**7.1 Further work**

Several distinct areas of research, based on the developed HILS framework, remain to ascertain the quality of the presented virtual potential-based decentralized cooperative framework. These are necessary in order to clear the framework for application in costly and logistically demanding operations in the real Ocean environment.

1. Realistically model the representation of knowledge of the other AUVs aboard each AUV locally.

This can be approached on several fronts:


Clegg, D. & Peterson, M. (2003). User operational evaluation system of unmanned underwater

Formation Guidance of AUVs Using Decentralized Control Functions 131

Curtin, T., Bellingham, J., Catipovic, J. & Webb, D. (1993). Autonomous oceanographic

Duda, R. O. & Hart, P. E. (1972). Use of the Hough transformation to detect lines and curves

Faucette, W. M. (1996). A geometric interpretation of the solution of the general quartic

Gertler, M. & Hagen, G. (1967). Standard Equations of Motion for Submarine Simulation,

Haule, D. & Malowany, A. (1989). Object recognition using fast adaptive Hough transform,

He, Y. & Li, Z. (2008). An effective approach for multi-rectangle detection, *Young Computer Scientists 2008. ICYCS. The 9th International Conference for*, pp. 862 –867. Healey, A. & Lienard, D. (1993). Multivariable sliding mode control for autonomous diving

Hough, P. & Powell, B. (1960). A method for faster analysis of bubble chamber photographs,

Illingworth, J. & Kittler, J. (1987). The adaptive Hough transform, *Pattern Analysis and Machine*

Jiang, X., Huang, X., Jie, M. & Yin, H. (2007). Rock detection based on 2D maximum entropy

Jung, C. & Schramm, R. (2004). Rectangle detection based on a windowed Hough transform,

Lienard, D. (1990). *Sliding Mode Control for Multivariable AUV Autopilots*, Master's thesis, Naval

Maitre, H. (1986). Contribution to the prediction of performances of the Hough transform, *Pattern Analysis and Machine Intelligence IEEE Transactions on* PAMI-8(5): 669 –674. Marco, D. & Healey, A. (2000). Current developments in underwater vehicle control

Marco, D. & Healey, A. (2001). Command control and navigation experimental results with the NPS Aries AUV, *IEEE Journal of Oceanic Engineering* 26(4): 466–476. Nguyen, T., Pham, X. & Jeon, J. (2009). Rectangular object tracking based on standard Hough

Pang, S. (2006). Development of a guidance system for AUV chemical plume tracing, *OCEANS*

*Technical report*, David W. Taylor Naval Ship Research and Development Center

*Communications Computers and Signal Processing 1989. Conference Proceedings IEEE*

and steering of unmanned underwater vehicles, *Oceanic Engineering IEEE Journal of*

thresholding segmentation and ellipse fitting, *Robotics and Bioimetics 2007. ROBIO*

*Computer Graphics and Image Processing 2004. Proceedings, 17th Brazilian Symposium on*,

and navigation: the NPS Aries AUV, *OCEANS Conference record (IEEE)*, Vol. 2,

transform, *Robotics and Biomimetics 2008. ROBIO IEEE International Conference on*,

Eisman, D. (2003). Navy ships seize boats carrying mines in Iraqi port, *The Virginian-pilot* . Farrell, J., S., P. & Li, W. (2005). Chemical plume tracing via an autonomous underwater

*Record (IEEE)*, Vol. 3, pp. 1417–1423.

*Pacific Rim Conference on*, pp. 91 –94.

Vol. 18, Italian Physical Society, pp. 1184 –1191.

Postgraduate School Monterey CA USA.

*2006 Conference Record (IEEE)*, pp. 1–6.

*Intelligence IEEE Transactions on* PAMI-9(5): 690 –698.

*2007. IEEE International Conference on*, pp. 1143 –1147.

Bethesda MD USA.

18(3): 327 –339.

pp. 113 – 120.

pp. 1011–1016.

pp. 2098 –2103.

sampling networks, *Oceanography* 6(3): 86–94.

in pictures, *Communications of the ACM* 15(1): 11–15.

vehicle, *IEEE Journal of Oceanic Engineering* 30(2): 428–442.

polynomial, *The American Mathematical Monthly* 103(1): 51–57.

vehicles for very shallow water mine countermeasures, *OCEANS 2003 Conference*


### **8. References**


32 Will-be-set-by-IN-TECH

2. Explore the applicability of the framework to non-conservative, energetic manoeuvring in 3D, i.e. use the same framework to generate commands for the depth / pitch low-level controllers. Explore the behaviour of 3D-formations based on the *honeycombs*

Abkowitz, M. (1969). *Stability and Motion Control of Ocean Vehicles*, MIT Press Cambridge MA

Ahn, S., Rauh, W. & Recknagel, M. (1999). Ellipse fitting and parameter assessment of

Allen, T., Buss, A. & Sanchez, S. (2004). Assessing obstacle location accuracy in the REMUS

An, P., Healey, A., Park, J. & Smith, S. (1997). Asynchronous data fusion for auv navigation via

