Underwater Vehicles Part 6 pps

Experimental Research on Biorobotic Autonomous Undersea Vehicle 189 0 20 40 60 80 100 120 140 160 1 21 41 61 81 101 121 vehi cl e s p eed ( cm/s ) p ower wi t hou zer o- l oad p ower ( w ) A0/ c0=1. 5 A0/ c0=1. 0 A0/ c0=0. 75 scr ew p ro p rller Fig. 15. Power consumption (without zero-load power) of caudal fin thruster and screw propeller 3) Measurement results of maneuverability performance Possible commissions of portable UUV include probe of port and coast, as well as identification and destroy of torpedo [17]. To perform these commissions, AUV often needs to be close to the object in a small distance and at the same time avoids colliding. At that moment low speed maneuverability is particularly important. For example, AUV is often constrained in a narrow space when it is in the state of autonomous navigation. At that moment AUV needs to turn in situ round itself to go back to open sea, which is a maneuverability often used by ROV but difficult for AUV whose advantage lies in its cruising. The VCUUV achieved 1.2m/sec and turn rates up to 75°/sec[12]. With flexible caudal hull and four joints caudal fin driven by hydrodynamics power, VCUUV possesses excellent maneuverability. It achieves a turning diameter of two body lengths(BL). Though the hull of SPC-III is completely rigid, and it only has two joints, its special caudal structure enables caudal fin to realize a deflection angle of 0~90°. 90°deflection angle can be used for emergency braking. Figure 9, 10 shows the circular trajectory of SPC-III and propellers comparison AUV under maneuverability measurements. The trajectory was drawn at GCS300 ground station software using GPS Coordinate data recorded by Autopilot. Note that the calibration of the map scale is 5m. SPC-III adopts a flapping frequency of 2Hz, with a rotation speed of propeller at 7.5r/sec, and correspondent linear speed being about 1.1m. Yet the speed decreases remarkably as the turning radius decreases in turning state. At 45°deflection angle, caudal fin thruster achieves a turning diameter of 2.5BL, while screw propeller which uses rudder achieves a turning diameter of 5BL. Caudal fin thruster achieves minimum Underwater Vehicles 190 turning diameter 2BL at 60°deflection angle. Figure 11 illustrate the results of turning speed measurement, including two kinds of data. The first data are obtained through calculation according to the time the vehicle took to finish circular route; the second data are obtained according to compass data. The two kinds of data are almost the same. At similar deflection angle, the yaw rate of Propellers AUV is about 1/2 of that of caudal fin AUV. Fig.16 The trajectory of SPC-III AUV performing different caudal fin deflection at about 1.1m/sec. Fig. 17. the trajectory of the Comparison AUV performing different rudder deflection at about 1.1m/sec. 2.4 Probe experiment on blue-green algae Probe experiment on blue-green algae can be regarded as a commission to inspect propulsion and maneuverability performance of SPC-III. Located in the area of Changjiang Delta Region, Taihu Lake is the major water source of Wuxi. In the summer of 2007, there was a mass breakout of blue-green algae in Taihu Lake, which became the prime environmental issue harassing the local residents and government. In November 2007, carrying Water Quality Multiprobes (HACH D5X), SPC-III successfully performed a probe cruising of about 49km in the water of Taihu Lake and brought back concentration distributing data of blue-green algae. Some of the probe results are shown in Table 2. Areas under heavy pollution are indicated in red in Figure 9. 60° 30° 15° 45° 15° 30° 45° Experimental Research on Biorobotic Autonomous Undersea Vehicle 191 0 5 10 15 20 25 30 35 40 1 1121314151 turing rate(deg./s) deflection angle(deg.) GPS data,caudal fin compass,caudal fin GPD data,screw compass,screw Fig. 18. Yaw rate of SPC-III and the Comparison AUV, at about 1.1m/sec. Fig. 19. Cruising trajectory of SPC-III at water quality probe on Taihu Lake shown in blue and areas under heavy pollution indicated in red Data on water quality of Taihu Lake ( November,2007) average PH value 8.52 maximum PH value 9.51 concentration of blue-green algae （center of the lake） 3823cell/ml. average pollution concentration （part of lake shore） 288112cell/ml. Maximum concentration obtained 868120cell/ml. Table 2. SPC-III brought back data carrying HACH D5X in the water of Taihu Lake Star Underwater Vehicles 192 Fig. 20. working environment of SPC-III on Taihu Lake As a portable UUV, the convenience of SPC-III was proven in the experiment on Taihu Lake. It can be plunged or fished easily by two persons manually without the usage of special ships and devices. Branches and aquatic grass near the bank are often great disaster to small propellers; yet caudal fin thruster which depends on oscillating propulsion can safely