bottom-following for remotely operated vehicles

Control Engineering Practice 11 (2003) 461–470

Bottom-following for remotely operated vehicles

M. Caccia*, R. Bono, G. Bruzzone, G. Veruggio

Consiglio Nazionale delle Ricerche, Istituto Automazione Navale, Via De Marini 6, 16149 Genova, Italy

Received 15 June 2001; accepted 31 August 2001

Abstract

This paper addresses the problem of designing high-precision bottom-followers for remotely operated vehicles. In the framework

of hierarchical control architectures able to uncouple the robot’s kinematics (guidance) and dynamics (velocity control), the task of

bottom-following is accomplished by suitable guidance task functions, which enable the system to handle unmodeled, i.e. not

measured or estimated, kinematics interactions between the robot and the operating environment. In order to increase the bottom-

followers’ reliability, the paper discusses possible techniques for modeling and handling the environmental and measurement

uncertainty in the estimate of the local interactions between the vehicle and the operating environment, i.e. altitude and bottom

slope. In particular, according to at-sea operational experience, the problem of managing dropouts due to erroneous tracking of

multi-path echoes by the ROV altimeter(s) is addressed. Results of a large set of pool trials carried out with the Romeo ROV are

reported and discussed. r 2003 Elsevier Science Ltd. All rights reserved.

Keywords: Guidance systems; Underwater vehicles; Uncertainty; Control

1. Introduction

Typical scientific benthic surveys require unmannedunderwater vehicles to execute traverses at low speednear the sea bottom, i.e. surge up to 0.5m/s and altitudelower than 1.5m, in order to collect samples, data andquantitative video sequences for sizing and counting ofbenthic communities (Smith, Terribile & Finotello,1998). These applications and, in particular, thequantitative video tasks, require the use of remotelyoperated vehicles (ROVs) characterized by high-preci-sion bottom-following capabilities. In the mid-1990s thetask of bottom-following, i.e. ‘‘maintaining a fixedaltitude above an arbitrary surface whose characteristicsmay or may not be known’’ (Bennett, Leonard &Bellingham, 1995), has been extensively treated in theliterature in the case of autonomous underwater vehicles(AUVs) dynamically controlled via fins and elevator. Inparticular, Bennett et al. addressed the problem ofbottom-following for survey-class AUVs, discussing the

performances of the so-called simplest bottom-followerand of a more advanced one, incorporating noisefiltering, outlier rejection, and slope estimate in thehypothesis of smooth seabed profile. The proposedbottom-follower interfaces the vehicle’s dynamic con-troller generating the desired path of the AUV as a high-level control module. On the other hand, sensor-basedcontrol laws generating the desired pitch rate, by LQand Lyapunov-oriented control laws, for AUVs con-trolled through their angular velocity are discussed in(Santos, Simon, & Rigaud, 1995). A bottom-followingtechnique based on the construction of a one-dimen-sional map and a synthetic non-linear elliptical force-field has been applied on the autonomous benthicexplorer (Yoerger, Bradley, Walden, Singh, & Bach-mayer, 1998). For what concerns ROVs, the perfor-mances of the simplest bottom-follower, in executingoperational near bottom and ice-canopy traverses andintegrated with a conventional ROV auto-pilot (Fossen,1994) performing auto-depth, auto-heading and auto-surge, have been discussed in Caccia, Bono, Bruzzone, &Veruggio (1999). The susceptibility to dropouts andnoise of the simplest bottom-follower suggested thedevelopment of multi-sensor systems and/or advancedmethodologies to handle the uncertainty in the acousticsensing of the underwater environment, while a strong

*Corresponding author. Tel.: +39-010-6475-612; fax: +39-010-

6475-600.

E-mail addresses: [email protected] (M. Caccia), [email protected]

(R. Bono), [email protected] (G. Bruzzone), [email protected]

(G. Veruggio).

0967-0661/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved.

PII: S 0 9 6 7 - 0 6 6 1 ( 0 1 ) 0 0 1 4 2 - 3

improvement in auto-depth, and thus in bottom-following performances has been allowed by thedevelopment of a methodology for modeling andidentifying the vehicle’s heave dynamics. On the otherhand, the performances of a Lyapunov-oriented bot-tom-follower generating the reference vehicle’s heave,integrated with an active sonar-based bottom estimatorand a conventional ROV auto-pilot, have been tested inpreliminary pool trials (Caccia, Bruzzone, & Veruggio,1999a). Experiments demonstrated that the interactionsbetween the noise in the depth estimate and thetransformation of the desired heave in the referencedepth could strongly reduce the system’s performances.Recent results in modeling and identification of open-frame ROVs (Caccia, Indiveri & Veruggio, 2000a)enabled the implementation of model-based non-linearheave estimators and the design and development ofhierarchical guidance and control architectures, whichuncouple the vehicle’s kinematics, i.e. absolute orenvironment-based position control, and dynamics, i.e.velocity control (Caccia & Veruggio, 2000).In this framework, Lyapunov-oriented bottom-fol-lowers do not require any artificial transformation ofthe generated desired heave and can be naturallyintegrated in the control architecture as guidance taskfunctions.

