Niu, X., Zhang, Q., Gong, L., Liu, J., 2015b. E ciency impro-vement of kalman filter for gnss/ins through one-step prediction of matrix. Mathematical Problems in Engineering 2015. https://doi.org/10.1155/2015/109267

Lin, Y., Zhang, W., Xiong, J., 2015. Specific force integration algorithm with high accuracy for strapdown inertial navigation system. Aerospace Science and Technology 42, 25-30. https://doi.org/10.1016/j.ast.2015.01.001

Lindner, L., Sergiyenko, O., Rodríguez-Quiñonez, J. C., Rivas-Lopez, M., Hernandez-Balbuena, D., Flores-Fuentes, W., Murrieta-Rico, F. N., Tyrsa, V., Loughlin, C., Loughlin, C., 2016. Mobile robot vision system using con-tinuous laser scanning for industrial application. Industrial Robot: An Inter-national Journal 43 (4). https://doi.org/10.1108/IR-01-2016-0048

Milanes, V., Naranjo, J., Gonzalez, C., Alonso, J., García, R., de Pedro, T., 2008. Sistema de posicionamiento para vehículos autonomos. Revista Ibe-roamericana de Automatica e Informatica Industrial RIAI 5 (4), 36-41. https://doi.org/10.1016/S1697-7912(08)70175-4

Miller, R. B., 1983. A new strapdown attitude algorithm. Journal of Guidance, Control, and Dynamics 6 (4), 287-291. https://doi.org/10.2514/3.19831

Real-Moreno, O., Rodriguez-Quinonez, J. C., Sergiyenko, O., Basaca-Preciado, L. C., Hernandez-Balbuena, D., Rivas-Lopez, M., Flores-Fuentes, W., 2017. Accuracy improvement in 3d laser scanner based on dynamic triangulation for autonomous navigation system. In: Industrial Electronics (ISIE), 2017 https://doi.org/10.1109/ISIE.2017.8001486

IEEE 26th International Symposium on. IEEE, pp. 1602-1608. Rodr'ıguez-Quinonez,˜ J., Sergiyenko, O., Flores-Fuentes, W., Rivas-lopez, M., Hernandez-Balbuena, D., Rascon,' R., Mercorelli, P., 2017. Improve a 3d distance measurement accuracy in stereo vision systems using optimization methods approach. Opto-Electronics Review 25 (1), 24-32. https://doi.org/10.1016/j.opelre.2017.03.001

Ronnback, S., 2000. Developement of a ins/gps navigation loop for an uav. Ruiz, A. R. J., Granja, F. S., Honorato, J. C. P., Rosas, J. I. G., 2010. Pedestrian indoor navigation by aiding a foot-mounted imu with rfid signal strength measurements. In: Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on. IEEE, pp. 1-7.

Savage, P. G., 1998. Strapdown inertial navigation integration algorithm de-sign part 1: Attitude algorithms. Journal of guidance, control, and dynamics 21 (1), 19-28. https://doi.org/10.2514/2.4228

Seifert, K., Camacho, O., 2007. Implementing positioning algorithms using ac-celerometers. Freescale Semiconductor.

Sorenson, H. W., 1970. Least-squares estimation: from gauss to kalman. IEEE spectrum 7 (7), 63-68. https://doi.org/10.1109/MSPEC.1970.5213471

Titterton, D., Weston, J. L., 2004. Strapdown inertial navigation technology. Vol. 17. IET. https://doi.org/10.1049/PBRA017E

Wang, Z., Zhao, H., Qiu, S., Gao, Q., 2015. Stance-phase detection for zupt-aided foot-mounted pedestrian navigation system. IEEE/ASME Transac-tions on Mechatronics 20 (6), 3170-3181. https://doi.org/10.1109/TMECH.2015.2430357

Woyano, F., Lee, S., Park, S., 2016. Evaluation and comparison of performance analysis of indoor inertial navigation system based on foot mounted imu. In: Advanced Communication Technology (ICACT), 2016 18th International Conference on. IEEE, pp. 792-798. https://doi.org/10.1109/ICACT.2016.7423562

Xu, Y., Chen, X., Li, Q., 2014. Adaptive iterated extended kalman filter and its application to autonomous integrated navigation for indoor robot. The Scientific World Journal 2014. https://doi.org/10.1155/2014/138548

Zampella, F., Khider, M., Robertson, P., Jimenez,' A., 2012. Unscented kal-man filter and magnetic angular rate update (maru) for an improved pe-destrian dead-reckoning. In: Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION. IEEE, pp. 129-139. https://doi.org/10.1109/PLANS.2012.6236874

Zhang, X., Liu, P., Zhang, C., 2016. An integration method of inertial navigation system and three-beam lidar for the precision landing. Mathematical Problems in Engineering 2016. https://doi.org/10.1155/2016/4892376

Zhang, X., Yin, J., Lin, Z., Zhang, C., 2015. A positioning and orientation met-hod based on the usage of ins and single-beam lidar. Optik-International Journal for Light and Electron Optics 126 (22), 3376-3381. https://doi.org/10.1016/j.ijleo.2015.06.066

Zhi, R., 2016. A drift eliminated attitude & position estimation algorithm in 3d.

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