pass such area. Thus SPC-III can cruise in water area which is close to the bank and full of aquatic plants. Since blue-green algae are active in these areas, maneuverability advantage of SPC- III is very remarkable. Furthermore, nets or navigation mark often appear on the set navigation route, which requires human intervention to change the course of the vehicle. Nevertheless, relying on greater turning rate, SPC-III can take action when it is very close to the obstacles and does not need early warning. As for the obtruding aquatic bushes it met when cruising in the area a few meters from the bank, SPC-III can steer clear of them with a very small turning radius by slowing down its speed. This is very difficult for a AUV with only one propeller. Having its batteries charged only one time, SPC-III completed its 49km-mission for 3 days continuously. No default was observed on caudal fin thruster. The reliability of this kind of propeller was preliminarily confirmed. 2.5 Discussions Compared with high speed dolphin and tuna, the current biorobotics unmanned undersea vehicle still has a long way to go. Yet compared with conventional single-screw propeller AUV, SPC-III has made great progress. With small displacement tonnage, it realizes one- component vector converter and increases low speed maneuverability of AUV remarkably. In addition, the power of caudal fin thruster is also satisfactory. It can be said that using actuating motors to drive two joints caudal fin thruster is a feasible option with the current engineering technology. Of course, there also exist some congenital deficiencies. For example, actuating motors works in oscillation condition and its peak power is 40% higher than that at even pace at similar power output, therefore actuating motor and amplifier possess higher power redundancy. This means power density of the propeller is also reduced. This is the exact reason why Vehicle velocity of SPC-III is hard to increase. Working in oscillation condition also prevents the actuating motors and reducer from work continuously at optimum efficiency points. It is foreseeable that both electro-mechanical conversion efficiency and transmission efficiency of caudal fin thruster are lower than screw Experimental Research on Biorobotic Autonomous Undersea Vehicle 193 propeller which is in a uniform rotation. In respect of noise, since reducer is adopted, there are no strong points in terms of radiated noise. Yet flapping frequency of caudal fin is far lower than working frequency of the propeller at the same vehicle speed, which means hydrodynamics noise may be low [11]. Future work can be carried out to obtain experiment data on noise through comparison experiment. 2.6 Conclusion This paper presents an alternative design scheme of two joints caudal fin thruster for portable AUV with single-screw propeller. Using this kind of caudal fin thruster, Biorobotic autonomous undersea vehicle SPC-III has a displacement tonnage of 47kg and a length of 1.75m. The caudal fin thruster only accounts 7% of its displacement tonnage. Comparison experiment on self-propelling has been carried out on the sea. Within the speed of 2~2.7 joints, power consumption of caudal fin thruster and screw propeller is nearly the same. Maximum speed is 1.36m/s and Maximum turning rate is 36°/s. Minimum turning diameter is 2BL, while Minimum turning diameter of the compared propeller AUV is 5BL. Theoretically, Equipped with inside 2352Wh power units, endurance can reach 20 hours at two knots. 3. Reference YinSheng Zhang, “Underwater Archaeology and Its Exploration Technology”, Southeast Culture, no. 4, pp. 29-33, 1996.in chinese. XiSheng Feng, “From Remotely Operated Vehicles to Autonomous Undersea Vehicles”, Engineering Science, vol. 2, no. 12, pp. 29-33, Dec 2000. .in chinese. JunFeng Huang, et al, “Remote Operated Vehicle(ROV) Dynamic Positioning Based on USBL(Ultra Short Base Line)”, Control Engineering of China, vol. 9, no. 6, pp. 75-78, Nov 2002 .in chinese. “Fish-like swimming, http://www.draper.com/tuna_web/vcuuc.html. F. E. Fish and J. J. Rohr, “Review of dolphin hydrodynamics and swimming performance,” United State Navy Technical Report 1801, Aug.1999. T. G. Lang, T. Y. Wu, C. J. Brokaw, and C. Brennen, “Speed, power, and drag measurements of dolphins and porpoises.” Swimming and Flying in Nature, pp. 553–571, Eds. Plenum Press, New York, NY, 1975. Oscillating foils of high propulsive efficiency. J. M. Anderson, K. Streitlien et al. [J] Fluid Mech., 1998, 360: 41-72. Drag Reduction in Fish-like Locomotion. D.S.Barrett, M.S.Triantafyllou, et al. [J] Fluids Mechanics. 