Recent Progress of UUV-Keynote Part 1

ISIUS Nanjing

© 2008 KEYNOTE SPEECH

International Symposium on Intelligent Unmanned System | October 15-18, 2008

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Advances in Unmanned Underwater Vehicles Technologies Agus Budiyono1 1 Smart Robot Center, Department of Aerospace Information Engineering, Konkuk University, Seoul, Korea. Previously with Center for Unmanned System Studies, Institut Teknologi Bandung, Indonesia

Recent decades have witnessed increased interest in the design, development and testing of unmanned underwater vehicles for various civil and military missions. A great array of vehicle types and applications has been produced along with a wide range of innovative approaches for enhancing the performance of UUVs. Key technology advances in the relevant area include battery technology, fuel cells, underwater communication, propulsion systems and sensor fusion. These recent advances enable the extension of UUVs’ flight envelope comparable to that of manned ve-hicles. For undertaking longer missions, therefore more advanced control and navigation will be required to maintain an accurate position over larger operational envelope particularly when a close proximity to obstacles (such as manned vehicles, pipelines, underwater structures) is in-volved. In this case, a sufficiently good model is prerequisite of control system design. The paper is focused on discussion on advances of UUVs from the modeling, control and guidance perspectives. Lessons learned from recent achievements as well as future directions are highlighted.

Unmanned underwater vehicle, model identification, control, navigation, guidance

I. Introduction

By definition, unmanned underwater vehicles (UUVs), are all types of underwater robots which are operated with minimum or without intervention of human operator. In the literatures, the phrase is used to describe both a remotely operated vehicle (ROV) and an autonomous underwater vehicle (AUV). Remotely operated vehicles (ROVs) are tele-operated robots that are deployed primarily for under-water installation, inspection and repair tasks. They have been used extensively in offshore industries due to their advantages over human divers in terms of higher safety, greater depths, longer endurance and less demand for sup-port equipment. In its operation, the ROV receives instruc-tions from an operator onboard a surface ship (or other mooring platform) through tethered cable or acoustic link. AUVs on the other hand operate without the need of con-stant monitoring and supervision from a human operator. As such the vehicles do not have the limiting factor in its operation range from the umbilical cable typically asso-ciated with the ROVs. This enables AUVs to be used for certain types of mission such as long-range oceanographic data collection where the use of ROVs deemed impractical.

Ura in [1] proposed the classification of AUVs area of ap-plications into three different categories starting from the basic to more advanced missions: a) Operations at a safe distance from the sea floor including observation of the sea floor using sonar, examination of water composition, sam-pling of floating creatures; b) Inspections in close proximi-ty to the sea floor and man-made structures such as inspec-tion of hydrothermal activity, creatures on the seafloor and underwater structures; c) Interactions with the sea floor and man-made structures i.e. sampling of substance on the seafloor and drilling.

The control of UUVs in all the above missions presents several challenges due to a number of factors. The first difficulty comes from the inherent nonlinearity of the un-derwater vehicle dynamics. Many uncertainties contribute to the prediction or calculation of hydrodynamic coeffi-cients. Meanwhile, additional challenge comes from the environment: more limited operational underwater naviga-tion sensors, low visibility when using vision sensors and underwater external disturbances.

Since the 1990s, various control techniques have been proposed for UUVs both in simulation environment and

2 International Symposium on Intelligent Unmanned System | October 15-18, 2008

actual in-water experiments. Among them are fuzzy sliding mode control [3,9,16,20], reinforcement learning [4], mod-el predictive [8], neural networks [10,47], hybrid [6,14,15], backstepping [11,13], nonlinear [12,51], adaptive control [16,19,70], PID [28], LQG/LTR [35] and sliding mode [36]. In terms of the model involved, the control design can be categorized into three different approaches:

1. Model-based nonlinear control: a nonlinear model is first derived using the Newton-Euler equation of motion for flight vehicle moving in six degrees of freedom. The forces and moments working on UUV are to be formulated consisting of hydros-tatic (gravity and buoyancy) and hydrodynamic (added mass, steady-state and thruster) compo-nents. Using this approach, only one model is re-quired to represent the whole flight envelope of the UUVs. Nonlinear optimization techniques are used as tool for system identification. Appropriate nonlinear control can then be designed using the model. Included in this category is work by Fos-sen and Ross [2] where they used proportion-al-integral-derivative (PID) control and backstep-ping nonlinear autopilot.

2. Model-based linear control: the method starts by defining a number of trim conditions associated with representative UUVs operation. Linearization procedure is conducted based on the principle of small perturbation around trimmed points. Linear control synthesis can then be applied to each li-near model. To cover the entire UUV flight envelope, a gain-scheduled controller is applied based on the interpolation between families of li-near models.

3. Control without system model: traditionally the method is based on classical sin-gle-input-single-output (SISO) PID. The feedback control system is designed in which the controller parameters are tuned empirically to acquire ac-ceptable control systems. The approach is predi-cated to the assumption that couplings between vehicle modes are negligible. Example of suc-cessful application of this approach is given in [3] where an experimental method is proposed to de-termine the parameters of a Sliding Mode Fuzzy Controller (SMFC). In general the SISO approach however is not agreeable with complex UUV ve-hicles with sophisticated performance criteria. To make a decent controller, more advanced multiva-riable controller synthesis approaches require ac-

curate models of the dynamics.

Comparative study of control system designs for underwa-ter flight vehicles is given in [5]. This paper is focused on the discussion of model-based control design and naviga-tion system technology in the framework of recent ad-vances in UUVs elaborated in the following order. In the section below (II), the system and technology background of UUVs are presented including the contemporary UUV development, summary of lessons from the research on UUV controls and identification of relevant UUV technol-ogy building blocks. The next section (III) gives the moti-vation of why modeling the UUV dynamic is an indis-pensable step in designing control system. Nonlinear dy-namic modeling is presented based on first principle ap-proach. Linearization procedure is conducted to provide appropriate model for the implementation of linear control. The last section (IV) identifies future trends in underwater robotics research. Concluding remarks on the challenges and future directions are made in final section (V).

