development of the omni directional intelligent navigator

S.K. Choi, J. Yuh, and G.Y. Takashige

Development of the Omni- Directional Intelligent Navigator

s e e ~ e o i ~ ~ i ~ e ~ ~ e e e ~ . a ~ e e . e e e ~ e e e ~ e m e e e e ~ ~ o ~ o ~ e ~ ~ e ~ ~ e e ~ ~ ~ ~ ~ e e ~ ~ ~ e ~ ~ ~ ~ o ~ e ~ ~ ~ ~ ~ ~ e ~ ~ ~ ~ ~ ~ ~ ~ . ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

The Autonomous Systems Laboratory is in the midst of developing an advanced underwater robotic technology test platform. The platform consists of the Omni-Directional Intelligent Navigator (ODIN) and the Integrated Graphic Workstation (IGW). ODIN is a six degree-of- freedom (dof) underwater vehicle with dual operational modes (autonomous & tethered) and a single dof mechanical manipulator. IGW is a real-time, 3-dimensional graphic monitoring, testing, and evaluation workstation. This paper presents ODIN’s mechanical and electrical specifications; its vehicle dynamics and depth control system; its recent simulation and experimental results; and IGWs specifications. ~ ~ ~ ~ ~ ~ m ~ % ~ e e ) e e e ~ ~ ~ e a ~ ~ ~ e ~ e a ~ e m a ~ ~ ~ a e ~ e m ~ a ~ ~ ~ ~ ~ e ~ ~ ~ e ~ ~ ~ ~ ~ ~ e a ~ ~ o ~ ~ ~ ~ ~ ~ e ~ ~ ~ ~ % ~ ~ e ~ ~ ~ ~ o ~ a ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~

INTRODUCTION nologies will eventually arrive at the autonomous underwater Even though the ocean covers about 70% of the earth and will vehicle (Am). Some recently developed AWs are listed in Ta- have great effects on the future existence of humankind, hu- ble 1. mans have been unable to explore the full depths of this re- Autonomous vehicles require many, varied subsystems. source. The deep oceans can range between 6,000 to 11,000 Among them are Vehicle System Sensors, Mission Sensors, meters. At a mere 10 m depth, the atmospheric pressure will Vehicle Work Packages such as Manipulators, On-board Corn- be twice the normal of 29.4 psi. This obstacle compounded Puter System, Propulsion Systems, Power Systems, Emergen- with other complexities inherent to the unstructured, hazard- CY Systems, Vehicle Simulation System, Intelligent System, ous underwater environment makes it difficult to travel in the Fault Tolerance Systems, etc. [2,4,18,23,27,311 Many of these Ocean even though today’s technologies allow space travel. subsystems are also used in land-based robots, but the similar-

In the last few years, the uses of remotely operated vehicles ities cease before they begin. For instance, the position keep- (ROV) have rapidly increased due to the development of these ing aspect on a land-based robot can be pin-pointed within vehicles to perform operations in deeper and riskier areas centimeters using avision system and the Global Position Sys- where human divers cannot reach. Applications of underwater tem (Gps). Position keeping in a URV becomes a much m ~ r e robotic vehicles (URV), to mention a few, include ocean sur- complex problem. First, a vision system becomes somewhat veying, mining, munitions recovery, maintenance and con- inconclusive as the vehicle ventures into deep oceans where struction of underwater structures, and maintenance of nude- the penetration of light is shortened. Second, any external PO- ar plants [ 1,3,10-12,16,19-21,27,31]. However, the extended sitioning system, such as GPS, cannot identify these Positions, utilization of ROV~ is still limited to a few applications due to and communication is limited due to the high-density medi- operator fatigue and operation costs. A major portion of the um. Third, the vehicle is constantly under changing paramet- operation costs is incurred from the use of a tether on a ROV. ric conditions created by the undercurrents and the unstruc- First, the tether requires the presence of a mother-ship; and tured environment. second, the tether increases vehicle power consumption due In an effort to contribute to URV development, the Auton- to the drag it creates. To alleviate these nuisances, it is neces- omous Systems Laboratory (ASL) of the University of Hawaii sary to develop more intelligent and efficient U R V ~ for applica- has designed and developed the Omni-Directional Intelligent tions requiring highly complex and Navigator (ODIN) and the Inte- sophisticated performance without grated Graphic Workstation the necessity of a tether; and thus, (IGW). ODIN is an omni-direction- the drive towards the development al URV with a mechanical manipu-