1999,392:183-212. M.S.Triantafyllou, G.S. Triantafyllou, D.K.P. Yue. Hydrodynamics of Fishlike Swimming [J]. Annu. Rev. Fluid Mech. 2000, 32: 33-53 Cheng JY,Zhuang LX,Tong BG.Analysis of swimming three-dimensional waing plate.J Fluid Mech,1991,232:341~355 P. R. Bandyopadhyay, “Trends in biorobotic autonomous undersea vehicles,” IEEE J. Oceanic Eng., vol. 30, no. 1, pp. 109–139, Jan. 2005. J. M. Anderson and N. K. Chhabra, Maneuvering and stability performance of a robotic tuna, Integ. Comp. Biol., vol. 42, 118–126, 2002. Underwater Vehicles 194 J.M.Anderson and P.A. Kerrebrock. The Vorticity Control Unmanned Undersea Vehicle(VCUUV)-An autonomous vehicle employing fish swimming propulsion and maneuvering [C]. Proc.10th Int. Symp. Unmanned Untethered Submersible Technology. NH, sept, 1997: 189-195 M. Nakashima, K. Tokuo, K. Aminaga, K. Ono. Experimental Study of a Self-Propelled Two- joint Dolphin Robot. Proceedings of the Ninth International Offsore and Polar Engineering Conference. 1999:419-424 M. Nakashima and K. Ono, “Development of a two-joint dolphin robot,” in Neurotechnology for Biomimetic Robots, J. Ayers, J. L. Davis, and A.Rudolph, Eds. Cambridge, MA: MIT Press, 2002. M. Nakashima, Y. Takahashi, T. Tsubaki, and K. Ono, “Threedimensional maneuverability of a dolphin robot (roll control and loop-theloop motion), Proc. of the 2nd International Symposium on Aqua Bio-Mechanisms, 2003, CD-ROM: S.6–10. Fletcher, B. UUV master plan: a vision for navy UUV development , OCEANS 2000 MTS, 2000:65-71 Liang Jianhong,Wang Tianmiao,Zou Dan,Wang Song,Wang Ye, Trial Voyage of “SPC-II” Fish Robot, transaction of Beihang University,2005,31(7):709-713. Tianmiao Wang, Jianhong Liang. Stabilization Based Design and Experimental Research of a Fish Robot. the proceeding of IEEE IROS2005,2005: 954 -959 JianHong Liang, TianMiao Wang, Song Wang, Dan Zou , Jian Sun, Experiment of Robofish Aided Underwater Archaeology, the proceeding of IEEE ROBIO2005,2005. http://www.ifly-uav.com/viewintranews.asp?id=6&menu=news JianHong Liang, 2006,Propulsive Mechanism of Bionic Undersea Vehicle, Ph.D. Diss., BEIHANG University, Beijing Liangmei Ying, Jianliang Zhu ,Screw design and implementary on Comparison UUV, Report of CSSRC ,2006 11 Computer Vision Applications in the Navigation of Unmanned Underwater Vehicles Jonathan Horgan and Daniel Toal University of Limerick Ireland 1. Introduction The inquisitive nature of humans has lead to the comprehensive exploration and mapping of land masses on planet earth, subsequently scientists are now turning to the oceans to discover new possibilities for telecommunications, biological & geological resources and energy sources. Underwater vehicles play an important role in this exploration as the deep ocean is a harsh and unforgiving environment for human discovery. Unmanned underwater vehicles (UUV) are utilised for many different scientific, military and commercial applications such as high resolution seabed surveying (Yoerger et al. 2000), mine countermeasures (Freitag et al. 2005), inspection and repair of underwater man-made structures (Kondo & Ura 2004) and wreck discovery and localisation (Eustice et al. 2005). Accurate vehicle position knowledge is vital for all underwater missions for correct registration between sensor and navigation data and also for control and final recovery of the vehicle. The characteristics of the underwater environment pose a plethora of difficult challenges for vehicle navigation and these obstacles differ greatly from the issues encountered in land, air and space based navigation (Whitcomb 2000). The rapid attenuation of acoustic and electromagnetic radiation in water restricts the range of acoustic and optical sensors and also limits communication bandwidth. As a consequence of this severe absorption acoustic and optical sensors require submersion near to the survey mission site to gather accurate high resolution data sets. The limitation on communication bandwidth means that vehicle autonomy can only be achieved when the large majority of computation is performed onboard. Whereas land based vehicles can rely on Global Positioning System (GPS) for accurate 3D position updates, the underwater equivalent acoustic transponder network is limited by range, accuracy, the associated cost and deployment & calibration time. Another challenge that is faced with underwater navigation is the intrinsic ambient pressure. While terrain based vehicle developers have to consider the relatively simplistic and well understood nature of atmospheric pressure in sensor and actuator design, underwater pressure, increasing at a rate of approximately 1 atmosphere (14.7 psi) every 10 meters of depth, can greatly influence and restrict sensor and actuator design. Other issues such as the inherent presence of waves and underwater currents can make the task of accurately describing vehicle motion more difficult and, as a result, affect the accuracy of vehicle navigation. Underwater Vehicles 196 Many of these problems cannot be overcome directly so the underwater community relies on improving the navigation sensors and the techniques in which the sensor data is interpreted. The development