II. Background: Science and Technology

A. History of UUV Development

While the conceptual design for submarine was dated back as early as 1578, it was not until 1868 that the first modern UUV was constructed in the form of a self-propelled tor-pedo. In 1958, US Navy instigated the cable-controller un-derwater vehicle program as the precursor of ROV. The underwater recovery vehicle was deployed in the search of USS Threster which sank off the New England coast in 1963 [21]. The use of commercial UUVs was finally rec-ognized owing to primarily the onset of the offshore oil and gas major operation. Since then ROVs have become the industry standard underwater operation of offshore busi-ness. The use of AUVs in the mean time only gradually gains acceptance both for naval and commercial sectors due to more stringent operational requirements. Notwith-standing, thanks to rapid development in underwater sen-sors, battery and other supporting technologies, the devel-opment of AUV has gained acceleration in recent decades. There were more than 46 AUV models in 1999 [23] and according to a survey in 2004, about 240 AUVs, ranging from 10 kg to 10 tons in weight and several meters to 6000 meter in operational depth, were in operation at different sea locations in the world [1,22,27].

The offshore-survey industry uses AUVs for detailed map-ping of the seafloor, allowing oil companies to carry out construction and maintenance of underwater structures in the most cost-effective manner and with minimum disrup-

International Symposium on Intelligent Unmanned System | October 15-18, 2008 3

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tion to the environment. The maintenance mission typically requires a combination of subbottom profilers, visual sen-sors, and extensive on-board processing. Military applica-tion for an AUV includes the mapping of an area for mine detection purposes and undersea resupply of foodstuffs, fuel, and ammunition. Meanwhile scientists deploy AUVs to study the ocean and the ocean floor using INS, side-scan sonar, multi-beam echo sounders, magnetometers, ther-mistors, and other underwater sensors including AD(C)Ps and water-quality sensors [22]. Fig. 1 shows contemporary AUVs with their corresponding maximum operational depth and speed.

Shallow water AUVs are typically used for test bed, for instance Musaku (JAMSTEC-Japan), Twin Burger (U of Tokyo), Phoenix (Naval Post Graduate), and ODIN (U of Hawaii). Low speed ultra-low power AUVs are used for a long endurance mission lasting for weeks or months at a time, periodically relaying data to shore by satellite before returning to be picked up. Slocum gliders can operate with the speed of 0.5 knot for 20 days collecting various data including depth, temperature, salinity, particulates, chloro-phyll and light intensity [24]. Spray Gliders [25] can dive

for 150 days with 0.6 knot. Deep sea AUVs are used for various missions: bottom survey (UROV-2000, Doggie, ABE, R1), science mission (Ocean Voyager II, Odyssey II), military/scientific intervention (SAUVIM), under sea-ice survey (Theseus) and underwater inspection (AE1000, Ex-plorer). Long, deep water surveys in particular are primari-ly undertaken by the oil industry and the geophysical sciences where side-scan and multibeam sonars are often used along with a range of chemical sensors. The high speed AUV is represented by Virginia Tech HSAUV which can travel with the maximum speed of over 15 knots.

The market trend for AUV has been driven by cost rather than technology. Despite their proven versatile use in many applications, the full commercialization of AUVs has been held back primarily by the high purchase cost. The most recent advances in AUV technology, however, have al-lowed the production of low-cost high-performance AUV (LCAUV). This class of vehicle cost substantially less than the conventional survey craft, need fewer personnel and fuel and take less survey time making it an attractive choice for suitable applications.

Max Speed

(knots)

Max Depth (m)

0

2

4

6

8

10

12

14

16

0 1000 2000 3000 4000 5000 6000 7000

Doggie

SAUVIM, Odyssey

ABE

Hugin 4500,Sea Surveyor III

DoradoUrashima

Eco Surveyor

Asterix

Hugin 3000,Sea Surveyor I

GaviaHugin 1000

Otter, Theseus

ReMUS2

AE1000

Spray gliders

Virginia Tech High Speed AUV

Fetch

Daurade

Sea Otter MkI

Sea Otter MkII

Sotong

ReMUS

Tiram Infante

R1Kerang

ALBAC

Slocum glider

ODINIIManta‐Ceresia

VORAMAutosub‐1

Tri‐DogAriesPhoenix, Twin Burger

Figure 1: Representative AUVs with their maximum operational depth and speed [22-34]


(a) Clam

(b) Oyster (c) Squid

Figure 2: UUV Prototypes- CentrUMS-ITB [28]

B. Lessons learned from CentrUMS-ITB AUV Program The research on UUVs at Center for Unmanned Systems Studies (CentrUMS)-ITB was started in 2001 with the de-velopment of ROV Kerang (Clam) as shown in Fig. 2 (a). This first prototype of the underwater vehicle is de-signed as a test-bed with operating depth of up to 10 m with a cruising speed of 3 knots. The sensor suit contains gyro, MLDA, depth sensor, camera and leakage detector. The position information, leak detection and power distri-bution are sent to fault manager which eventually transmit the signal to maneuvering control unit and communication unit for display to the remote operator. The maneuvering unit receives information from mission plan through the mission executor. The maneuver can be achieved using the buoyancy control by means of control valve and using the propulsion control by means of motor driver controller. The second prototype named Oyster as shown in Fig. 2(b) features a more advanced underwater vehicle design with the operating depth of up to 300m and the speed of 4 knots. Fig. 3 shows the drawings of the vehicle dimensioned at 1200 mm (L) x 800 mm (W) x 800 mm (H) and weighed 150 kg. The orientation is obtained through triad accelero-meters, gyros and magnetometers. While the depth and leakage is measured and detected respectively by the same transducer as those of the first prototype vehicle. The design is equipped with hydraulically actuated 4 axis ma-nipulator with the maximum payload of 10 kg. The third is biologically-inspired design characterized by squid-like structure for a better hydrodynamic property shown by Fig. 2(c). The more refined prototype is sup-ported by more elaborate sensor arrays which consist of triad accelerometers, gyros and magnetometers for position and orientation, speed log sensor for velocity measurement, single beam altimeter, depth sensor, GPS, Doppler Velocity Log (DVL), and USBL tracking system. The vehicle

weighs 300 kg with the dimension of 4400mm x 750 mm x 950 mm. The operating depth is up to 100 m within the working range of 6 nautical miles and the cruising speed of 6 knots. The control systems for all the above UUVs were designed by empirically tuning PID gains. Even though the control systems were successfully applied for the original proto-types, the empirical technique was proved to be difficult for implementation to the modified prototype. Due to an up-date in mission requirement for AUV Squid, a sensor suit was moved to the nose changing its dynamics characteristic. The problem with cumbersome gain optimization led to the effort in developing dynamic model of the AUV for mod-el-based control synthesis elaborated in Section III.

