**10. References**


96 Autonomous Underwater Vehicles

desired goal point (depicted by a circle). The SeaFox USV then followed it almost precisely (the magenta line). As seen from Fig.27 the trajectory generator was invoked at an arbitrary location while the USV was performing a clockwise turn. Since the USV was commanded to return to its start location upon completion of this manoeuvre, the magenta line includes a portion of this return trajectory as well (otherwise, the actual USV track would be nearly

An onboard trajectory planner based on the Inverse Dynamics in the Virtual Domain direct method presented in this chapter is an effective means of augmenting an unmanned maritime vehicle's autopilot with smooth, feasible trajectories and corresponding controls. It also facilitates incorporation of sophisticated sensors such as forward-looking sonar for deliberative and reactive obstacle avoidance. This approach has been implemented on both unmanned undersea and surface vehicles and has demonstrated great potential. Beyond its ability to compute near-optimal collision-free trajectories much faster than in real time, the proposed approach supports the utilization of any practically-sound compound performance index. This makes the developed control architecture quite universal, yet simple to use in a variety of applied scenarios, as demonstrated in several simulations and preliminary sea trials. This chapter presented results from only a few preliminary sea trials. Future research will continue development of the suggested trajectory framework in

The authors wish to gratefully acknowledge the support of Doug Horner, Co-Director of the CAVR and Principle Investigator for the REMUS UUV and SeaFox USV research programs at NPS. In addition, Sean Kragelund would like to thank his CAVR colleagues Tad Masek and Aurelio Monarrez. Mr. Masek's outstanding software development work to implement obstacle detection and mapping with forward looking sonar made possible the OA applications described herein. Likewise, the tireless efforts of Mr. Monarrez to continually upgrade, maintain, and operate CAVR vehicles in support of field experimentation have

Basset, G., Xu, Y. & Yakimenko, O. (2010). Computing short-time aircraft maneuvers using

BlueView Technologies, Inc. (2011). 2D Imaging sonar webpage. Available from:

Bourke, P. (1992). Intersection of a line and a sphere (or circle). Professional webpage.

Elfes, A. (1989). Using occupancy grids for mobile robot perception and navigation.

Available from: http://paulbourke.net/geometry/sphereline

direct methods," *Journal of Computer and Systems Sciences International*, 49(3), 145-176

indistinguishable from the reference trajectory on this plot).

**8. Conclusion** 

support of other tactical scenarios.

made a lasting contribution to this Center.

*Computer*, 22(6), 46-57

www.blueview.com/2d-Imaging-Sonar.html

**9. Acknowledgements** 

**10. References** 


**0**

**5**

*Croatia*

**Formation Guidance of AUVs Using**

*University of Zagreb, Faculty of Electrical Engineering and Computing*

Autonomous Underwater Vehicles (AUVs) are the most complex type of unattended marine systems, being *mobile*, with challenging dynamics and non-holonomic kinematics. They are increasingly being recognized as a keystone technology for projecting human scientific and economical interests into the deep Ocean (Papoulias et al., 1989). A recent report by Bildberg (2009) delivers the verdict of several key researchers that the AUVs are rapidly moving

The *autonomy* of AUVs is their key capability. They autonomously explore Ocean phenomena relevant to human scientific and economic interests. Well engineered autonomous control allows them to act robustly and predictably with regards to waves, currents, wind, sea-state and numerous other disturbances and operational conditions in nature. As a consequence, they are today being cast in the leading role in projecting human presence and human interests

• Marine biology, conservationist biology, marine ecology management, biological

• Geology, petrology, seismology, hydrography (for the benefit of e.g. the oil and gas

• Maritime and naval archaeology, submerged cultural heritage protection and

• Marine traffic management, search and rescue, hazardous material and waste management, emergencies and catastrophes management and first responding

• Maritime security, customs enforcement, border protection and defense (Allen et al., 1997; 2004; Clegg & Peterson, 2003; Curtin et al., 1993; Eisman, 2003; US Navy, 2004).

To increase the effectiveness, safety, availability, economics and applicability of AUVs to these and other topics of interest, this chapter proposes a *decentralized cooperative cross-layer formation-control* paradigm for entire groups of AUVs collaborating in exploration tasks. The AUVs are assumed to navigate on a common "flight ceiling" by using robust altitude controllers, based on altimeter echosounder measurements. The proposed virtual potential framework allows for the 2D organization of individual trajectories on such a "flight ceiling".

in the Ocean, in an increasingly diverse gamut of topics:

industry, maritime civil engineering etc.),

(Carder et al., 2001; Pang, 2006)

• Physical oceanography (Plueddemann et al., 2008; Tuohy, 1994),

oceanography (Farrell et al., 2005; Pang, 2006; Pang et al., 2003),

**1. Introduction**

towards maturity.

management,

**Decentralized Control Functions**

Matko Barisic, Zoran Vukic and Nikola Miskovic

Yakimenko, O.A., Horner, D.P. & Pratt, D.G. (2008). AUV rendezvous trajectories generation for underwater recovery, In: *Proceedings of the 16th Mediterranean conference on Control and Automation, Corse, France* 
