Hostname: page-component-77c89778f8-m42fx Total loading time: 0 Render date: 2024-07-19T01:23:45.487Z Has data issue: false hasContentIssue false

USV attitude estimation: an approach using quaternion in direction cosine matrix

Published online by Cambridge University Press:  31 July 2014

Chiemela Onunka*
Affiliation:
Discipline of Mechanical Engineering, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
Glen Bright
Affiliation:
Discipline of Mechanical Engineering, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
Riaan Stopforth
Affiliation:
Discipline of Mechanical Engineering, University of KwaZulu-Natal, KwaZulu-Natal, South Africa
*
*Corresponding author. Email: connadoz@gmail.com

Summary

Positioning and navigation data for unmanned surface vehicles (USVs) are extracted using the Global Positioning System (GPS) and the Inertial Navigation System (INS) integrated with an inertial measurement unit (IMU). The integration of quaternion with direction cosine matrix (DCM) with the aim of obtaining high accuracy with complete system independence has been effectively used to supply position and attitude information for autonomous navigation of marine crafts. A DCM integrated with a quaternion provided an advanced technique for precise USV attitude estimation and position determination using low-cost sensors. This paper presents the implementation of an INS developed by the integration of DCM and quaternion.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Noureldin, A., El-shafie, A. and Taha, M. R., “Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation,” Eng. Appl. Artif. Intell. 20 (1), 4961 (2007).Google Scholar
2.Loebis, D., Sutton, R., Chudley, J. and Naeem, W., “Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system,” Control Eng. Pract. 12 (12), 15311539 (2004).Google Scholar
3.Lee, G., Surendran, S. and Kim, S.-H., “Algorithms to control the moving ship during harbour entry,” Appl. Math. Model. 33 (5), 24742490 (2009).Google Scholar
4.Wendel, J., Meister, O., Schlaile, C. and Trommer, G. F., “An integrated GPS/MEMS-IMU navigation system for an autonomous helicopter,” Aerosp. Sci. Technol. 10 (6), 527533 (2006).Google Scholar
5.Bitner-Gregersen, E. M. and Skjong, R., “Concept for a risk based navigation decision assistant,” Mar. Struct. 22 (2), 275286 (2009).Google Scholar
6.Caron Francois, D. E., Denis, P. and Philippe, V., “GPS/IMU data fusion using multisensor Kalman filtering: Introduction of contextual aspects,” Inf. Fusion 7 (12), 221230 (2006).Google Scholar
7.Kong, X., “INS algorithm using quaternion model for low cost IMU,” Robot. Auton. Syst. 46 (1), 221246 (2004).Google Scholar
8.de La Parra, S. and Angel, J., “Low-cost navigation system for UAV's,” Aerosp. Sci. Technol. 9 (6), 504516 (2005).Google Scholar
9.Onunka, C. and Bright, G.. “A Study on Direction Cosine Matrix (DCM) for Autonomous Navigation,” Proc: 25th International Conference on CAD/CAM, Robotic & Factories of the Future, Cars & FoF 2010, Pretoria, South Africa 1–10, 13–16 July, (2010).Google Scholar
10.Mark Euston, P. C., Mahony, R., Kim, J. and Hamel, T., “A Complementary Filter for Attitude Estimation of a Fixed-Wing UAV,” in Proceedings of the International Conference on Intelligent Robots and Systems (IROS 2008), Nice,. 340–345, 22–26 Sept (2008).Google Scholar
11.Robert Mahony, T. H. and Pflimlin, J.-M., “Nonlinear complementary filters on the special orthogonal group,” IEEE Trans. Autom. Control 53 (5), 12031218, (2008).Google Scholar