C. UUV Technology Building Blocks

Some key areas in current state-of-the-art underwater ro-botic technologies are responsible for recent advances in AUVs. They include battery technology, fuel cells, under-water communication, propulsion systems and sensor fu-sion. Ref. [23] surveyed key subsystems of the autonomous underwater robots, recent developments in each subsystem, and current state-of-the art in underwater robotic technolo-gy for future advancement. Key subsystems are grouped under five more general system category: mission (sensors, world modeling, data fusion [61,72], planner), computer (SW, HW, fault-tolerance), platform (hull [54], propulsion [17,80], power, workpackage, emergency [79]), vehicle sensor (guidance [37,44,50,53,60,64,77,85,86,88,91], na-vigation [45,56, 61,63,73,76], obstacle avoidance [62,66], self-diagnostic [41,74,78], communication) and support (logistic, simulation, user interface [58]). Along the de-sign evolution, key technology areas have been manifested in dynamic modeling [52,58,67,68,95], control [3-20,30, 38,42,46,50,73,82-84,98-100], pressure halls/fairings, and mechanical manipulator systems. The ongoing research activities are aiming at enhancing the autonomy of the un-derwater vehicle including better design of communication, higher power density and more reliable navigation and control for deep water operation. The existing primary me-thods for AUVs navigation are [45]: (1) dead-reckoning and inertial navigation systems, (2) acoustic navigation, and (3) geophysical navigation techniques. The use of dead-reckoning and inertial navigation system (INS) has been inhibited by the high cost and power consumption especially for small AUVs. Lower grade INS on the other hand poses a problem of error drift as the vehicle travels further distance. An integration of INS with other sources of error-bounding navigation such as Doppler velocity so-nar (DVS) or GPS through Kalman filtering is desirable


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and has been proven to be a viable solution. Unlike the tethered ROVs that are powered by the mother ship, the AUVs depend on the power traditionally provided by lead-acid type battery. Due to higher energy density, ten to twenty-fold as high, fuel-cell and fuel-cell-like devices have been attracted more attention in the area of AUV power.

III. Dynamics and Control of Underwater Vehicles

The equation of motion of underwater vehicles in six de-grees of freedom consists of three elements: vehicle kine-matics, vehicle rigid body dynamics and vehicle mechanics. This section is focused on describing the mathematical modeling of UUV dynamics for the purpose of mod-el-based control system design. Interested readers are re-ferred to [102,103] for more detail account. For the sake of brevity, the discussion is confined to the longitudinal mode of torpedo like AUV following [104], Fig. 3.

Figure 3: AUV Sotong (Squid)- CentrUMS-ITB

A. Underwater Vehicle Modeling The description of forces equation for a vehicle moving in inertial frame of reference is given by Euler-Newton equa-tion: ) (1)

Assuming the vehicle mass is constant and the forces are evaluated with respect to body frame which moves with respect to the inertial frame of reference, the expression can be rewritten as

(2)

where: : Linear velocity vector of body axis origin

: Angular velocity vector of body axis origin

: Position vector of vehicle cg w.r.t body axis By defining the following relation and doing the cross-product :

the forces equation can be decomposed into three scalar components:

(3)

By the same token, the moments equation read

(4)

If the vehicle cg does not coincide with the origin of the body frame, the component of moments equation can be expressed as: (5)

(6)

(7)

where

(8)

(9)

At this stage, to express the external forces and moments that works on a UUV. In general, the they can be written in terms of the following contributions:

(10)

(11)

The first components of forces and moments come from


gravity and buoyancy representing hydrostatic forces. Ex-pressed in the body frame, the hydrostatic forces and mo-ments can be written as:

(12)

(13) The second components are from added mass which is the hydrodynamic force due to the acceleration of the vehicle. For a general body, the added mass is given in terms of tensor with elements of Aij representing the magnitude of the added mass in the –i direction due to acceleration in the –j direction. The values of i,j from 1 to 3 represents the masses associated with surge, sway and heave motions while those from 4 to 6 the moment of inertias associated with roll, pitch and yaw motions. Thus,

Added Mass = (14)

For UUVs having symmetry in the x-z and x-y planes, the above matrix reduces to

Added Mass = (15)

or in terms of the equivalent derivative coefficients:

Added Mass = (16)

The forces and moments due to the added mass can be ex-pressed as (17)

(18)

where the vector of added mass for forces is defined as (19)

And for moments

(20)

After appropriate substitution and expansion of cross-product, the following scalar components of added mass forces and moments can be obtained

(21)

The values of the added force and moment derivative coef-ficients are dependent of the vehicle geometry and can be calculated by Equivalent Spheroid method or Strip Theory method. The steady-state forces and moments are the result of visc-ous fluid effect and are usually calculated based on semi-empirical/empirical formula. For longitudinal case the expression of forces and moments working on UUV is summarized in Table 1.

Table 1 Longitudinal Forces and Moments of AUV In

ertia

l

Hyd

rost

atic

s

Add

ed M

ass

Stea

dy S

tate

Prop

ulsi

on

Con

trol

Kin

emat

ics

The control term contains three differential thrusters: δT1, δT2 and δT3. The configuration of these differential thrus-ters is illustrated in Fig. 4.


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Figure 4: Differential Thruster Configuration Linearization of the equations of motion of UUV around trim condition will be necessary for stability analysis and linear control system design. The trim condition deter-mined for the study case here is steady straight level flight. In this flight condition, surge velocity is dominantly larger than heave velocity and Euler angles and their rate is neg-ligible. Therefore the following conditions apply:

(22)

The subscript 1 indicates small perturbation to the steady state variables. The result of linearization procedure is giv-en in Table 2. To be amenable for stability analysis and control synthesis the linearized equations of motion are rewritten in state-space form. First, the matrix equations of motion can be expressed as

(23) This matrix equation can be simply written (24)

and finally the standard state-space can be expressed as

(25)

where (26)

The values of the A and B matrices content are function of flight parameters, primarily the forward speed and depth.

Table 2 Linearized Longitudinal Forces and Moments of AUV

Linerization results

Iner

tial

Hyd

rost

atic

s ;

;

Add

ed M

ass

Stea

dy S

tate

Prop

ulsi

on

Con

trol

K

inem

atic

s

The stability analysis of the AUV can therefore be con-ducted by observing the changes of root loci as function of the speed or depth variation. The detail expression for the elements of A and B matrices can be found in [104].

