IEEE TRANSACTIONS ON ROBOTICS, VOL. 21, NO. 4, AUGUST 2005 695
Experimental Study on AdvancedUnderwater Robot ControlSide Zhao, Member, IEEE, and Junku Yuh, Fellow, IEEE
Abstract—The control issue of underwater robots is very chal-lenging due to the nonlinearity, time variance, unpredictable ex-ternal disturbances, such as the sea current fluctuation, and thedifficulty in accurately modeling the hydrodynamic effect. Con-ventional linear controllers may fail in satisfying performance re-quirements, especially when changes in the system and environ-ment occur during the operation since it is almost impossible tomanually retune the control parameters in water. Therefore, it ishighly desirable to have an underwater robot controller capableof self-adjusting control parameters when the overall performancedegrades. This paper presents the theory and experimental workof the adaptive plus disturbance observer (ADOB) controller forunderwater robots, which is robust with respect to external distur-bance and uncertainties in the system. This control scheme con-sists of disturbance observer (DOB) as the inner-loop controllerand a nonregressor based adaptive controller as the outer-loop con-troller. The effectiveness of the ADOB was experimentally investi-gated by implementing three controllers: PID, PID plus DOB, andADOB on an autonomous underwater robot, ODIN III.
Index Terms—Adaptive control, disturbance observer (DOB),underwater robots.
I. INTRODUCTION
OCEANS are the main resource of the energy and chemicalbalance that sustains mankind whose future is very much
dependent on the living and nonliving resources in the oceans[1]. Oceans’ activities are also critically relevant to climatechanges. Therefore, various studies have been conducted forocean exploration and intervention. Underwater vehicles havebeen a popular and effective means for ocean exploration andintervention as they make it possible to go far beneath the oceansurface, collect first-hand information about how the oceanswork, and furthermore perform intervention tasks.
There are three types of underwater vehicles. 1) Mannedsubmersible vehicles: They can carry out complicated tasksbecause of human intelligence. However, they have short en-durance due to human physical and psychological limitations,and are costly to operate because of the endeavor done to ensure
Manuscript received June 3, 2004; revised August 8, 2004. This paper wasrecommended by Associate Editor W. K. Chung and Editor I. Walker upon theevaluation of the reviewers’ comments. This work was sponsored in part by theNational Science Foundation under Grant BES97-01614, in part by the Office ofNaval Research under Grants N00014-97-1-0961 and N00014-00-1-0629, andin part by KRISO/KORDI via MASE. Any opinions, findings and conclusions,or recommendations expressed in this material are those of the authors and donot necessarily reflect the views of the funding agencies.
S. Zhao is with Department of Mechanical Engineering, University of Hawaiiat Manoa, Honolulu, HI 96822 USA (e-mail: [email protected]).
J. Yuh is with National Science Foundation, Arlington, VA 22230 USA(e-mail: [email protected]).
Digital Object Identifier 10.1109/TRO.2005.844682
human safety. Alvin at the Woods Hole Oceanographic Insti-tute and Pisces V at the NOAA Hawaii’s Undersea ResearchLaboratory are examples of manned submersible vehicles.2) Remotely operated vehicles (ROV): They are unmanned,tethered vehicles with umbilical cables to transfer power, sensordata and control commands between the operators on the sur-face and the ROV. They are usually launched from surfaceships. They can also carry out complicated tasks via tele-oper-ation by human pilots on the surface ships. Even though theiroperations are often limited by operator fatigue, they are freefrom the safety concern of on-board human operators and havealmost unlimited endurance in the ocean, compared to mannedsubmersibles. However, the dragging force on the tether, timedelay, and operator fatigue make ROV difficult to operate andthe daily operating cost is still very expensive .KAIKO from the Japanese Marine–Earth Science and Tech-nology Center (JAMSTEC) was the most advanced ROV everoperated at a 11 000-m depth. Unfortunately, KAIKO was lostduring the operation in 2003 as the tether was snapped due tobad weather. 3) Autonomous underwater vehicles (AUVs) orunderwater robots: They are unmanned, tether-free, poweredby onboard energy sources, equipped with various navigationsensors such as inertial measurement unit (IMU), sonar sensor,laser ranger, and pressure sensor, and controlled by onboardcomputers for given missions. They are more mobile andcould have much wider reachable scope than ROV. On-boardpower and intelligence could help AUV self-react properlyto changes in the system and its environment, avoiding anydisastrous situation like the KAIKO case. With the continuousadvance in control, navigation, artificial intelligence, materialscience, computer, sensor, and communication, AUVs havebecome a very attractive platform in exploring the oceans,and numerous AUV prototypes have been proposed, such asODIN [2], REMUS [3], and ODYSSEY [4]. While most ofthe currently available AUV are for noncontact tasks such asmapping, monitoring or sampling in the water column, researchon AUV with robotic manipulators has recently been underway[5]. Various underwater robotic technologies were surveyed byYuh and West [6].