12.Grant Baldwin, R. M., Trumpf, J., Hamel, T. and Cheviron, T., “Complementary Filter Design on the Special Euclidean Group SE(3),” Proceedings of the European Control Conference (ECC 2007), Kos, Greece, 1–8, 2–5 July, (2007).Google Scholar
13.Robert Mahony, S.-H. C. and Hamel, T., “A Coupled Estimation and Control Analysis for Attitude Stabilisation of Mini Aerial Vehicles,” Proceedings of the Australasian Conference on Robotics and Automation, Auckland, New Zeland, 1–10, 8 December, (2006).Google Scholar
14.Wagner, J. F. and Wieneke, T., “Integrating satellite and inertial navigation–conventional and new fusion approaches,” Control Engineering Practice 11 (5), 543550 (2003).CrossRefGoogle Scholar
15.Stancic, R. and Graovac, S., “The integration of strap-down INS and GPS based on adaptive error damping,” Robot. Auton. Syst., 58 (10), 11171129 (2010).Google Scholar
16.Bijker, J. and Steyn, W., “Kalman filter configurations for a low-cost loosely integrated inertial navigation system on an airship,” Control Eng. Pract. 16 (12), 15091518 (2008).Google Scholar
17.Wang, W., Liu, Z.-Y. and Xie, R.-R.., “Quadratic extended Kalman filter approach for GPS/INS integration,” Aerosp. Sci. Technol. 10 (8), 709713 (2006).Google Scholar
18.William Premerlani, P. B., “Direction Cosine Matrix IMU: Theory,” (2009). Available: http://gentlenav.googlecode.com/files/DCMDraft2.pdf, Accessed: 20 September 2009.Google Scholar
19.Fossen, T. I., Guidance and Control of Ocean Vehicles (John Wiley, West Sussex, UK, 1994).Google Scholar
20.Rigatos, G. G., “Sensor fusion-based dynamic positioning of ships using extended Kalman and particle filtering,” Robotica 31 (3), 386403 (2013).Google Scholar
21.Fahimi, F. and Van Kleeck, C., “Alternative trajectory-tracking control approach for marine surface vessels with experimental verification,” Robotica 31 (1), 2533 (2012).Google Scholar
22.Soltan, R. A., Ashrafiuon, H. and Muske, K. R., “ODE-based obstacle avoidance and trajectory planning for unmanned surface vessels,” Robotica 29 (5), 691703 (2011).Google Scholar
23.Sharma, S. K., Naeem, W. and Sutton, R., “An autopilot based on a local control network design for an unmanned surface vehicle,” J. Navig. 65 (2), (The Royal Institute of Navigation), 281301 (2012).Google Scholar
24.Motwani, A., Sharma, S. K., Sutton, R. and Culverhouse, P., “Interval Kalman filtering in navigation system design for an uninhabited surface vehicle,” J. Navig. 66 (5), (The Royal Institute of Navigation), 639652 (2013).Google Scholar
25.Hong, S., Lee, M. H., Chun, H.-H., Kwon, S.-H. and Speyer, J. L., “Observability of error states in GPS/INS integration,” IEEE Trans. Veh. Technol. 54 (2), 731743 (2005).Google Scholar
26.Kim, J., Lee, J.-G., Jee, G.-I. and Sung, T.-K., “Compensation of Gyroscope Errors and GPS/DR Integration,” Proceedings of the IEEE Positioning Location and Navigation Symposium, Atlanta, GA, 464–470, 22–26, April (1996).Google Scholar
27.Han, S. and Wang, J., “Quantization and coloured noise error modelling for inertial sensors for GPS/INS integration,” IEEE Sensors J. 11 (6), 14931503 (2011).CrossRefGoogle Scholar
28.Kralikova, E. and Ravas, R., “Analysis of the Influence of Quantization Error at Sensor Autocalibration,” Proceedings of the 13th International Symposium on MECHATRONIKA, Trencianske Teplice, 74–77, 2–4 June, (2010).Google Scholar
29.Stocco, L., Salcudean, S. E. and Sassani, F., “Matrix Normalization for Optimal Robot Design,” Proceedings of the IEEE International Conference on Robotics and Automation, Leuven, 1346–1351, 16–20 May, (1998).Google Scholar