B. Control Synthesis The availability of the nonlinear and linear models can be exploited for various control architectures as necessary. The control synthesis presented in this section is limited for the low level controller design for the purpose of illustra-tion. The analysis and synthesis of controller are typically con-ducted in a number of representative design points e.g. for the present study the design points represent combination of speed variations (U0=0.5,1.0,1.5,2.0,2.5,3.0 m/s) and depth variations (D=50,1000m). The root locus describing the pole and zero configuration of transfer function can be drawn for the

above 12 design points, where

and (27)


Figure 5: Root locus of Gu-δT1 as speed varied for D = 50m Fig. 5 shows the root locus of Gu-δT1 with respect to speed variation evaluated for D = 50m. It is evident from the root locus diagram that the vehicle gets unstable when the speed is increased from U0=0.5 to 1.0 m/s and then gets restabi-lized when the speed increasing up to the maximum. The controller to stabilize the vehicle is therefore required for the speeds around U0=1.0 m/s. Other root locus diagrams are not shown due to space limitation. The time response analysis due impulsive input is con-ducted to investigate the dynamics characteristic of the vehicle. The result is presented in Fig. 6 for variable heave velocity w. To stabilize the AUV in the low-speed regime, a stability augmentation system (SAS) is designed. The control block diagram is given in Fig. 7 showing multi-loop control sys-tem design. The SAS is realized as an inner loop with pitch rate q as feedback. Once the inner loop gain is optimized, the Pitch Attitude Hold (PAH) is then designed as an outer loop with pitch angle θ as the feedback. Both feedback have two input channels: pitch up and pitch down channels associated with δT1 and (δT2,δT3) respectively.

Figure 6: Response of w due to impulse δT1 for D = 50m

Pitch up channel

Pitch up channel

Pitch down channel

Pitch down channel

Pitch down channel

Pitch up channel

AUV

Figure 7: Multi-loop control diagram The vehicle transfer function is expressed as

(28)

The engine and propeller is modeled as first order system

(29)

The sensors are assumed to respond much faster than other dynamical elements, thus are represented by unity.

As illustration, the root locus of the inner loop system for pitch down channel is shown in Fig. 8 for velocity U0=1.0 m/s, depth D = 50 m and negative gain. The diagram also the variation of root locus with thruster time constant τe as the parameter.

Figure 8: Root locus of inner loop system in pitch down channel

The time response analysis is performed to compare the open loop and closed loop response to impulse disturbance. The result is presented in Fig. 9 for velocity U0=1.0 m/s, depth D = 50 m. The first row is the time response of the open loop and the second that of closed loop. The diagram show that the control system can successfully stabilize the system using pitch damper as SAS. It is also indicated that the thruster or engine with faster time response perform better as expected.


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Figure 9: Impulse time response of inner loop in pitch down channel

IV. Trends in Underwater Robotics Research

Significant advances in various relevant science and engi-neering disciplines have propelled the emergence of more complex engineering systems. In the realm of underwater robotics, the advancements of technologies (new materials, computing, power, sensors) have led to the development of more advanced, yet reliable and practical underwater ve-hicles. The section identifies recent technology trends in the areas relevant to underwater robotics.

A. Autonomous system

The autonomous operation of underwater vehicle presents different level of navigational challenges compared to other robots for ground or aerial applications. The autonomous underwater vehicles operate in a highly unstructured envi-ronment where navigation information from satellites is not directly available. Other aspect of AUV operation, such as the effects of acoustic propagation is also unique to under-water environment. More and more missions require in-creasing level of autonomy of underwater vehicles includ-ing mine countermeasures, oceanographic surveys and un-der-ice operations where applications of manned submersi-ble or ROV rendered impractical or risky. The autonomous operation of underwater application also allows more refine survey unattainable by cabled UUV. The main challenge of autonomous underwater operation is maintaining the accu-racy of position over an extended mission. Under influence of strong currents or other underwater disturbances, AUVs require external references for maintaining accurate navi-gation.

All current navigation technologies used for AUVs can be generally classified into three categories: (1) dead-reckoning and inertial navigation systems, (2) acous-tic navigation, and (3) geophysical navigation techniques. The problem with exclusive reliance on dead reckoning or inertial navigation is that position error increases without

bound as the distance traveled by the vehicle increases [45]. The vehicle speed, ocean currents and quality of dead-reckoning sensor all affect the rate of the drift. The combined INS/DVL has shown major increase in naviga-tion performance only for operation near seabed. In addi-tion to this limitation, over a longer period the coupled INS/DVL is still subject to drifting position estimate. In practice the use of dead-reckoning/inertial system for a long mission needs position fix from radio or satellite na-vigation system. However, this will require the AUVs to travel at or near the surface periodically to receive update for error bounding. This requirement is clearly unattainable for deep water survey or under-ice AUVs.

In the recent decade, AUV navigation technologies are dominated by the use of dead-reckoning, INS, and acoustic systems. Increased endurance of AUVs however has caused their utilization more restrictive in terms of range and af-fordability. The state of the art problem of AUV navigation is to minimize position estimate drift of existing navigation systems over extended missions by using affordable me-thods. Geophysical methods utilizing information from AUVs’ local environment offer most affordable solution [105]. The realization of this capability using sonar will be dependent on the suitability of the environment for naviga-tion and will require technological advancements for fea-ture extraction from sonar data and modeling of underwater dynamic environments.

B. Bio-robotics

The need to improve to improve AUV performance to meet the demand of increasingly more challenging missions has led to intensive research effort in the exploration of biolog-ical principles that can be adapted for underwater vehicle engineering applications. It is known from diverse exam-ples that nature offers better solution than traditional engi-neering. Principles from nature have been manifested in various disciplines: structure and materials, power, control, hydrodynamics, and navigation. Biomimetic approach fea-tures multi-disciplinary activity that results in highly inte-grated, multi-functional system resembling real biological systems. In the context of underwater propulsion and ma-neuvering technology, significant advances have been at-tained in three different areas [27]: the biology-inspired high-lift unsteady hydrodynamics, artificial muscle tech-nology and neuroscience based control. The biological-ly-inspired methods have been envisioned to improve AUVs’ low speed maneuvering capabilities including ho-vering, small-radius turning, sinking and precision station keeping all of which are natural capabilities of aquatic an-


imals. Primary implementation of bio-robotics for AUVs has been limited to the use of hydrodynamics control sur-faces mimicking underwater animals, such as dorsal fin [81], tail [90] and pectoral fin [106]. Significant advances could be anticipated if artificial muscles can be imple-mented for such hydrodynamic surfaces under the neural control.