lator and is capable of tethered and of advanced undenvater robot tech-

44 . IEEE Robotics &Automation Magazine March 1995

Vehicle Name

EAVE

ABE

Odyssey

Ocean Voyager

Operating Affiliation Depth

150 m

6,000 m Woods Hole Oceanic

6,000 m MIT, USA

150 m Florida Atlantic Univ. USA

MSEL, Univ. of New Hampshire, USA

Institute, USA

autonomous operations. Its distinction from other AWs is its unique closed-structured, spherical shape which provides axi- al symmetry and simplifies its hydrodynamics. It is intended to be a research vehicle with shallow water task capabilities; however, a larger scale vehicle of ODIN will be able to venture into deeper oceans. IGW is the 3-dimensiona1, graphic monitoring, testing, and evaluation workstation.

The main objective of ODIN and IGW is to develop a low- cost test environment for advanced underwater robot technologies before expensive field testing. The ODIN prototype has been field tested in the university’s dive well, and the IGW has been lab tested for various simulation and monitoring situa- tions. This paper presents ODIN’s mechanical and electrical design specifications, its dynamics and depth control scheme developments, and recent simulation and experimentation results.

MT-88

AE 1000

ODIN The configuration of ODIN is a closed-framed sphere with eight thrusters (4 horizontal and 4 vertical) and a manipulator [5] as shown in Figure 1. This configuration provides instantaneous, omni-directional (6 degrees of freedom) prowess. The manipulator is only capable of 1 dof motion; however, this is sufficient for most tasks due to the vehicle’s omni-directional capability. The vehicle can be controlled by either an on-board computer in autonomous mode or a ground computer in remote-tethered mode. The vehicle’s mechanical and electrical architectures were designed to be easily expandable for additional components. ODIN’s specifications are shown in Table 2.

The external structure is composed of two aluminum alloy hemisphere hulls. The reason for the spherical configuration choice is two-fold. First, the spherical shape is axially symmet- ric and provides equivalent drag in any direction which simplifies the hydrodynamic equation of motion. Second, the sim- plicity of shape allows easy modification to the shape to emu- late different vehicle configurations.

The four vertical thrusters allow instantaneous motions of pitch and roll by providing positive voltage to an adjacent pair of thrusters and negative voltage to the opposing adjacent pair of thrusters, and thus the pitch and roll motions may be simulated without having to re-orient the vehicle. The four horizontal thruster configuration possesses the capability of providing small incremental steps in the x, y, and yaw motions. This thruster configuration provides a two-fold advantage:

6,000 m Institute of Marine ~

Technology Problems, Russia

1,000 m KDD, Japan

Figure 1. Photo of the Omni-Directional Intelligent Navigator (ODIN) Table 2 ODIN Suecifications

Motion accuracy

Depth range

Control

Specifications

0.2 m

0 - 30 m

dual modes: remote and autonomous

Max. speed 11 knot I

Navigation Acoustic transponder, Doppler sonar, Inertial system, Radio & Satel- lite positioning aids

Vehicle dof 16 dof, omni-directional motion I

Obstacle avoidance

Self-Diagnostic System

Communication

Imaging System

Sonar & Laser

Leak detector, Temperature, Pres- sure, Voltage, Current monitoring systems, & ActuatorslSensor monitoring system

Umbilical, Acoustic, Radio, & Laser

Sonar, Electro-Optical sensor, & Magnetic sensor

March 1995 IEEE Robotics &Automation Magazine . 45

1) it allows for easy modifications to specific thruster configuration of test vehicles; and 2) it possesses an inherent thruster redundancy.