The control issue of AUV is very challenging due to the non-linearity, time-variance, unpredictable external disturbances,such as the environmental force generated by the sea currentfluctuation, and the difficulty in accurately modeling the hy-drodynamic effect. The well-developed linear controllers mayfail in satisfying performance requirements especially whenchanges in the system and environment occur during the AUVoperation since it is almost impossible to manually retune thecontrol parameters in water. Therefore, it is highly desirable
696 IEEE TRANSACTIONS ON ROBOTICS, VOL. 21, NO. 4, AUGUST 2005
to have an AUV controller capable of self-adjusting controlparameters when the overall performance degrades. Variousadvanced control schemes for underwater robots have beenproposed in the literature as some of them are summarizedbelow.
Sliding mode control (SMC): SMC restricts the system statesinside a certain subspace of the whole state space and makesthem asymptotically converge to their equilibrium point. It re-quires a raw estimation of the system parameters and an estima-tion of the system uncertainty for the switching surface designand variable-structure control law design. Even though SMC hasbeen well known for its robustness to parameter variations, ithas the inherent problem of chattering phenomenon. Yoergerand Newman [7] and Yoerger and Slotine [8] introduced thebasic methodology of using sliding mode control for AUV ap-plication, and later Yoerger and Slotine [9] developed an adap-tive sliding mode control scheme, in which a nonlinear systemmodel was used. When the generalized disturbance makes thesystem state exceed the sliding mode tolerance layer, the ex-ceeding value is used to update the nonlinear model parame-ters and furthermore update the control input. Song and Smith[10] introduced a sliding mode fuzzy controller that uses Pon-tryagin’s maximum principle for time-optimal switching sur-face design, and uses fuzzy logic to form this surface.
Robust/optimal control: The principles of the robust/optimalcontrol are calculus of variations, Pontryagin maximum prin-ciple, and Bellman dynamic programming. However, due to thedifficulty of deriving an accurate model of AUV system, it is dif-ficult to apply optimal control directly. Therefore, generally op-timal control combined with system identification or robust con-trol is used in AUV control. Kim et al. [11] proposed ancontrol scheme, in which the robust stability problem againsttime delays and parameter uncertainties is transformed intocontrol problem, and performance problem is transformed into
problem. Riedel and Healey [12] proposed an optimal con-trol (LQR) scheme that uses an auto-regression (AR) model topredict the wave-induced hydrodynamic disturbance.
Adaptive control: Adaptive control modifies control gains ac-cording to the changes in the process dynamics and the distur-bances. Since there are parameter uncertainties and unknowndisturbances in the underwater vehicle’s hydrodynamics, manyresearchers studied adaptive control to address the AUV con-trol issues. However, adaptive control may fail when the dy-namics changing speed is beyond its adapting capability, andthe model-based adaptive control may be calculation burden-some because of the excessive endeavor in system identifica-tion. Cristi and Healey [13] proposed a model-based adaptivecontroller. Assuming that the vehicle dynamics are nearly linearwithin the range of its operating conditions, the controller usesthe RLS method for system parameter estimation and, futher-more, uses the pole placement technique for control gain desgin.Yuh [14] proposed a discrete-time adaptive controller using aparameter adaptation algorithm. Yuh [15], and Yuh and Nie[16] proposed a nonregressor-based adaptive control schemethat uses parametric bound estimation, instead of system param-eter estimation, to tune the control gains.