Figure 10: Underwater breathing insect [89]

Preliminary findings in the principle of underwater breath-ing mechanism of insects represent a different aspect of potential biomimetic application for AUV. The water boatman uses a thin layer of air as an "external lung" al-lowing it to breathe underwater, Fig. 9. By virtue of their rough, water-repellent coat, when submerged these insects trap a thin layer of air on their bodies [89]. These bubbles not only serve as a finite oxygen store, but also allow the insects to absorb oxygen from the surrounding water. If successfully implemented for a practical device, oxygen needed by fuel cells could be supplied by the mechanism to power small autonomous underwater vehicles.

C. Swarm and coordinated multi UUV

Another distinct example in nature is a coordinated swarm where a large group acts collectively to accomplish a task, but does so with very limited central control and commu-nication. There are tasks that could be much more easily solvable by collaborative networks of robots compared to a single multi-functional robot. In the realm of underwater application, this principle has been implemented for vari-ous missions: maritime domain awareness [59, 65], mine-fields reconnaissance and object mapping [40,48,49,68], target tracking [93], high performance navigation [107].

The viability of the above application is derived from fleet behavior which can be employed to accomplish large scale tasks, while providing fault tolerance and flexibility. Al-though hardware requirements differ greatly among differ-ent implementations, a common component to the devel-

opment of these types of systems is guidance algorithms that can translate the high-level system behavior into low-level stimulus and response actions for individual ele-ments [108]. It is important in this regards to be able to derive and analyze collective robotic behavior rather than the response of an individual agent.

An emerging application for multi UUV system includes oceanic exploration and observation. The use of coordi-nated groups of simple single-sensor UUV for oceanic ex-ploration offers many advantages: higher fault tolerance, more effective search and higher navigation performance.

V. Concluding Remarks

The paper discussed recent progress in the technology for unmanned underwater vehicles from the modeling, control and guidance perspectives. The survey of contemporary AUVs is briefly presented and innovative approaches for enhancing their performance are highlighted. Dynamics of unmanned underwater vehicle is derived to describe the importance of modeling in the control synthesis. A mod-el-based low level controller is presented for illustration. The three major trends in underwater robotics are dis-cussed: autonomous system, biorobotics approach and mul-ti UUV system. Future challenges for advancing underwa-ter robotics technology will be pivoted on finding accurate, robust yet affordable navigation technology for longer mis-sion, exploitation of biomimetic principles for viable prod-ucts and development of formal model and analysis tool to synthesize collaborative underwater robotics behavior.

References

1. T. Ura, AUV ‘r2D4’, Its Operation, and Road Map for AUV Devel-opment. In “Advances in Unmanned Marine Vehicles” edited by G.N. Roberts & R. Sutton, IEE Control Series 69, 2006

2. T.I. Fossen and A. Ross, Nonlinear Modelling, Identification and Control of UUVs. In “Advances in Unmanned Marine Vehicles” edited by G.N. Roberts & R. Sutton, IEE Control Series 69, 2006

3. Feijun Song and S.M. Smith, Design of sliding mode fuzzy control-lers for an autonomous underwater vehicle without system model, OCEANS 2000 MTS/IEEE Conference and Exhibition, Volume 2, 11-14 Sept. 2000, pp.835 – 840

4. C. Gaskett, D. Wettergreen, A. Zelinsky, Reinforcement Learning applied to the control of an Autonomous Underwater Vehicle, Aus-tralian Conference on Robotics and Automation, Brisbane, Australia, pp. 125-131, March 1999

5. R.K. Lea, R. Allen, and S.L.Merry, A Comparative study of control techniques for an underwater flight vehicle. International Journal of System Science, 1999, volume 30, number 9, pages 947-964


AR

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6. D.B.Marco, A.J.Healey, R.B.McGhee, Autonomous Underwater Vehicles: Hybrid Control of Mission and Motion, Autonomous Ro-bots 3, 1996, pp.169-186

7. David B. Marco, Anthony J. Healey , Command, Control, and Navi-gation Experimental Results With the NPS ARIES AUV, IEEE Journal Of Oceanic Engineering, Vol. 26, No. 4, October 2001

8. J. Riedel, A. Healey, Model Based Predictive Control of AUVs for Station Keeping in a Shallow Water Wave Environment, Proceedings International Advanced Robotics Program MRF’ 98, pp.77-102, University of South Louisiana, February 18, 1998.

9. Feijun Song, Edgar An and Samuel Smith, “Design of robust nonli-near controllers for autonomous underwater vehicles with compari-son of simulated and at-sea test data”, Journal of Vibration and Con-trol, vol. 8 pp. 189—217, 2002

10. J. Yuh, “A Neural Net Controller For Underwater Robotic Vehicles,” IEEE Journal of Oceanic Engineering, vol.15, no.3, pp.161-166, 1990.

11. L. Lapierre, D. Soetanto, Nonlinear Path-Following Control of an AUV, Journal of Ocean Engineering, 2007, Vol.34, pp.1734-1744

12. K. Kim, H.S. Choi, Analysis, On the Controlled Nonlinear Motion of a Testbed AUV-SNUUV I, Journal of Ocean Engineering, 2007, Vol.34, pp.1138-1150

13. L. Lapierre, D. Soetanto, A. Pascoal, Nonlinear Path Following with Applications to the Control of Autonomous Underwater Vehicles, Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, Hawaii USA, December 2003

14. J.H. Li, B.H. Jun, P.M. Lee, S.W. Hong, A Hierarchical Real-Time Control Architecture for A Semi-Autonomous Underwater Vehicle, Journal of Ocean Engineering, 2005, Vol.32, pp.1631-1641

15. V. Sankaranarayanan, A.D. Mahindrakar, R.N. Banavar, A Switched Controller for an Underactuated Underwater Vehicle, Journal of Communications in Nonlinear Science and Numerical Simulation, 2007

16. H.S. Kim, Y.K. Shin, Expanded Adaptive Fuzzy Sliding Mode Con-troller using Expert Knowledge and Fuzzy Basis Function Expan-sion for UFV Depth Control, Journal of Ocean Engineering, 2007, Vol.34, pp.1080-1088

17. T.K. Podder, N. Sarkar, Fault-Tolerant Control of an Autonomous Underwater Vehicle Under Thruster Redundancy, Journal of Robot-ics and Autonomous System, Vol.34, 2001, pp.39-52

18. C. Silvestre, . Pascoal, Depth Control of the INFANTE AUV using Gain-Scheduled Reduced Order Output Feedback, Journal of Con-trol Engineering Practice, Vol.15, 2007, pp. 883-895

19. M. Narasimhan, S.N. Singh, Adaptive Input-Output Feedback Li-nearizing Yaw Plane Control of BAUV using Dorsal Fins, Journal of Ocean Engineering, 2006, Vol.33, pp.1413-1430

20. W.M. Bessa, M.S. Dutra, E. Kreuzer, Depth Control of Remotely Operated Underwater Vehicles using an Adaptive Fuzzy Sliding Mode Controller, Journal of Robotics and Autonomous System, 2007.