The eight-thruster configuration allows a one thruster failure in the vertical layout and a one thruster failure in the horizontal layout and still generates enough thrust to provide motion to the vehicle in all directions; thus, ODIN will be able to navigate to a target location.

A typical underwater robot has an on-board computer, a ACDC converter, a thruster servo drive unit, an interfacing circuit, A/D and D/A boards, and vehicle sensors as well as mission sensors. Table 3 shows a list of sensors in each category. Depending on required tasks and mission, a different set of work packages and mission sensors will be needed; therefore, on-board components must be compact, flexible, and of industrial standard architecture. ODIN’s overall architecture is shown in Figure 2. A detailed description of the vehicle and its components can be found in reference 1271.

IGW - A MONITORING, TESTING, EVALUATION PLATFORM The current Integrated Graphic Workstation (IGW) platform consists of ODIN, 386 PC laptop, Tektronix 29PEX X-terminal, Silicon Graphics Onyx workstation, Stereographics virtual re- ality (VR) system, Tektronix Phase 111 color printer, Wave- front’s Data Visualizer and Advanced Visualizer, and C-pro- grams consisting of the underwater robot dynamic module, the control module, the gage panel module, and the communication module. The modularity of the platform design allows for independent development, trouble-shooting, and addition of new modules. Unlike other stand-alone systems, this integrated platform allows users to test advanced underwater robot technologies with actual underwater robot hardware in a dry environment. Its capabilities of real-time, high quality 3D graphics, and VR provide realistic underwater environment using coarse and fine, local underwater mapping data.

Developing and testing advanced underwater technologies require a number of expensive wet tests - ship time, divers, and other costs. Use of this platform will reduce the number of field tests, lessen costs, and accelerate development of advanced technologies.

This platform is also used for monitoring vehicle motion since it cannot be clearly observed by optical systems due to very low light in deep oceans. The 3D graphic model and vehicle sensor signals can provide a more realistic visual perspec- tive of the vehicle operation, rather than numerical data or a “blinking dot” on a screen. It can also be used as an operator training tool. Figure 3 shows a diagram of the IGW platform, and Figure 4 shows six picture frames with ODIN’s 3D graphic model performing a navigational motion.

VEHICLE DYNAMICS Various external forces and torques - hydrodynamic, gravitational and buoyancy - act on an underwater vehicle. Additional inertia terms must be introduced and compounded with the rigid body inertia terms to account for the effective mass of surrounding fluid that must be accelerated with the vehicle. These terms are called “added mass” [14,17]. The added mass coefficients are defined as the proportionality constants which relate each of the linear and angular accelerations with each of

To Grovnd Computer

Figure 2. ODIN’s Electronic Luyout.

I Figure 3. Integrated Graphic Workstation Layout.

the hydrodynamic forces and moments they generate. For a vehicle moving in a low density fluid, such as a plane in air, the forces and moments exerted on the vehicle by the fluid motion (of the air) are negligible. However for a URV traveling at low speeds in a dense fluid (water), these effects become significant and must be included in the dynamic model. In general, the mass of the fluid displaced by the vehicle will not be equal to the mass of the vehicle. Therefore, the forces and moments produced by vehicle motion and fluid motion cannot be con- veniently combined into functions of relative motion. The drag is described as a force proportional to the square of the corresponding relative motion of the vehicle. In general, the gravitational and buoyancy forces are defined in the global coordinate system; and these terms must be transformed to the local coordinate systems for use. The resultant force and moment of a thruster configuration can be expressed as the vector sum of the force and moment from each individual thruster. The major problem encountered in thruster modeling is its highly nonlinear actuator.