Neural network (NN) control: Neural networks attractedmany researchers because they can achieve nonlinear mapping.
Using NN in constructing controllers has the advantage thatthe dynamics of the controlled system need not be completelyknown. This makes NN suitable for underwater vehicle con-trol. However, NN-based controllers have the disadvantagethat no formal mathematical characterization exists for theclosed-loop system behavior. The validation of the final designcan only be demonstrated experimentally. There are mainlytwo approaches in using NN for control purpose: learning witha forward model and direct learning. In the former approach,generally, the forward model is trained by the output error orstate error and then used for gain derivation, while in the latterapproach, the state or output error is used directly to map thedesired control input [17]. Yuh [18] described a multiplayerfeedforward network. Each layer has 13 neurons, except thelast layer that has six neurons. The input signals are six positionerrors, six velocity errors and a constant. The output signals arethe six control forces. The back-propagation (BP) algorithm isused for training the network. Ishii et al. [19] proposed a neuralnetwork system that is based on self-organizing neural-netcontrol system (SONCS) that executes identification of robotdynamics and controller adaptation in parallel with robotcontrol and adjusts the controller network based on the resultsof virtual operation of the control calculation and the actualcontrol operation.
Fuzzy logic control: The theoretical basis of fuzzy logic con-trol is that any real continuous function over a compact set canbe approximated to any degree of accuracy by the fuzzy infer-ence system. For control engineering applications, researchersuse fuzzy logic to form a smooth approximation of a nonlinearmapping from system input space to system output space. Thismakes it suitable for nonlinear system control. However, deter-mining the linguistic rules and the membership functions re-quires experimental data and, therefore, very time-consuming,and the rule-based structure of fuzzy logic control makes it dif-ficult to characterize the behavior of the closed-loop system inorder to determine response time and stability. Kato et al. [20]used a very basic fuzzy controller in AQUA EXPLORER 1000cable inspection. Lee et al. [21] proposed a self-adaptive neuro-fuzzy inference system (SANFIS) that uses a five-layer-struc-tured NN to achieve better function approximation: a recursiveleast squares algorithm and a modified Levenberg–Marquardtalgorithm with limited memory are used in extracting fuzzyrules and tuning the membership functions. Kim and Yuh [22]proposed a fuzzy membership function-based neural networks(FMFNNs) that uses a BP network for fuzzy control’s member-ship function derivation.
This paper describes an adaptive plus DOB (ADOB) con-troller for underwater robots as an extension of the second au-thor’s previous study on a nonregressor based adaptive con-troller [16], [23]. The nonregressor based adaptive controllerdoes not require any physical information about the robot modelexcept the number of inputs and the number of outputs. Asdemonstrated by experimental investigation in [16], [23], thenonregressor based adaptive controller is very effective for au-tonomous underwater vehicles whose hydrodynamics cannot beaccurately modeled or may vary while in operation as changesin the system and environment occur. However, the adaptivecontroller does not address robustness with respect to external
ZHAO AND YUH: EXPERIMENTAL STUDY ON ADVANCED UNDERWATER ROBOT CONTROL 697
Fig. 1. Diagram of the ADOB controller.