21. G..N. Roberts and R. Sutton, Advances in Unmanned Marine Ve-hicles, IEE Control Series 69, 2006.

22. AUV Product Survey, Hydro International, September, 2006 23. J. Yuh, “Design and Control of Autonomous Underwater Robots: A

Survey,” Autonomous Robots, vol. 8, pp. 7—24, 2000. 24. Slocum Gliders website: http://www.webbresearch.com/slocum.htm 25. Spray Gliders: http://www.sio.ucsd.edu/ 26. Mark A. Moline et al., “Remote Environmental Monitoring Units:

An Autonomous Vehicle for Characterizing Coastal Environments”, Journal of Atmospheric and Oceanic Technology, vol. 22, pp. 1797—1808, November 2005.

27. Promode R. Bandyopadhyay, “Trends in Biorobotic Autonomous Undersea Vehicles”, IEEE Journal of Oceanic Engineering, Vol. 29, October 2004 (in Press)

28. Muljowidodo, S.D. Jenie, A. Budiyono and S. Adinugroho, Design, Development and Testing of Underwater Vehicles: ITB Experience. The International Conference on Underwater System Technology: Theory and Application, Penang, Malaysia, 18-21 July 2006

29. Muljowidodo and A. Budiyono, Recent Progress in the Research on Unmanned Underwater Vehicles at ITB, Workshop on Underwater System Technology, Kuala Lumpur, Malaysia, December 11-12, 2007

30. TK Podder and N Sarkar, Motion Planning and Control of UVMS: A Unified Dynamics-based Approach, OCEANS 2003. Proceedings, 2003

31. Vincent Rigaud, Innovation and operation with robotized underwater systems, Journal of Field Robotics, Volume 24 Issue 6, Pages 449 - 459

32. Infante: http://dsor.isr.ist.utl.pt/Projects/Infante/Report3/1.html 33. VORAM: http://www.moeri.re.kr/ 34. Urashima: http://www.nmeweb.jp/e/duties_probe_urashima.html 35. M.S. Triantafyllou and M.A. Grosenbaugh, “Robust Control for

Underwater Vehicle Systems with Time Delays,” IEEE Journal of Oceanic Engineering, vol.16, no.1, pp.146-151, 1991.

36. D.R. Yoerger and Jean-Jacques E. Slotine, “Robust Trajectory Con-trol of Underwater Vehicles,” IEEE Journal of Oceanic Engineering, vol.10, no.4, pp.462-470, 1985.

37. Estela Bicho, Pierre Mallet and Gregor Schoner, “Target representa-tion on an autonomous vehicle with low-level sensors”, The Interna-tional Journal of Robotics Research, vol. 19 No. 5, May 2000, pp. 424—447

38. A. J. Healey, Y. Kim, Control of Small Robotic Vehicles In Unex-ploded Ordnance Clearance, Proceedings of the IEEE ICRA97, Al-buquerque, New Mexico, April 20-25, 1997

39. “Solar-powered submersible robot,” Machine Design, 23, October 20, 2005.

40. Anthony J. Healey, Application of Formation Control for Mul-ti-Vehicle Robotic Minesweeping. IEEE CDC Conference 2001.

41. A. Alessandri, A.J.Healy, G.Veruggio, Robust residual generation for detection of actuator and sensor faults in unmanned underwater ve-hicles, 38th Conference on Decision and Control, 1999

42. A.J.Healey, E.P.An, D.B.Marco, Online compensation of Heading sensor bias for low cost AUVs, Proceedings IEEE AUV9 Workshop


on Underwater Navigation, Draper laboratories, Cambridge mass, Aug 21-22,1998

43. A.J.Healey, Horner, D.P, Kragelund, S., Wring, B., Quick Look report of NPS Activities, AUVFEST’05, Monterey CA, 2005.

44. Douglas P.Horner, Anthony J. Healey, Use of Artificial Potential fields for UAV Guidance and Optimization of WLAN Communica-tion, Autonomous Underwater Vehicles, 2004 IEEE/OES

45. John J.Leonard, Andrew A.Bennet, Christopher M.Smith, Hans Jacob S. Feder, Autonomous Underwater Vehicle Navigation, MIT Marine Robotics Laboratory Technical Memorandum 98-1

46. Roy M.Turner, Context-Sensitive, Adaptive Reasoning for Intelli-gent AUV Control : Orca Project Update, 9' International Sympo-sium on Unmanned Untethered Submersible Technology (AUV'95), Durham, New Hampshire, 1995, pp. 456-460.

47. Ishii, K., Fujii, T., and Ura, T. 1998. Neural network system for online controller adaptation and its application to underwater robot. In Proceedings of IEEE International Conference on Robotics & Automation, pp. 756–761.

48. A.J.Healey, J.Kim, Multiple Autonomous Vehicle Solution to Mine-field Reconnaissance and Mapping, Australian-American Confe-rence on MCM, Sydney, July 15, 1999

49. A.J Healey, J.Kim, Auto-Recorm-Bugs : Algorithms and Coopera-tive behavior for enhancing UXO clearance, Naval Postgraduate School, Dept. of Mechanical Engineering, Monterey CA, December 31, 1998

50. Anthony J. Healey, Guidance and Control for Small AUVs using DGPS and Doppler Aided Inertial Underwater Navigation, AIAA Guidance, Navigation, and Control Conference and Exhibit. Aug 5-8, 2002

51. Nakamura, Y. and Savant, S. 1992. Nonlinear tracking control of autonomous underwater vehicles. In Proceedings of IEEE Int. Conf. on Robotics and Automation, Vol. 3, pp. A4–A9.