This section briefly discusses the underwater robot dynamics. A more detailed description of the following equations can

46 . IEEE Robotics &Automation Magazine March 1995

be found in reference [26]. Figure 5 shows the local (vehicle) coordinate and global coordinate systems. All external forces and torques can be consolidated into the rigid body equations of motion, then the vehicle dynamic model can be compactly described as:

F = RF, (3)

where V = [u,v,w,p,q,rIT are the linear and angular velocities of the center of mass of the vehicle in the vehicle coordinate system; X=[x,y,z,$,B,y~]~ is the position and orientation in global coordinates; J is a transformation matrix which transforms the velocities of the local coordinate to the global coordinate system; Vf is a fluid velocity vector; M is an inertia matrix where M = M, (rigid body inertia) + Ma (added mass); C is a vector including all the nonlinear dynamic terms including the inertia velocity terms, terms associated with the forces and torques exerted on the vehicle by fluid motion, and drag forces and torques; G is a vector containing the vehicle's gravitational and buoyancy terms; and an F vector representing the forces and torques generated by the thruster forces FT with a matrix R that is defined by the thrusters and the control surface configuration. In general, the vehicle motion is described by highly nonlinear coupled differential equations with parameter uncertainties. However, as mentioned earlier, ODIN's sym- metric sphere structure simplifies the vehicle equations.

There are two types of thruster systems commonly used in the vehicle system: a torque-controlled thruster and a velocity-controlled thruster. The torque-controlled thruster has a linear, steady-state relationship between the torque and the thruster force, but its time constant depends on the propeller angular velocity. At low velocities, the effect of thruster dynamics on the overall vehicle dynamics becomes significant. Effects of a torque-controlled thruster system on vehicle dynamics were investigated in detail in reference 1241.

ODIN's thruster system is velocity-controlled. The velocity- controlled thruster has a servo velocity feedback loop as shown:

TUiRi+Qi = Usi (4)

where the subscript i indicates the i-th thruster, Us is the servo velocity control input, and T, represents a time constant. The servo velocity loop is usually designed to have a much smaller time constant than the overall system's time constant.

The thrust force F T ~ of the i-th thruster is proportional to the absolute square of angular velocity:

FTi = CTiUi (5)

in the Ocean Floor Environment.

CTi = K,,pD, 4

where Ui = RilQil; Cti is a constant; ai is the propeller angular velocity; Di is the diameter of the propeller; and KTi are the thruster coefficients. Ct may change for forwardhackward thrust due to the obstruction of the thruster housing. The value of Ct can be determined by experiment. The effect of motor saturation is introduced by thruster force limits F T ~ ~ ~ and F T ~ ~ ~ . The desired motion of the vehicle must be well planned considering the thruster saturation limits. A detailed description for deriving the Ct, F T ~ ~ ~ , and F T ~ ~ is described in the following section.

SIMULA TIONSIEXPERIMENTS Tests were conducted on ODIN in a dive well for system calibration as well as identification of the dynamics. Both linear and nonlinear controllers were then investigated for ODIN's depth motion. Experimental results of calibration, system identification, proportional-derivative (PD) controller and proportional-integral-derivative (PID) controller are presented along with the simulation results for the PD, PID, and adaptive controllers. A detailed theoretical description and discussion can be found in references [7,261.

CA LIBRA TlON Several different experiments were performed to determine


Global Coordinate

System

\

Z

Transformation

\

+ Y

I z ligure 5. Local and Global Coordinate Systems.

the system parameters for the C f of thruster dynamics and the hydrodynamic coefficients.

A single thruster experiment was set up to examine its characteristics. As expected, the result of the input voltage and the motor velocity shows a linear relationship between the steady-state values due to its velocity servo-loop. However, Figure 6 shows a nonlinear relationship between the steady- state values of the motor velocity and the thruster force; but it is proportional to the square of the motor speed with different coefficients for the positive and negative speeds as shown in Equation (5).

The two curve fittings to the experimental data in Figure 6 show the values for C, as 6.835 x [N/(rad/~)~] for the pos-

itive rpm (diving) and 2.339 x [N/(rad/s)2] for the negative rpm (surfacing).