of more advanced navigation sensors is motivated by the need to expand the capabilities and applicability of underwater vehicles and to increase the accuracy, quantity and cost effectiveness of oceanographic data collection. Sensor selection can depend on many factors including resolution, update rate, cost, calibration time, depth rating, range, power requirements and mission objectives. In general the accuracy of a particular sensor is directly proportional to its expense. This has lead to increased research efforts to develop more precise lower cost sensors and improve data interpretation by implementing more intelligent computation techniques such as multi sensor data fusion (MSDF). Many commercially available underwater positioning sensors exist but unfortunately no one sensor yet provides the perfect solution to all underwater navigation needs so, in general, combinations of sensors are employed. The current state of the art navigation systems are based on the use of velocity measurements from a Doppler velocity log (DVL) sensor conveniently fused with accurate velocity/angular rate and position/attitude measurements derived by integration and double integration respectively of linear acceleration and angular rates from an inertial measurement unit (IMU) (Kinsey et al. 2006). To bound the inherent integration drift in the system position fixes from an acoustic transponder network such as Long Baseline (LBL), Ultra Short Baseline (USBL) or GPS Intelligent Buoys (GIB) are commonly used. However, this option raises the mission cost as transponders require deployment prior to the mission or a mother ship is necessary. This solution also limits the area in which the vehicle can accurately navigate to within the bounds of the transponder network (acoustic tether). Over recent years, computer vision has been the subject of increased interest as a result of improving hardware processing capabilities and the need for more flexible, lightweight and accurate sensor solutions (Horgan & Toal 2006). Many researchers have explored the possibility of using computer vision as a primary source for UUV navigation. Techniques for implementing computer vision in order to track cables on the seabed for inspection and maintenance purposes have been researched (Balasuriya & Ura 2002; Ortiz et al. 2002). Station keeping, the process of maintaining a vehicle’s pose, is another application that has taken advantage of vision system’s inherent accuracy and high update rates (Negahdaripour et al. 1999; van der Zwaan et al. 2002). Motion estimation from vision is of particular interest for the development of intervention class vehicle navigation (Caccia 2006). Wreckage visualization and biological and geological surveying are examples of applications that use image mosaicking techniques to acquire a human interpretable view of the ocean floor but it has also been proven as an appropriate means for near seabed vehicle navigation (Negahdaripour & Xu 2002; Garcia et al. 2006). This chapter gives and introduction to the field of vision based unmanned underwater vehicle navigation and details the advantages and disadvantages of such systems. A review of recent research efforts in the field of vision based UUV navigation is also presented. This review is discussed under the following headings in relation to recent literature reviewed: image mosaicking, cable tracking, station keeping and positioning & localisation. This chapter also considers the applications of sensor fusion techniques for underwater navigation and these are also considered with reference to recent literature. The author gives an opinion about the future of each application based on the presented review. Finally conclusions of the review are given. Computer Vision Applications in the Navigation of Unmanned Underwater Vehicles 197 2. Underwater optical imaging Underwater optical imaging has many interesting and beneficial attributes for underwater vehicle navigation, as well as its ability to open up a wealth of understanding of the underwater world. However, it is not an ideal environment for optical imaging as many of its properties inherently affect the quality of image data. While image quality is a pertinent issue for vision system performance, other difficulties are also encountered such as the lack of distinguishable features found on the seafloor and the need for an artificial light source (Matsumoto & Ito 1995). For most UUV applications (below 10 meters) natural lighting is not sufficient for optical imaging so artificial lighting is essential. Light is absorbed when it propagates through water affecting the range of vision systems (Schechner & Karpel 2004). Many variables can affect the levels of light penetration including the clarity of the water, turbidity, depth (light is increasingly absorbed with increasing depth) and surface conditions (if the sea is choppy, more