Development of a low-cost GPS/INS integrated system for tractor automatic navigation
Keywords:
global positioning system, tractor, automatic navigation, sensor fusion, Kalman filter, inertial sensor, heading angleAbstract
The use of low-cost single GPS receivers and inertial sensors for auto-guidance applications has been limited by their reduced accuracy and signal drift over time compared to real-time kinematic (RTK) differential GPS units and fiber-optic gyroscope (FOG) sensors. In this study, a prototype low-cost GPS/INS integrated system consisting of a triangle-shaped array of three Garmin 19x GPS receivers and an Xsens inertial measurement unit (IMU) to improve the accuracy of position and heading angle measured with a single GPS receiver was developed. A triangular algorithm that uses data collected from the three single GPSs mounted on the angular points of a triangular frame was designed. A sensor fusion algorithm based on the Kalman filter combining the GPS and IMU data was developed by integrating position data and heading angles of a triangular array of GPS receivers. The optimized values of two noise covariance matrixes (Q and R) for the Kalman filtering were determined using the Central Composite Design (CCD) method. As compared to the use of a single Garmin GPS receiver, use of the developed GPS/INS system showed improved accuracy performance in terms of both position and heading angle, with reductions in root mean square errors (RMSEs) from 2.7 m to 0.64 m for position and from 8.9º to 2.1º for heading angle. The accuracy improvements show new potential for agricultural auto-guidance applications. Keywords: global positioning system, tractor, automatic navigation, sensor fusion, Kalman filter, inertial sensor, heading angle DOI: 10.3965/j.ijabe.20171002.3070 Citation: Han X Z, Kim H J, Jeon C W, Moon H C, Kim J H. Development of a low-cost GPS/INS integrated system for tractor automatic navigation. Int J Agric & Biol Eng, 2017; 10(2): 123–131.References
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[2] Zhang N, Wang M, Wang N. Precision agriculture: a worldwide overview. Computers and Electronics in Agriculture, 2002; 36(2): 113–132.
[3] Auernhammer H. Precision farming: the environmental challenge. Computers and Electronics in Agriculture, 2001; 30(1): 31–43.
[4] Keicher R, Seufert H. Automatic guidance for agricultural vehicles in Europe. Computers and Electronics in Agriculture, 200; 25(1): 169–194.
[5] Mizushima A, Noguchi N, Ishii K, Terao H. Development of navigation sensor unit for the agricultural vehicle. In Proceedings of the Advanced Intelligent Mechatronics, 2003; 1067–1072.
[6] Li M, Imou K, Wakabayashi K, Yokoyama S. Review of research on agricultural vehicle autonomous guidance. Int J
Agric & Biol Eng, 2009; 2(3): 1–16.
[7] Shearer S A, Pitla S K, Luck J D. Trends in the automation of agricultural field machinery. In Proceedings of the 21st Annual Meeting of the Club of Bologna. Italy, 2010.
[8] Cappelle C, Pomorski D, Yang Y. GPS/INS data fusion for land vehicle localization. In Proceedings of the Computational Engineering in Systems Applications, 2006; 1: 21–27.
[9] Dissanayake G, Sukkarieh S, Nebot E, Durrant-Whyte H. The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications. IEEE Transactions on Robotics and Automation, 2001; 17(5): 731–747.
[10] Noureldin A, Karamat T B, Eberts M D, El-Shafie A. Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications. IEEE Transactions on Vehicular Technology, 2009; 58(3): 1077–1096.
[11] Farrell J A, Givargis T D, Barth M J. Real-time differential carrier phase GPS-aided INS. IEEE Transactions on Control Systems Technology, 2000; 8(4): 709–721.
[12] Guo L, Zhang Q, Feng L. A low-cost integrated positioning system of GPS and inertial sensors for autonomous agricultural vehicles. In Proceedings of the American Society of Agricultural Engineers, 2003; Paper No. 033112.
[13] Guo L, Zhang Q. A low-cost integrated positioning system for autonomous off-highway vehicles. Journal of Automobile Engineering, 2008; 222(11): 1997–2009.
[14] Xiang H, Tian L. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosystems Engineering, 2011; 108(2): 174–190.
[15] Kownacki C. Optimization approach to adapt Kalman filters for the real-time application of accelerometer and gyroscope signals’ filtering. Digital Signal Processing, 2011; 21(1): 31–140.
[16] Gomez-Gil J, Ruiz-Gonzalez R, Alonso-Garcia S, Gomez-Gil F J. A kalman filter implementation for precision improvement in low-cost GPS positioning of tractors. Sensors, 2013; 13(11): 15307–15323.
[17] Leung K T, Whidborne J F, Purdy D, Barber P. Road vehicle state estimation using low-cost GPS/INS. Mechanical Systems and Signal Processing, 2011; 25(6): 1988–2004.
[18] Garmin GPS 19x HVS technical specifications. http://www.fo ndriest.com/pdf/garmin_19xhvs_spec.pdf.
[19] Enge P, Walter T, Pullen S, Kee C D, Chao Y C, Tsai Y J. Wide area augmentation of the global positioning system. In Proceedings of the IEEE, 1996; 84(8): 1063–1088.
[20] Sorenson H W. Kalman filtering: theory and application. IEEE Press, 1985.
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Published
2017-03-31
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Han, X., Kim, H.-J., Jeon, C. W., Moon, H. C., & Kim, J. H. (2017). Development of a low-cost GPS/INS integrated system for tractor automatic navigation. International Journal of Agricultural and Biological Engineering, 10(2), 123–131. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3070
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Information Technology, Sensors and Control Systems
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