52. Xiadong Chen, Dave Marco, Sam Smith, Edgar An, K. Ganesan, Tony Healey, 6 DOF Nonlinear AUV Simulation Toolbox, OCEANS’97. MTS/IEEE Conference Proceedings, Vol.2, October 6-9, 1997

53. A.J.Healey, D.P.Horner, NPS ARIES Forward Look Sonar Integra-tion, Center for Autonomous Underwater Vehicle Research, Naval Postgraduate School, Monterey CA, 2004

54. Anthony Beis, A Finite Element Analysis Of The Nps Autonomous Underwater Vehicle (AUV) Hull Intended To Operate In Deep Wa-ters, Naval Postgraduate School, Monterey CA, July, 2001

55. A. J. Healey1, D. B. Marco, R. B. McGhee, Autonomous Underwa-ter Vehicle Control Coordination Using A Tri-Level Hybrid Software Architecture, Proceedings IEEE Robotics and Automation Confe-rence, Minneapolis, MI. 1996 pp. 2149-2159.

56. A. J. Healey, Asynchronous Data Fusion for AUV Navigation, Cen-ter for AUV Research, Naval Postgraduate School, Monterey, CA, October 8, 1997

57. Per Espen Hagen, Dual Use Development: The HUGIN Untethered Underwater Vehicle, SMi conference ”Naval Mines”, London, 27–28 November 2000.

58. Joël Doléac, A Graphic User Interface (GUI) for generating Nps Autonomous Underwater Vehicle (AUV) Execution Script Files, Naval Postgraduate School, Monterey CA, August, 1999

59. A.J. Healey, D.P. Horner, And S.P. Kragelund, Collaborative Un-manned Vehicles For Maritime Domain Awareness, Proceedings Of The 2005 International Workshop On Underwater Robotics, Genoa, Italy, November 9-11, 2005

60. Massimo Caccia, and Gianmarco Veruggio, Acoustic motion estima-tion and guidance for unmanned underwater vehicles, International Journal of Systems Science, 1999, volume 30, number 9, pages 929- 938

61. X. Yun, E.R. Bachmann, R.B. McGhee, R.H. Whalen, R.L. Roberts, R.G. Knapp, A.J. Healey, and M.J. Zyda, Testing and Evaluation of an Integrated GPS/INS System for Small AUV Navigation, IEEE Journal Of Oceanic Engineering, Vol. 24, No. 3, July 1999

62. Anthony J. Healey, Obstacle Avoidance While Bottom Following for the REMUS Autonomous Underwater Vehicle, Proceedings of the IFAC-IAV Conference, Lisbon, Portugal, July 5-7, 2004.

63. D. B. Marco, A. J. Healey1, R. B. McGhee, D. P. Brutzman, R. Cristi, Control Systems Architecture, Navigation, and Communication Re-search Using the NPS Phoenix Underwater Vehicle, In proceedings of the 6th International Advanced Robotics Workshop on Underwa-ter Robotics, Toulon, France , 1996

64. Bjørn Jalving, Jon Kristensen, and Nils Størkersen, Program Philos-ophy And Software Architecture For The HUGIN Seabed Surveying UUV, Norwegian Defence Research Establishment

65. A. J. Healey, D. P. Horner, Tactical Decision Aids High Bandwidth Links Using Autonomous Vehicles, Collaborative Unmanned Ve-hicles for Maritime Domain Awareness, Naval Postgraduate School, Monterey Ca, 2004

66. LT Lynn R Fodrea, Anthony J. Healey, Obstacle Avoidance Control for the REMUS Autonomous Underwater Vehicle, MSME Thesis, Naval Postgraduate School, Dec. 2001

67. Marco, D., Martins, A., and Healey, A.J., "Surge Motion Parameter Identification for the NPS Phoenix AUV , Proceedings of The First International Workshop on Autonomous Underwater Vehicles for Shallow Waters and Coastal Environments, Lafayette LA, 17-19 February 1998

68. A. J. Healey, J. Kim, Modeling and Simulation Methodology for Reconnaissance in VSW Minefields with Multiple Autonomous Vehicles, Naval Postgraduate School, Monterey CA

69. A. J. Healey, J. Kim, Control and Random Searching with Multiple Robots, proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, December, 2000

70. Tabaii, S.S., El-Hawary, F., and El-Hawary, M. 1994. Hybrid adap-tive control of autonomous underwater vehicle. In Proceedings of Symposium of Autonomous Underwater Vehicle Technology, pp. 275–282.

71. Brutzman, D., Healey, T., Marco, D., and McGhee, B., “The Phoenix Autonomous Underwater Vehicle,” AI-Based Mobile Robots, MIT/AAFI Press, 1997


AR

TIC

LES

72. P. E. An, A. J. Healey, J. Park, and S. M. Smith, Asynchronous data fusion for AUV navigation via heuristic fuzzy filtering techniques, in Proc. IEEE, Oceans ’97, Halifax, Oct. 1997

73. Healey, A. J., Marco, D. B., "Current Developments in Underwater Vehicle Control and Navigation: The NPS ARIES AUV". Proceed-ings of IEEE Oceans 2000

74. Healey, A. J., Analytical redundancy and fuzzy inference in AUV fault detection and compensation, in: Proc. of Oceanology-1998, Brighton, 1998, pp. 45-50

75. Smith, S.M., McPhail, S., Healey, A., Russell, R., Lontalk as a stan-dard protocol for underwater sensor platforms, OCEANS '97. MTS/IEEE Conference Proceedings, Volume 1, 6-9 Oct. 1997, pp.267

76. A. Huster, S. D. Fleischer, and S. M. Rock, "Demonstration of a Vision-Based Dead-Reckoning System for Navigation of an Under-water Vehicle," Proceedings of the OCEANS 98 Conference, pp. 326-330, Nice, France, September 1998. MTS/IEEE

77. Riedel, J.S., Healey, A.J., "Estimation of Directional Wave Spectra from an Autonomous Underwater Vehicle (AUV)" Proceedings of 11th International Symposium on Unmanned Untethered Submersi-ble Technology (UUST ’99), Durham, NH, Autonomous Underseas System Institute, 140–149

78. A.Alessandri, T.Hawkinson, A.J.Healey, G.Veruggio, Robust Mod-el-Based Fault Diagnosis for Unmanned Underwater Vehicles using Sliding Mode-Observers, 11th Internaional Symposium on Un-manned Untethered Submersible Technology (UUST’99), Augustus 22-25, 1999

79. J.S.Riedel, A.J.Healey, D.B.Marco, B.Beyazay, Design and Devel-opment of Low Cost Variable Buoyancy System for the Soft Groun-ding of Autonomous Underwater Vehicles, Naval Postgraduate School, Center of AUV Research, Monterey CA