disturbances while DOB is very robust with respect to externaldisturbances. While more details about DOB can be found in[24]–[26], it can be briefly described as follows. The DOB ba-sically removes the effect of external disturbances and mod-eling errors, and makes the system behave close to a nominalmodel that was prechosen by the user. Then the user designsan outer-loop controller, such as PID that controls the overallsystem. However, the DOB controllers require using a low-passfilter affected by the nominal model, and, therefore, the perfor-mance of the DOB controller such as PID plus DOB varies de-pending on the nominal model or the low-pass filter. As shownin Fig. 1, this paper presents an ADOB controller using DOB asan inner-loop compensator, taking advantage of its robustnesswith respect to disturbances, and using the nonregress basedadaptive controller as an outer-loop controller, taking advan-tage of its robustness with respect to model uncertainties dueto the nominal model or the low-pass filter. The effectiveness ofthe control system was experimentally investigated by imple-menting three controllers: PID, PID plus DOB, and ADOB onan autonomous underwater robot, ODIN III. The PID controllergains were manually tuned for satisfactory performance and itsresult was used as the baseline performance in this study. Thepaper is organized as follows. Section II describes the ADOBcontroller and Section III presents experimental results beforeconclusions in Section IV.
II. CONTROLLER DESIGN
There are two coordinate systems that are commonlyused to describe the AUV kinematics: the earth-fixedframe (E-frame) and the body-fixed frame (B-frame). Theposition and orientation of the vehicle are described inthe E-frame, while the linear and angular velocity andthe control forces/moments are described in the B-frame:
position and orientation vectorin E-frame; velocity vector inB-frame; and force/momentvector in B-frame.
The velocity vectors in E-frame and in B-frame have the fol-lowing relationship:
(1)
where is the transformation matrix between the B-frameand the E-frame.
The dynamics of AUV in the B-frame can be representedby a six-degree-of-freedom (DOF) nonlinear dynamic equationshown as follows:
(2)
where is the inertial matrix including added mass; isthe matrix of Coriolis and centripetal terms, and velocity-depen-dent terms due to added mass; is the damping matrix in-cluding terms representing drag forces; is the gravitationalforce and buoyant force; is the control input; is the externaldisturbance. Detailed description of (1) and (2) can be found inFossen [27].
The overall control system as shown in Fig. 1, where isthe desired system output, is the system output, is the ex-ternal disturbance, is the output measurement noise, andis the nominal model that can be chosen by the user. It has theinner loop compensator of DOB inside the dotted line and theouter loop of the adaptive controller. It can be easily seen thatthe system compensated by DOB becomes the nominal model(i.e., assuming no measurement noise) if thelow-pass filter . It would be straightforward to designthe outer-loop controller to control the nominal model that isknown. However, the low-pass filter is needed since the inverseof the nominal model cannot be realized. First proposed in [24]and later refined in [25] and [26], DOB can remove the effectof the external disturbance and the modeling error, which canbe referred as the generalized disturbance, in the bandwidthof the low-pass filter . However, because of the phase delayand bandwidth restriction of the low-pass filter , there is al-ways an estimation error of the generalized disturbance, and,therefore, the overall performance with a simple outer-loop con-troller, such as PID, may vary depending on affected by thenominal model. Therefore, the system compensated by DOBwith could be seen as a nominal model with disturbance es-timation error that would be handled by the adaptive controller.
Consider the following nominal model for in the body-fixed frame in designing DOB
(3)
After applying DOB, the system dynamics becomes
(4)
(5)
698 IEEE TRANSACTIONS ON ROBOTICS, VOL. 21, NO. 4, AUGUST 2005
where represents the estimation error, and isshown as in Fig. 1.
The DOB compensated system shown in (4) could berewritten in the E-frame using as follows [27]:
(6)
where , ,, , and represents a
class of estimation errors which are bounded by
(7)
where , is a desired value of , and , , andare positive constants. Since the system matrices are based onthe nominal model (3) chosen by the user, they are known andcan be bounded as
(8)
where , , and are positive constants.Instead of mathematically proving (7) and (8), this paper de-
scribes how to estimate
(9)
where , is a positive constant, andwhen .