light will be reflected off the surface and less light transmitted to the underwater scene) (Garrison 2004). Underwater optical imaging has four main issues associated with it: scattering, attenuation, image distortion and image processing. Scattering is as a result of suspended particles or bubbles in the water deflecting photons from their straight trajectory between the light source and the object to be viewed. There are two different types of scattering; backscatter and forward scatter (see Fig. 1). Backscatter is the reflection of light from the light source back to the lens of the camera. This backscattering can result in bright specs appearing on the images, sometimes referred to as marine snow, while also affecting image contrast and the ability to extract features during image processing. Forward scatter occurs when the light from the light source is deflected from its original path by a small angle. This can result in reduced image contrast and blurring of object edges. The affect of forward scatter also increases with range. The rapid absorption of light in water imposes great difficulty in underwater imaging. This attenuation necessitates the use of artificial lighting for all but the shallowest of underwater missions (less than 10m, dependent on water clarity). The visible spectrum consists of several colours ranging from the red end of the spectrum (wavelength of <780nm) to the blue (wavelength of >390nm). Water effectively works as a filter of light, being more efficient at filtering the longer wavelength end of the visible spectrum, thus absorbing up to 99% of red light by a depth of approximately 4m in seawater (Garrison 2004). Absorption intensifies with increasing depth until no light remains (see Fig. 1). The effects of absorption discussed apply not only to increasing depth but also to distance. Due to the extreme pressures associated with deep-sea exploration there is need for high pressure housing around each sensor. In the case of a camera a depth rated lens is also required. Imperfections in the design and production of the lens can lead to non-linear distortion in the images. Moreover, the refraction of light at the water/glass and glass/air interface due to the changes in medium density/refractive-index can result in non-linear image deformation (Garcia 2001). To account for this distortion the intrinsic parameters of the camera must be found through calibration and using radial and tangential models the lens distortion effects can be compensated for. The characteristics of the underwater environment not only create issues for collection of clear and undistorted images but also affect the subsequent image processing. Due to the severe absorption of light and the effects of scattering (marine snow etc.) it is essential to decrease range to the objects being viewed in order to obtain higher resolution clearer images. This has the consequence of limiting the Underwater Vehicles 198 field of view (FOV) of the camera and thus not allowing for wide area images of the seafloor while also challenging the assumption that changes in floor relief are negligible compared to camera altitude. The motion of the artificial light source attached to the vehicle leads to non-uniform illumination of the scene thus causing moving shadows which makes image to image correspondence more difficult. The lack of structure and unique features in the subsea environment can also lead to difficulties in image matching. While terrestrial applications can make use of man-made structures, including relatively easily defined points and lines, the subsea environment lacks distinguishable features. This is in part due to the lack of man- made structures but also due to the effects of forward scattering blurring edges and points. An issue that affects all real-time image processing applications is whether the hardware and software employed are capable of handling the large amounts of visual data at high speed. This often requires a trade-off in image processing between the frame rate and the image resolution which can be detrimental to the performance of the application. Fig. 1. Scattering and light attenuation (left), colour absorption (right) (Garrison 2004). 3. Vision based navigation Cameras are found on almost all underwater vehicles to provide feedback to the operator or information for oceanic researchers. Vision based navigation involves the use of one or more video cameras mounted on the vehicle, a video digitizer, a processor and, in general, depending on depth, a light source. By performing image processing on the received frames, the required navigation tasks can be completed or required navigation information can be calculated. The usual setup for the vision system is a single downward facing camera taking images of the sea floor at an altitude of between 1 and 5 meters (see Fig. 2). The use of optical systems, like all navigation sensors, has both advantages and disadvantages. If the challenges of underwater optical imaging, described in section 2, can be successfully addressed some of the potential advantages of vision based underwater navigation include: [...]