80. A.J. Healey, S.M.Rock, S.Cody,D.Miles, and J.P.Brown, Toward an Improved Understanding of Thruster Dynamics for Underwater Ve-hicles, IEEE Journal of Oceanic Engineering, Vo.20, No.4, October 1995

81. M. Narasimhan, S.N. Singh, Adaptive Optimal Control of An Auto-nomous Underwater Vehicle in The Dive Plane Using Dorsal Fins, Journal of Ocean Engineering, 2006, Vol.33, pp.404-416

82. P. Kiriazov, E. Kreuzer, F.C. Pinto, Robust Feedback Stabilization of Underwater Robotic Vehicles, Journal of Robotics and Autonom-ous Systems, Vol.21, 1997, pp.415-423

83. M.S. Naik, S.N. Singh, State-Dependent Riccati Equation-Based Robust Dive Plane Control of AUV with Control Constraints, Jour-nal of Ocean Engineering, Vol.34, 2007, pp.1711–1723

84. T.W. Kim, J. Yuh, Development of A Real-Time Control Architec-ture for a Semi-Autonomous Underwater Vehicle for Intervention Mission, Journal of Control Engineering Practice, Vol.12, 2004, pp. 1521-1530

85. M. Caccia, G. Casalino, R. Cristi, G.Verugio, Acoustic Motion Esti-mation and Control for an Unmanned Underwater Vehicle in a Structured Environment, Journal of Control Engineering Practice, Vol.6, 1998, pp. 661-670

86. M. Caccia, G. Bruzzone, G. Veruggio, Active Sonar-Based Bot-tom-Following for Unmanned Undewater Vehicles, Journal of Con-trol Engineering Practice, Vol.7, 1999, pp.459-468

87. E. Bovio, D. Cecchi, F. Baralli, Autonomous Underwater Vehicles for Scientific and Naval Operations, Annual Reviews in Control, Vol.30, 2006, pp.117-130

88. M. Chyba, T. Haberkorn, R.N. Smith, S.K. Choi, Design and Im-plementation of Time Efficient Trajectories for Autonomous Under-water Vehicles, Journal of Ocean Engineering, 2008, Vol.35, pp.63-76

89. M.R. Flynn and J. W. M. Bush, Underwater breathing: the mechan-ics of plastron respiration, J. Fluid Mech. (2008), vol. 608, pp. 275–296.

90. A. Suleman, C. Crawford, Design and Testing of A Biomimetic Tuna using Shape Memory Alloy Induced Propulsion, Journal of Comput-ers and Structures, Vol.86, 2008, 491-499

91. S.C. Yu, Development of Real-Time Acoustic Image Recognition System using by Autonomous Marine Vehicle, Journal of Ocean En-gineering, 2008, Vol.35, pp.90-105

92. J. Amat, A. Monferrer, J. Batlle, X. Cufi, GARBI: a Low-Cost Un-derwater Vehicle, Journal of Microprocessors and Microsystems, Vol.23, 1999, pp.61–67

93. Y. Hou, R. Allen, Intelligent Behaviour-Based Tam UUVs Coopera-tion and Navigation in a Water Flow Environment, Journal of Ocean Engineering, 2008, Vol.35, pp.400-416

94. B.H. Cho, S.H. Byun, C.H. Shin, J.B. Yang, S.I. Song, J.M Oh, KeproVt: Underwater Robotic System for Visual Inspection of Nuclear Reactor Internal, Journal of Nuclear Engineering and De-sign, Vol.231, 2004, pp.327-335

95. M.C. Fang, C.S. Hou, J.H. Luo, On the Motions of the Underwater Remotely Operated Vehicle with the Umbilical Cable Effect, Journal of Ocean Engineering, 2007, Vol.34, pp.1275-1289

96. J. Guo, Optimal Measurement Strategies for Target Tracking by a Biomimetic Underwater Vehicle, Journal of Ocean Engineering, 2008, Vol.35, pp.473-483

97. P.E. Hagen, N. Størkersen, B.E. Marthinsen, G. Sten, K. Vestgård, Rapid Environmental Assessment with Autonomous Underwater Ve-hicles-Examples from HUGIN Operations, Journal of Marine Sys-tems, Vol.69, 2008, pp.137–145

98. S.M. Zanoli, G. Conte, Remotely Operated Vehicle Depth Control, Journal of Control Engineering Practice, Vol.11, 2003, pp. 453-459

99. J.K. Choi, T. Shiraishi, T. Tanaka, Safe Breakwater-Following Con-trol of an Autonomous Underwater Vehicle with Non-Zero Forward Velocity, Journal of Automation in Construction, Vol.16, 2007, pp.778–786

100. A. Alcocer, P. Oliveira, A. Pascoal, Study and Implementation of an EKF GIB-based Underwater Positioning System, Journal of Control Engineering Practice, Vol.15, 2007, pp. 689-701

101. D.R. Blidberg, The Development of Autonomous Underwater Ve-hicle (AUV): A Brief Summary, Proceedings 2001 ICRA.IEEE In-ternational Conference, 2001.


102. M.S. Triantafyllou and Franz Hover, Maneuvering and Control of Marine Vehicles, MIT Mechanical Engineering, Lecture Notes Course 13.49, 2003

103. E. M. Lewandowski, The Dynamics of Marine Craft: Maneuvering and Seakeeping, World Scientific Publishing Company, 2003

104. A. Budiyono, A. Sugama and Muljowidodo, Dynamics Analysis of AUV Sotong, USYS08, Bali, November, 2008

105. Luke Stutters, Honghai Liu, Carl Tiltman, and David J. Brown, Navigation Technologies for Autonomous Underwater Vehicles, IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews, Vol. 38, No. 4, July 2008

106. Patar Ebenezer Sitorus, Yul Yunazwin Nazaruddin, Edi Leksono and A. Budiyono, Design, and Implementation of Paired Pectoral Fins Locomotion of Labriform Fish Applied to a Fish Robot, USYS08, Bali, November, 2008

107. Jules S. Jaffe, Multi Autonomous Underwater Vehicle Optical Imag-ing for Extended Performance, OCEANS 2007 – Europe, 18-21 June 2007

108. Emilie J. Siochi et.al., Biomimetics for NASA Langley Research Center, NASA/TM-2002-211445, 2002

Recent Progress of UUV-Keynote Part 1

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