Consider the following control law:
(10)
where , which is the desired acceleration, ,which is a positive constant, , are
the control gain matrices.The error equation can be obtained by combing (6) and (10) as
(11)
where , , , .The adaptive controller defines the gain matrices and the es-
timations of the as follows:
(12)
(13)
where are positive constants, are estimates of , and
(14)
where is a positive constant satisfying .It is proven in Appendix A that using the controller described
in (10), (12)–(14), the tracking error will asymptotically go to
zero and the parameter estimations will also asymptoticallyconverge to .
It is noted that the direct use of the controller shown in (12)would generate large control input signals and chattering phe-nomenon when the value of the denominator is close to zero. Toavoid this problem, the following modified controller is used:
for
for(15)
where and are positive constants. The modifiedcontroller (15) may not guarantee the asymptotic stability buttracking errors are bounded by small numbers depending on .
There are three parameters that affect the performance ofthe adaptive controller: adaptation gain , sigma , andthreshold . One can note the following: affects the timeconstant of the overall system; the adaptation gain affectsthe adaptation period; and appropriate values of the threshold
would keep the denominator in (15) from becoming the nearzero value that may cause high gain values and large controlsignals beyond saturation limits. More details about the adap-tive control part and discussion about influence of the adaptivecontrol parameters could be found in [16] and [23] with exper-imental results.
III. EXPERIMENTAL RESULTS
The experimental work of the proposed controller was carriedout on ODIN III [28], which is shown in Fig. 2. It is a six-DOFautonomous underwater robot developed by the AutonomousSystems Laboratory of the University of Hawaii. ODIN III isbased on and Windows 2000 with the Real Time eX-tension (RTX) embedded real-time system. It is a close-framedsphere-shaped vehicle that makes its dynamics in each direc-tion nearly identical. It has eight thrusters: four horizontal andfour vertical, which make ODIN III capable of six-DOF ma-neuvering and also have thrust redundancy for fault tolerancepurpose. It also has various navigation sensors including eightsonar sensors, a pressure sensor and an Inertial MeasurementUnit (IMU). The eight sonar sensors are used to measure the dis-placements in the horizontal plane, the pressure sensor is used tomeasure the depth of the vehicle, and the IMU is used to measurethe angular displacements. A Kalman filter is used to suppressthe sensor noise and to estimate the translational and angularvelocities. ODIN III has a multisampling-rate system with thesampling rate of the sonar sensors at 3 Hz and the sampling rateof the pressure sensors and the IMU at 30 Hz. The controllersampling rates are selected same as those of the sensors: 3 Hzfor surge and sway and 30 Hz for roll, pitch, yaw, and depth. Allthe tests were done in the diving pool of Kahanamoku Pool atthe University of Hawaii. Three different controllers (PID con-troller, PID plus DOB controller, and ADOB controller) wereimplemented on ODIN and the results are shown in this section.The vehicle was tested for a six-DOF desired motion shown as asolid line in Fig. 3(a) and (b) tracking in , , and in sequencewhile regulating in . The controller settings used inthe experiment are listed as follows.
ZHAO AND YUH: EXPERIMENTAL STUDY ON ADVANCED UNDERWATER ROBOT CONTROL 699
PID controller: The system was decoupled into six SISOsubsystems and a separate PID controller was designed for eachDOF not only because ODIN III is a nearly decoupled systembut also because it would be easier to manually tune gains. Aftera lengthy gain-tuning process, the following numerical valuesfor proportional, derivative and integral gains in each DOFwere used: ; ;
; ;; .
PID plus DOB controller: In constructing DOB, thelow-pass filters are all set as ,and the nominal model is set as a pure inertiasystem, where
is the rigid body inertia param-eters with and . However,because the sampling frequencies in and
are different, different are used in parameterizingthe : for ; for .The following numerical values were used for PID controlgains: ; ;
; ;; .