... Mapping IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles (NGCUV 08') Pizarro, O & Singh, H (2003) Toward large-area mosaicing for underwater scientific applications IEEE Journal of Oceanic Engineering 28(4): 65 1 -67 2 Ribas, D.; Ridao, P.; Neira, J & Tardos, J D (20 06) SLAM using an Imaging Sonar for Partially Structured Underwater Environments IEEE/RSJ International Conference on... Mosaic–based Visual Navigation for Autonomous Underwater Vehicles Instituto Superior T'ecnico Lisbon, Universidade Técnica de Lisboa Doctor of Philosophy: 138 Gracias, N.; van der Zwaan, S.; Bernardino, A & Santos-Victor, J (2003) Mosaic-based navigation for autonomous underwater vehicles IEEE Journal of Oceanic Engineering 28(4): 60 9 -62 4 Gracias, N & Negahdaripour, S (2005) Underwater Mosaic Creation using Video... Operations with Autonomous Underwater Vehicles Underwater Defense Technology (UDT) Garcia, R.; Xevi, C & Battle, J (2001a) Detection of matchings in a sequence of underwater images through texture analysis Proceedings of the International Conference on Image Processing Garcia, R (2001) A Proposal to Estimate the Motion of an Underwater Vehicle through Visual Mosaicking Department of Electronics, Informatics... has a number of advantages over other types of sensors for underwater applications Computer Vision Applications in the Navigation of Unmanned Underwater Vehicles 211 Cameras are light weight and do not possess a minimum operating range unlike their acoustic counterparts (Nolan 20 06) Despite these advantages over other sensors, machine vision underwater poses an amount of difficult challenges to be overcome... Science and Systems Cambridge, MA, MIT Press: 57 -64 Eustice, R M (2005) Large-Area Visually Augmented Navigation for Autonomous Underwater Vehicles Boston, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Doctor of Philosophy: 187 Fleischer, S D (2000) Bounded-error vision-based navigation of autonomous underwater vehicles Department of Aeronautics and Astronautics, Stanford... Escolano, F & Jenkin, M (20 06) Underwater 3D SLAM through entropy minimization Proceedings 20 06 IEEE International Conference on Robotics and Automation, ICRA 20 06 Schechner, Y Y & Karpel, N (2004) Clear underwater vision IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004 Singh, H.; Howland, J & Pizarro, O (2004) Advances in large-area photomosaicking underwater IEEE Journal... target tracking for autonomous vehicles with the out-of-sequence measurements solution Robotics and Autonomous Systems 56( 2): 157-1 76 Kinsey, J C & Whitcomb, L L (2004) Preliminary field experience with the DVLNAV integrated navigation system for oceanographic submersibles Control Engineering Practice 12(12): 1541-1549 Kinsey, J C.; Eustice, R M & Whitcomb, L L (20 06) Survey of underwater vehicle navigation:... Practice 15 (6) : 703-714 Cufi, X.; Garcia, R & Ridao, P (2002) An approach to vision-based station keeping for an unmanned underwater vehicle IEEE/RSJ International Conference on Intelligent Robots and System, 2002 Davison, A J.; Reid, I D.; Molton, N D & Stasse, O A S O (2007) MonoSLAM: Real-Time Single Camera SLAM IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (6) : 1052-1 067 212 Underwater. .. edge detector 4th Alvey Vision Conference: 147-151 Haywood, R (19 86) Acquisition of a Micro Scale Photographic Survey Using an Autonomous Submersible OCEANS, New York, USA Horgan, J & Toal, D (20 06) Vision Systems in the Control of Autonomous Underwater Vehicles 7th IFAC Conference on Manoeuvering and Control of Marine Craft (MCMC 20 06) Horgan, J.; Toal, D.; Ridao, P & Garcia, R (2007) Real-time vision... Using Particle Filters for Autonomous Underwater Cable Tracking IFAC Workshop on Navigation, Guidance and Control of Underwater Vehicles (NGCUV 08') Yoerger, D.; Bradley, A.; Walden, B.; Cormier, M H & Ryan, W (2000) Fine-scale seafloor survey in rugged deep-ocean terrain with an autonomous robot IEEE International Conference on Robotics and Automation, ICRA '00 12 AUV Application for Inspection of Underwater . the Navigation of Unmanned Underwater Vehicles 197 2. Underwater optical imaging Underwater optical imaging has many interesting and beneficial attributes for underwater vehicle navigation,. Zhang, Underwater Archaeology and Its Exploration Technology”, Southeast Culture, no. 4, pp. 29-33, 19 96. in chinese. XiSheng Feng, “From Remotely Operated Vehicles to Autonomous Undersea Vehicles ,. energy sources. Underwater vehicles play an important role in this exploration as the deep ocean is a harsh and unforgiving environment for human discovery. Unmanned underwater vehicles (UUV)