The desired motion of the vehicle must be well planned considering the thruster saturation limits. The possible state transition region can be determined by the thruster limits. Then a desired velocity profile should be designed within that region. The possible transition region represents the system state space that can be achieved within the given thruster limits. The saturation limits for the z-directional motion for OD- IN’S model are -2.08 N 5 FT I 6.07 N. As shown in Figure 7, the region is bounded by two curves in the ri, - w plane and a desired trapezoidal velocity profile is drawn by straight lines within that region showing the maximum velocity and accel- eration. If a desired motion of the vehicle requires a velocity profile whose lines in the w - w plane are drawn outside of the possible state transition region, the desired motion may not be achieved due to saturation limits. The saturation limit for ODIN is set at +3000 RPM.

ODIN’s dynamic behavior was also observed by inputting impulse, step, and square wave signals for thruster speeds to an open-loop system. From measured input and output signals, a mathematical model of ODIN’s depth motion was estimated as follows:

12

0 200 400 -600 -400 -200

Vdocity (rad/sec)

G r e 6. Thruster Force us. Motor Velocity.

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00 w (radlsec)

Figure 7. Possible State Transition Region in w - w Plane with ODINs Thruster Saturation.

ri, = 0.247 I W (W - 0 . 0 5 ~ + 0.101 F + 0.0262 (7)

t = w (8)

where w is the velocity; and z is the position. The experimental and simulation results using Equations

(7) and (8) with various inputs can be seen in Figures 8. The jagged line indicates experimental result, and the smooth line indicates the simulated result. The graphs indicate that the experimental result follows the simulated result. This is expected when the coefficients of the model are good approximations of the actual values.

This model was also confirmed by observing ODIN’s performance of a proportional-derivative (PD) controller in a water environment versus a computer simulation of the model. As shown in Figure 9, the experimental data (jagged line) of ODIN follows the simulation data (smooth line) and both con- verge on a desired depth. The oscillation rises from the noise within ODIN’s electronics, and the initial offset is due to an initial sampling delay during the experiment.

ODIN CONTROL SCHEMES ODIN for the autonomous operation will have an intelligent control system to monitor its missions and provide modifications for its future missions. A heterarchicalhierarchical intelligent control architecture, as described in detail in reference [30], is being developed.

At its low control level, ODIN will have a redundant control system consisting of a conventional controller such as proportional-derivative (PD), an adaptive controller, and a neural network controller. A conventional controller will be used as a primary controller. An adaptive controller will be used when performance degradation due to parametric changes occur in the vehicle dynamics. The adaptive controller has the ability of

48. IEEE Robotics &Automation Magazine March 1995

8 -

I O - 12 - 14 - 16 I I I I I I I

1

5

c;

constantly making slight adjustments to its control parameters. This also eliminates the need to define operating points when dealing with linearized, conventional controllers. A neural network controller will be used when structural changes occur in the vehicle. Several control schemes [8,9,15,22,28] in each class have been studied, and their effectiveness were investigated by computer simulation, however, without considering the effects of thruster dynamics. In this section, a discrete-time adaptive velocity controller for a URV is described.

The control system is determined using a discrete-time ap- proximation of the URV dynamic model - Equations (l), (3) and (5) - which can be expressed by the following vector equation:

where k is the k-th sampling time step; Vis the velocity vector of the URV; and U is a force vector (control input vector). If the parameters - AO, A l , and B1- in the discrete-time model were known exactly, a conventional digital control law could be determined using classical methods. Since the parameters include poorly known hydrodynamic coefficients, are time- varying, and dependent on vehicle trajectory, a conventional control scheme cannot guarantee high performance in URV motion control. Therefore, a Parameter Adaptation Algorithm (PAA) is introduced to solve this problem. The PAA estimates the parameters in the discrete-time model at each sampling time step using input-output measurements from URV. These estimates are then used to adjust the controller gains to provide the required control signals. The basis of the presented control system is a linear predictor that is designed with the aim that the prediction error vanishes according to the following relation: Let

& ( k + 1 ) = c , (q- ’ ) t h e n

[ V ( k + 1 ) - \ i ( k + I ) ]

lim E (k+ I ) = 0 k+-=

where t ( K + 1) is the predicted value of V ( K + I ) ; CR (q-’)=l+q~’ C i defines the regulation dynamics; I is the

ident*ity matrix; and q-’ is the unit delay operator. Defining R=CR +A1 and A0 = Wd where W and a constant d are arbi-

trary factors of AO, Equation (10) will be satisfied if the predictor equation is