ADOB controller: The DOB part is the same as that of the PIDplus DOB controller. The adaptive controller part parametersare: ; , ,
, ; ,, ,
. The initial values of all controlgain in (10) were zero by setting initial values of in(12) to zero. It does not require manually tuning gain values un-
like PID since it is capable of self tuning. Before the vehiclebegan trajectory tracking, ODIN III was let to have a short ini-tial adaptation period when the vehicle adaptive control self-ad-justed control gains from zero initial values.
To show the experimental results more effectively, two per-formance indices named generalized position error (GPE) andgeneralized orientation error (GOE) are defined in (16) and (17),respectively, where are errors in
(16)
(17)
A. Effect of External Disturbance
The effect of external disturbance was considered during theexperiment. In addition to a strong water circulation in the pool,a disturbance in the horizontal plane was applied to ODIN IIIbetween the fiftieth and the one-hundredth seconds by mechan-ically holding the vehicle at the current position for the sameduration and releasing it. Fig. 3 shows results of the PID con-troller. Since the PID gains were manually tuned for satisfactoryperformance, results shown in Fig. 3 are used as the baseline per-formance in this study. Fig. 3(a) shows tracking performance in
, , and while Fig. 3(b) shows regulation performance of roll,pitch, and yaw. Fig. 3(c) and (d) show GPE and GOE, respec-tively. They show large errors around the eightieth second whenthe additional disturbance was applied. It is noted that perfor-mance of the sonar sensor degrades as it gets close to its range
700 IEEE TRANSACTIONS ON ROBOTICS, VOL. 21, NO. 4, AUGUST 2005
Fig. 3. PID control with external disturbance (a) tracking performancein [ x y z ], (b) regulation performance in [� � ], (c) generalizedposition error, and (d) generalized orientation error.
limits. GPE and GOP were computed using sensor outputs in-cluding unfiltered sonar measurements and Fig. 3(c) shows alarge noise level around the 320th second as the vehicle sen-sors are away from the wall. It is also observed in Fig. 3(a) and
Fig. 4. PID plus DOB control with external disturbance (a) trackingperformance in [ x y z ], (b) regulation performance in [� � ], (c)generalized position error, and (d) generalized orientation error.
(b) that GPE and GOE of the baseline performance are approx-imately bounded by 0.3 m and 0.06 rad, respectively.
Fig. 4(c) and (d) show GPE and GOE of the PID plus DOBcontroller, respectively. It is observed that the DOB is effective
ZHAO AND YUH: EXPERIMENTAL STUDY ON ADVANCED UNDERWATER ROBOT CONTROL 701
Fig. 5. ADOB control with external disturbance (a) tracking performance in[ x y z ], (b) regulation performance in [� � ], (c) generalized positionerror, and (d) generalized orientation error.
in reducing the effect of the external disturbance as expected.Fig. 5(c) and (d) show GPE and GOE of the ADOB controller,respectively. Compared to the results of PID and PID plus DOB,it is also observed in Fig. 5 that satisfactory performance can be
Fig. 6. PID plus DOB control with 2.0 times nominal model parameterchange (a) tracking performance in [ x y z ], (b) regulation performancein [� � ], (c) generalized position error, and (d) generalized orientationerror.
obtained by the ADOB controller whose gains were initially setto zero and then self tuned during the operation.
702 IEEE TRANSACTIONS ON ROBOTICS, VOL. 21, NO. 4, AUGUST 2005
Fig. 7. ADOB control with 2.0 times nominal model parameter change (a)tracking performance in [x y z ], (b) regulation performance in [� � ],(c) generalized position error, and (d) generalized orientation error.
B. Effect of the Nominal Model in DOB
The effect of the nominal model in DOB was also consideredby using a nominal model whose parameters are two times largerthan the nominal model used in Fig. 4. Fig. 6 shows results of
PID plus DOB with the larger nominal model. It is observed inFigs. 4 and 6 that the choice of the nominal model affects theoverall performance of PID plus DOB. Fig. 7 shows results ofADOB with the larger nominal model. It is observed in Figs. 5and 7 that the choice of the nominal model does not affect theoverall performance of ADOB as much as that of PID plus DOB.
