**6.1 Preliminary of basin test**

In its underwater mission, the vehicle is always forced to keep zero pitch angle. And in the horizontal plane, we design the vehicle's reference path to be always parallel to the axis X or axis Y, see Fig. 12. In this case, at any point, the vehicle's position can be easily got through simple rotation mode. However, considering the fact that the vehicle does not keep at the same point through its rotation, in other word, there is a drift for the vehicle's position in the rotating mode. So, though the accuracy of range sonar measurement is in the centimetres level, the total position error for this kind of rotation mode is significant. Through a number of basin tests, we observe that this kind of position error is up to 0.5m.

Consider this kind of forward/backward motion; the vehicle's forward/backward velocity can be calculated using range sonar measurements. For this purpose, the following filter is designed for acquisition of range sonar raw measurements

$$d\_{FR}(k) = (1 - 2^{-n})d\_{FR}(k - 1) + 2^{-n}d\_R(k),\tag{6}$$

where ��� and �� denote each of filtered and raw measurements of range sonar, and � is filtering order. The filtering results can be seen in Fig. 14.

Fig. 14. Calculated forward speeds.

32 Autonomous Underwater Vehicles

To demonstrate the proposed vision-based underwater localization and the SLAM methods,

In its underwater mission, the vehicle is always forced to keep zero pitch angle. And in the horizontal plane, we design the vehicle's reference path to be always parallel to the axis X or axis Y, see Fig. 12. In this case, at any point, the vehicle's position can be easily got through simple rotation mode. However, considering the fact that the vehicle does not keep at the

we carried out a series of field tests in the engineering basin in PIRO.

Fig. 12. Acquisition of 2D profile image.

Fig. 13. Acquisition of 3D profile image.

**6.1 Preliminary of basin test**

**6. Basin test** 

Fig. 15. Comparison of heading measurements

Development of a Hovering-Type Intelligent Autonomous Underwater Vehicle, P-SURO 37

Jiang, J. P. (2002). Global tracking control of underactuated ships by Lyapunov's direct

Marthiniussen, R., Vestgard, K., Klepaker, R., Storkersen, N. (2004). HUGIN-AUV concept

Leonard, J. & Durrant-Whyte, H. (1992). *Directed Sonar Sensing for Mobile Robot Navigation*.

Li, J. H., Jun, B. H., Lee, P. M., Hong, S. K. (2005). A hierarchical real-time control

Li, J. H., Lee, P. M., Jun, B. H., Lim, Y. K. (2008a). *Underwater Vehicle*, InTech, ISBN 978-953-

Li, J. H., Lee, P. M., Jun, B. H., Lim, Y. K. (2008b). Point-to-point navigation of underactuated

Li, J. H., Yoon, B. H., Oh, S. S., Cho, J. S., Kim, J. G., Lee, M. J., Lee, J. W. (2010). Development

Oh, S. S., Yoon, B. H., Li, J. H. (2010). Vision-based localization for an intelligent AUV,

Peuch, A., Coste, M. E., Baticle, D., Perrier, M., Rigaud, V., Simon, D. (1994). And advanced

Prestero, T. (2001). *Verification of a six-degree of freedom simulation model for the REMUS* 

Quek, C & Wahab, A. (2000). Real-time integrated process supervision. *Engineering* 

Simon, D., Espiau, B., Castillo, E., Kapellos, K. (1993). Computer-aided design of a generic

Valavanis, K. P., Hebert, T., Kolluru, R., Tsourveloudis, N. C. (2000). Mobile robot

Wang, H. H., Marks, R. L., Rock, S. M., Lee, M. J. (1993). Task-based control architecture for

Zhang, Z. (2000). A flexible new technique for camera calibration. *IEEE Transactions* 

and operational experience to date, *Proceedings of IEEE/MTS Oceans'04*, pp. 846-850,

architecture for a semi-autonomous underwater vehicle. *Ocean Engineering*, Vol.32,

of an Intelligent Autonomous Underwater Vehicle, P-SURO, *Proceedings of* 

P-SURO, *Proceedings of KAOSTS Annual conference*, pp. 2602-2605, Jeju, Korea,

control architecture for underwater vehicles, *Proceedings of Oceans'94*, Brest, France,

robot controller handling reactivity and real-time control issues. *IEEE Transactions* 

navigation in 2-D dynamic environments using electrostatic potential fields. *IEEE Transactions on Systems, Man and Cybernetics-part A*, Vol.30, pp. 187-197,

an untethered, unmanned submersible, *Proceedings of the 8th Symposium on Unmanned Untethered Submersible Technology*, Durham, New Hampshire, pp. 137-

*on Pattern Analysis and Machine Intelligence*, Vol. 22, No. 11, pp. 1330-1334,

method. *Automatica*, Vol.40, pp. 2249-2254, 2002

London: Kluwer Academic Publishers, 1992

ships. *Automatica*, Vol. 44, pp. 3201-3205, 2008

*Oceans'10 IEEE Sydney*, Sydney, Australia, May 24-27, 2010

*autonomous underwater vehicles*, Masters Thesis, MIT, USA

*Applications of Artificial Intelligence*, Vol.13, pp. 645-658, 2000

Thrun, S., Burgard, W., Fox, D. (2005). *Probabilistic Robotics*. The MIT Press, 2005

*on Control Systems Technology*, Vol.1, pp. 213-229, 1993

Kobe, Japan, November 9-12, 2004

pp. 1631-1641, 2005

pp. I-590-595, 1994

2010

2000

148, 1993

2000

7619-49-7, Vienna, Austria

AUV, we observed that the proposed technologies provided satisfactory accuracy for the autonomous navigation of hovering-type AUV in the basin.

However, through the basin tests, we also observed that proposed vision algorithm was somewhat overly sensitive to the environmental conditions. How to improve the robustness of underwater vision is one of great interest in our future works. Besides, developing certain low-cost underwater navigation technology with partially known environmental conditions is also one of our future concerns.