C , ( f ’ ) P ( k + 1) - R ( k ) V ( k ) + k ( k ) d + h l ( k ) U ( k ) (11)

Figure 8. Experimental fiagged line) and simulation (smooth line) Re- sults o f Open-Loop System Responses with Different Inputs: (a) No Input, (6) Impulse Input, (c) Square- Wave Input, and (d) Constant Input.

with the following PAA:


lLI ( k ) "=h,o where A denotes an estimated value,

The parameter values estimated by the predictor are then used at each time step to compute a control signal such that the output of the predictor follows the desired output:

on ODIN for experimental results for a desired depth of 8 ft. at steady-state. They both show similar performances when no payload is added (Figures 9 and lob, respectively). However, when a 1.02 kg (2.25 Ib) payload is added, the PD controller performance degrades, and the vehicle can not obtain its desired depth (Figure loa); but the PID controller performance is maintained, and the vehicle's desired depth at steady-state is reached (Figure 1Oc).

The PD, PID, and adaptive controllers were simulated with vehicle payload changes as a substitute for parametric changes. The PD and PID control gains were determined based on values of the system equation parameters in Equations (7) and (8). It should be mentioned that a change in vehicle mass effects all parameters of the dynamic Equation (7). Case 1 was the no payload case. Case 2 was the 1.02 kg payload case. The adaptive controller was also implemented for these two cases. Results of case 1 are presented in Figure 11 for the vehicle performance in terms of depth. Results of case 2 are presented in Figure 12. The results from Figures 11 indicate that the three controllers' performances are similar with slight differences in position errors. Figure 12 indicates that the PD controller is unable to compensate for the parametric change while the PID and adaptive controllers are capable of correcting for these errors. However, the PID controller does not perform as well as the adaptive controller. This is due to the PID controller not having the capability of adapting to changes in its dynamics. The adaptive controller, on the other hand, makes constant adjustments to the vehicle's dynamics as changes occur. These results indicate the adaptive controller provides the highest performance and robust position control when the vehicle is subjected to parametric changes in its dynamics during an op-

Substituting Equation (14) into Equation (1 l), and solving for U(k):

U ( k ) = W ( k )

[c,(q-')rP(k+ 1) - i i ( k ) V ( k ) -L+(k)d]

eration. During these simulations, it was also observed that the PID often reached thruster saturation limit for different payloads which caused larger parametric changes in the vehicle dynamics.

(15)

From Equations (4) and (5), the servo control input to the i-th thruster motor, U,i is computed by

where Ui is the i-th component of U. To summarize, the parameters of the predictor in the proposed control algorithm are estimated at each time step using Equations (12) and (13), then the values of these estimated parameters are used to compute the control signal in Equations (15) and (16).

The control system was implemented with ODIN's dynamic equations (5), (7), and (8). The desired output, wd, was computed by

d w = w,+p(z,-z)

DISCUSSION The design and control of an omni-directional underwater robotic vehicle, ODIN, and its integrated graphic workstation, IGW, platform have been described with initial experimental results. The design of this vehicle was motivated by the following factors with reference to existing state-of-art vehicles: 1) the lack of low-cost vehicle test-beds for developing advanced vehicle technologies; 2) rigid designs for different tasks and re-

0

2

4

6

d 8

r

E

2 10

12

where w, and z, are desired velocity and position, respectively, generated by the trapezoidal speed profile, and p is a constant for Dosition error comDensation.

l4 16 I 0 20 40 60 80 100 120

Time (sec)

The PD cdntroller with gains; Kp=l and Kd=3, and the PID controllers with gains, Kp=l, Kd=5, and Ki=0.04, are tested

' Figure 9. Experimental (jagged line) and Simulation (smooth line) Re- sults of ODINs PD Controller with Kp=l and K 8 3 .