IV. CONCLUSION
The effectiveness of a new control system, ADOB wasexperimentally investigated on an underwater robot, ODIN IIIusing PID as a baseline performance. The ADOB consists ofthe regressor-free adaptive control and DOB, taking advantagesof DOB robustness with respect to external disturbances andmodeling errors, and the regressor-free adaptive controller’srobustness with respect to uncertainties in the system model.The ADOB controller has the capability of self-tuning controlgains and adapting to changes in the system and environmentwhile PID requires a lengthy pretuning process for satisfactoryperformance. The PID would need retuning control gainswhen the performance degrades due to changes in the systemand environment. However, it is almost impossible to retunecontrol gains of underwater robots until they are brought upto the surface where hydrodynamics would change again.Therefore, as shown in the experimental result, the ADOBcontroller is promising for underwater robots, especially whenthe robot performance degrades or fails by PID type controllers.Fault-tolerant control for underwater robots including ADOBis currently considered for future study.
APPENDIX
Proof: Construct the Lyapunov function as follows:
(A1)
Differentiating (A1) along (11) with respect to time yields
(A2)
With the adaptive control law (12), (13) and , the equa-tion in the first bracket of (A2) becomes
(A3)
ZHAO AND YUH: EXPERIMENTAL STUDY ON ADVANCED UNDERWATER ROBOT CONTROL 703
and the second bracket becomes
(A4)
From (A2)–(A4), is reduced to
(A5)
Therefore, the tracking error will asymptotically go to zeroand the parameter estimations will also asymptotically con-verge to .
ACKNOWLEDGMENT
The authors would like to thank staff of the Autonomous Sys-tems Laboratory and MASE, Inc., for their assistance in testingODIN III, and the reviewers for their useful suggestions.
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Side Zhao (M’00) was born in Hebei, China, in1971. He received his B.S. degree in automotiveengineering from Hebei University of Technology,Hebei, in 1993, the M.S. degree in automotiveengineering from Jilin University of Technology,Jilin, China, in 1996, and the Ph.D. degree in me-chanical engineering from the University of Hawaii,Honolulu, in 2004.
His current research focus is on underwater roboticvehicle control.
Mr. Zhao is an active member of the Associationfor Computing Machinery.
Junku Yuh (F’05) received the B.S. degree fromSeoul National University, Seoul, Korea, in 1981and the M.S. and Ph.D. degrees from Oregon StateUniversity, Corvallis, in 1983 and 1986, respectively.
He joined the U.S. National Science Foundation(NSF) in 2001 and serves as Program Director of theRobotics Program and Computer Vision Program,Division of Information and Intelligent Systems,after 17 years as a Professor of mechanical engi-neering with the Graduate Faculty of Information andComputer Science, University of Hawaii. His main
research interests include intelligent navigation and guidance and underwaterrobotics. He has published over 120 technical articles and edited/co-editedten books in the area of robotics, including Underwater Robots (Norwell,MA: Kluwer, 1996) and Underwater Robotic Vehicles: Design and Control(Albuquerque, NM: TSI, 1995). He serves as an Associate Editor for theInternational Journal of Engineering Design and Automation and InternationalJournal of Intelligent Automation & Soft Computing. He also serves on theEditorial Board of the Journal of Autonomous Robots and the InternationalJournal of Intelligent Automation & Soft Computing.
Dr. Yuh received a 1991 Presidential Young Investigator Award from U.S.President George Bush from the NSF and a 2004 Lifetime Achievement Awardfrom World Automation Congress. He has served as an Associate Editor forthe IEEE TRANSACTION ON ROBOTICS AND AUTOMATION. He has chaired sev-eral conference, including Program Chair of the 2003 IEEE/RSJ InternationalConference on Intelligent Robots and Systems (IROS) and Program Co-Chairof the 2006 IEEE International Conference on Robotics and Automation. Hefounded and chairs the technical committee on Underwater Robotics of the IEEERobotics and Automation Society.