Barisic, M., Vukic, Z. & Miskovic, N. (2007a). Kinematic simulative analysis of virtual potential

Barisic, M., Vukic, Z. & Miskovic, N. (2007b). A kinematic virtual potentials trajectory planner

Bildberg, R. (2009). Editor's foreword, *in* B. R. (ed.), *Proceedings of the 16th International*

Boncal, R. (1987). *A Study of Model Based Maneuvering Controls for Autonomous Underwater Vehicles*, Master's thesis, Naval Postgraduate School Monterey CA USA. Carder, K., Costello, D., Warrior, H., Langebrake, L., Hou, W., Patten, J. & Kaltenbacher, E.

model inputs, *IEEE Journal of Oceanic Engineering* 26(4): 742–751.

*Proceedings. 1999 IEEE/RSJ International Conference on*, Vol. 1, pp. 525 –530. Allen, B., Stokey, R., Austin, T., Forrester, N., Goldborough, R., Purcell, M. & von Alt, C. (1997).

results, *OCEANS 1997 Conference Record (IEEE)*, Vol. 2, pp. 994–100.

circular object targets for robot vision, *Intelligent Robots and Systems 1999. IROS '99*

REMUS A Small Low Cost AUV system description field trials and performance

unmanned underwater vehicle, *Proceedings - Winter Simulation Conference*, Vol. 1,

heuristic fuzzy filtering techniques, *OCEANS '97. MTS/IEEE Conference Proceedings*,

field method for AUV trajectory planning, *in* K. Valavanis & Z. Kovacic (eds), *15th Mediterranean Conference on Control and Automation, 2007. Proceedings of the*, p. on CD.

for AUV-s, *in* M. Devy (ed.), *6th IFAC Symposium on Intelligent Autonomous Vehicles,*

*Symposium on Unmanned Untethered Submersibles Technology*, Autonomous Undersea

(2001). Ocean-science mission needs: Real-time AUV data for command control and

estimates.

USA.

pp. 940–948.

Vol. 1, pp. 397 –402 vol.1.

*2007. Proceedings of the*, p. on CD.

Systems Institute. on CD.

**8. References**

(3D tesselations) of the vector space of reals.

(c) Dealing with the issues of the instability of the "foreign" AUVs' state estimates covariance matrix by one of three ways: *(i)* using synchronous, pre-scheduled hydroacoustic communication. Communication would entail improved estimates coming from on-board the AUVs, where the estimates are corrected by collocated measurement; *(ii)* exploring an on-demand handshake-based communication scheme. Handshaking would be initiated by an AUV polling a team-member for a correction to the local estimate featuring unacceptably large covariance; *(iii)* exploring a predictive communication scheme where the AUVs themselves determine to broadcast their measurements without being polled. This last option needs to involve each AUV continually predicting how well other AUVs are keeping track of its own state


**1. Introduction** 

user-specified mission.

"AUV-XX" are described.

(Yuh, 1990; Venugopal and Sudhakar, 1992).

**6** 

*China* 

**Modeling and Motion Control** 

Autonomous Underwater Vehicles (AUV) speed and position control systems are subjected to an increased focus with respect to performance and safety due to their increased number of commercial and military application as well as research challenges in past decades, including underwater resources exploration, oceanographic mapping, undersea wreckage salvage, cable laying, geographical survey, coastal and offshore structure inspection, harbor security inspection, mining and mining countermeasures (Fossen, 2002). It is obvious that all kinds of ocean activities will be greatly enhanced by the development of an intelligent underwater work system, which imposes stricter requirements on the control system of underwater vehicles. The control needs to be intelligent enough to gather information from the environment and to develop its own control strategies without human intervention

However, underwater vehicle dynamics is strongly coupled and highly nonlinear due to added hydrodynamic mass, lift and drag forces acting on the vehicle. And engineering problems associated with the high density, non-uniform and unstructured seawater environment, and the nonlinear response of vehicles make a high degree of autonomy difficult to achieve. Hence six degree of freedom vehicle modeling and simulation are quite important and useful in the development of undersea vehicle control systems (Yuh, 1990; Fossen 1991, Li et al., 2005). Used in a highly hazardous and unknown environment, the autonomy of AUV is the key to work assignments. As one of the most important subsystems of underwater vehicles, motion control architecture is a framework that manages both the sensorial and actuator systems (Gan et al., 2006), thus enabling the robot to undertake a

In this chapter, a general form of mathematical model for describing the nonlinear vehicle systems is derived, which is powerful enough to be applied to a large number of underwater vehicles according to the physical properties of vehicle itself to simplify the model. Based on this model, a simulation platform "AUV-XX" is established to test motion characteristics of the vehicle. The motion control system including position, speed and depth control was investigated for different task assignments of vehicles. An improved Ssurface control based on capacitor model was developed, which can provide flexible gain selections with clear physical meaning. Results of motion control on simulation platform

**Strategy for AUV** 

Lei Wan and Fang Wang *Harbin Engineering University* 