I

50 . I€€€ Robotics &Automation Magazine March 1995

c

E 5 E n

0o 2 - - - . Desired Depth

. - _ _ _ - - -

10 - 12 - -

- - - . Desired Depth

. - _ _ _ - - -

10 - 12 - -

l4 t -1 I I I I I I

0 10 20 30 40 SO 60 'Time (sec)

- -

_..--Desired Depth - 8 -

'O t 1

(b) n 10 20 30 40 SO tro 'rime (see)

0 I I I I I 1

Figure IO. Experimental Results of ODIN Control Systems. (a) PD (Kp=I & K d d ) with 2.25Ib. Payload, (b) PID (Kp=I, Kd=5, & Ki=0.04) with No Payload, and (c) PID with 2.25 Ib. Payload.

quirements; 3) inefficient and ineffective operating proce- dures; and 4) poor performance in terms of position accuracy and response times. The flexible and redundant design of this vehicle's mechanical and electrical architectures allows easy adaptation to any changes and modifications required to im- plement different tasks or to test different vehicle configurations.

Adaptive control system experiments are continuously being conducted, and a redundant control system incorporating

12

(a' I, 20 4o 6o xn io0 120

I I I I I I

Time (sec)

0.03 I / ' - - \ I I I I

0.02

0.01

0

-0.01

-0.02

-0.03 I I I I I I I (b ) 0 20 30 60 xo 100 I20

Time (sec)

Figure 11. (a) PO, PID, &Adaptive Controller Simulation (vehicle position with no payload), (b) PO, PlD, &Adaptive Controller Simulation (vehicle position error with no payload).

a conventional, adaptive, and neural net is being developed. Their results will be presented in the future. Other studies for URVs at ASL include: adaptive coordinated motion control system of vehicle and manipulator [ 151; learning control systems [as]; real-time 3D graphic vehicle test-bed [6,30]; underwater object recognition and shape recovery [ 131; and redundant vehicle control systems [29].

Even though ODIN is essentially an experimental, test-bed vehicle, it is still capable of various shallow water tasks such as hazardous water surveying, light-weight payload maintenance, shallow water scientific sample collection, and other low-pressured scientific and industrial applications.

ACKNOWLEDGMENTS Funding for this research was provided in part by the NOAA, Of- fice of Sea Grant, Department of Commerce Institute Grant No. NA89AA-D-SG062 (poject #WOE-13), in part by the National Science Foundation PYI Grant No. BES91-57896, and in part by the Office of Naval Research through Florida Atlantic University. The views expressed herein are those of the authors and do not necessarily reflect the views of the funding agencies. This is Sea Grant Publication UNIHI-SEAGRANT-JC-95-08.

March 1995 lEEE Robotics & Automation Magazine . 51

0 1 I I I 1 I I

2 - -

-

-

\ PID

’ PID

10

0.5

0

/ -0.5 /

-1

-1.5

-2

- - _ _ _ _ _ _ _ _ 4 - PD

I I I I I

I - - - - _ _ Adaptive

/

(b) 0 20 40 60 80 100 120 Time (sec)

Figure 12. (a) PO, PID, &Adaptive Controller Simulation (vehicle position with payload), (6) PO, PID, &Adaptive Controller Simulation (vehicle position error with payload).

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[21] J. Wilson “Nuclear RSI ROV,” (communication with Mr. J. Wilson, RSI Research Ltd.), June 1992.

1221 D.R. Yoerger and J.E. Slotine, “Robust Trajectory Control of Under- water Vehicles,” IEEE Journal of Oceanic Engineering, vol. OE-10, no. 4, pp. 462-470, 1985.

1231 D.R. Yoerger, A.M. Bradley, and B.B. Walden, “The Autonomous Benthic Explorer,” Unmanned Systems, pp. 17-23, Spring 1991.

1241 D.R. Yoerger, J.G. Cooke, and J.E. Slotine, “The Influence of Thruster Dynamics on Underwater Vehicle Behavior and Their Incorporation Into Control System Design,” IEEE Journal of Oceanic Engr., vol. OE-15, no. 3, pp. 167-178, 1990.

[25] J . Yuh, “Learning Control for Underwater Robotic Vehicles,” IEEE Control Systems Magazine, Apr. 1994.

1261 J. Yuh, “Modeling and Control of Underwater Robotic Vehicles,” IEEE Transactions on System, Man and Cybernetics, vol. 20, vol. 6, 1990.

[27] J. Yuh (Editor), Underwater Robotic Vehicles: Design and Control, TSI Press, Albuquerque, NM, 1995.

[28] J. Yuh and R. Lakshmi, “An Intelligent Control System for Remotely Operated Vehicles,” Journal of IEEE Oceanic Engineering, 1992.

1291 J. Yuh, S.K. Choi, and G.Y. Takashige, “Omni-Directional Intelligent Navigator,” Intelligent Automation and Soft Computing: Trends in Research, Development, and Application (M. Jamshidi, C.C Nguyen, R. Lumia, and J. Yuh - Editors), vol. 1, pp. 637-642, 1994.

[30] J. Yuh, V. Adivi, and S.K. Choi, “Development of a 3D Graphic Test Platform for Underwater Robotic Vehicles,” The Proceedings of the 1992 ISOPE Conference, vol. 11, pp. 500-505.

1311 J. Yuh and S. Negahdaripour (Editors), “Report of the Workshop on Future Research Directions in Underwater Robotics,” NSF, UH Sea Grant, State of Hawaii, Maui, HI, Aug. 1994.

pp. 4-(16-17) & 13(9-lo), 1961.

ABOUT THE AUTHORS Song K. Choi received his B.S. in Mechanical Engineering in 1986 from Worcester Polytechnic Institute and M.Eng. in Me- chanical Engineering in 1988 from Carnegie Mellon Universi-

5 2 . IEEE Robotics &Automation Magazine March 1995

ty. From 1988 to 1991, he was a member of the Manufacturing Engineering Group of the Robotics Institute, Carnegie Mellon Universi- ty, working on design optimization, process automation, process optimization, and artificial intelligence to manufacture implementation. Since 1991 he has been at the Me- chanical Engineering Department of the Uni-

versity of Hawaii in Manoa, working towards his Ph.D. in the field of underwater robot control. His research interests are in controls, robotics, industrial automation, computer graphic animatiodsimulation, and artificial intelligence. He is a member of the ASME and IEEE.

J. Yuh is an Associate Professor of Mechani- cal Engineering and the Director of the Au- tonomous Systems Laboratory at the Univer- sity of Hawaii, Honolulu. He received the B.S. in Mechanics and Design from Seoul Na- tional University, Seoul, Korea in 1981 and the M.S. and Ph.D. in Mechanical Engineer- ing from Oregon State University, Corvallis,

in 1982 and 1986, respectively. His current research interests include adaptive control, neural network applications, robot control, underwater robotic vehicles, and modeling and control of flexible structures. He is an editor of a book, Underwater Robotics Vehicles: Design and Control, and was a guest editor of the Special Issue on Underwater Robotics of the Journal of Robotic Systems in June 1991. He is an associate editor of the International Journal of Intelligent Automation and Soft Computing. He currently serves as a Program Chair of the First International Symposium on Intelligent Automation and Control, May 1996, in France. He is an active member of the RAS Mobile Robotics Committee and has been nominated to chair a new Technical Committee on Underwater Robotics. He is an organizer and chair of a workshop on Robotic Technolo- gies in Oceanic Engineering for the 1995 IEEE International Conference on Robotics and Automation. He is a recipient of the 1991 NSF PYI Award.

Gregg Y. Takashige received the B.S. in Me- chanical Engineering from the University of Hawaii, Manoa, in 1990. In 1993, he received the M.S. in Mechanical Engineering with emphasis in Controls from the University of California at Berkeley. He worked at the UHM Autonomous Systems Laboratory from 1993-1994 and was involved with the ODIN

project. He is currently working for the Pacific Division, Naval Facilities Engineering Command at Pearl Harbor, Hawaii.

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