In particular, the research presented in the followingassumes that a heave estimation and control system,consisting of an extended Kalman filter model-basedestimator of the vehicle vertical motion and a PI gainscheduling heave controller, has been designed andimplemented as discussed in Caccia and Veruggio(2000).

A set of bottom-followers, which compute the desiredvehicle’s heave enabling the robot to handle unmodeledkinematics interactions between the robot and the

operating environment, e.g. seabed slope and currents,are presented in Section 2. At this stage, the maintechnical issue is to improve the system’s reliability inorder to guarantee that the bottom-follower works evenin uncertain conditions typical of at-sea operations.Thus, current research focuses on the development ofestimators of the local interactions, i.e. altitude andbottom slope, between the vehicle and the operatingenvironment able to cope with sudden variations in thebottom roughness and slope, and dropouts due toerroneous tracking of multi-path echoes by the ROValtimeter(s). A simple estimator of the vehicle’s altitudeand bottom slope based on the measurements of acouple of echo-sounders is presented in Section 3.Section 4 discusses the problem of modeling andmanaging the environmental and measurement uncer-tainty, focusing on the topic of multi-path handling,according to at-sea operational experience. Anextended session of pool trials has been carried out inpool with Romeo, the last ROV prototype developedby CNR-IAN. Results are reported and discussedin Section 5. Section 6 concerns some concludingremarks.

2. Bottom-following task functions

According to the approach discussed in Caccia andVeruggio (2000), the synthesis of a guidance controller isbased on the definition of a positive definite function V

of a task function%e; V ð

%eÞ ¼ 1

2%eT

%e: If the reference

velocities are chosen so that ’e ¼ �l%e; then V is a

negative definite function and V is a Lyapunov functionwith a stable equilibrium point

%e ¼

%0: According to the

nomenclature defined in Fig. 1, the local interactionsbetween the vehicle and the environment are described

Fig. 1. Bottom-following nomenclature.

M. Caccia et al. / Control Engineering Practice 11 (2003) 461–470462

by the kinematic relation:

’h ¼ �w � u tga; ð1Þ

where h; w and u are the vehicle’s altitude, heave andsurge, respectively, and a is the bottom slope in the surgedirection.

In the case where the vehicle surge and bottomslope are neglected, i.e. not measured, Eq. (1) can besimplified as

’hD� w: ð2Þ

It is worth noting that Eq. (2) completely models thevehicle’s altitude during hovering operations. In thefollowing, the term auto-altitude will refer to thisoperating condition or model adoption.

The task of bottom-following can be described by thetask function of P-type

e ¼ h � hn ð3Þ

or by the task function of PI-type

e ¼ h � hn þ mZ

h � hn� �

dt ð4Þ

in case unmodeled kinematics has to be compensated,where the asterisk indicates the reference value of thegiven quantity.

According to the available estimates of the robot–environment interactions and the desired compensationof unmodeled kinematics, a set of bottom-followingguidance laws can be defined as it follows:

P-auto-altitude: from (2) and (3):

wn ¼ kP h � hn� �

: ð5Þ

PI-auto-altitude: from (2) and (4):


þ kI

Zh � hn� �

dt: ð6Þ

P-bottom-following: from (1) and (3):


� u tga: ð7Þ

PI-bottom-following: from (1) and (4):


þ kI

Zh � hn� �

dt� u tga: ð8Þ

3. Bottom estimation

The robot-environment interactions can be locallymodeled by the vehicle’s altitude h and the bottom slopea; aAð�p=2;p=2Þ in the surge direction (see Fig. 1).Defining the model state as

%x ¼ h tga½ �T ð9Þ

in the hypothesis the vehicle executes a traverseat constant heading, the following state equation

holds:

’x ¼0 � #u

0 0

" #%x �

#w

0

" #; ð10Þ

where #u and #w are the estimated surge and heave,respectively.

Assuming that a couple of altimeter echo-sounder ismounted on the vehicle’s bow and stern, the measure-ment equations are, respectively:

rB ¼ h �L

2tga ¼ x1 �

L

2x2 ð11Þ

and

rS ¼ h þL

2tga ¼ x1 þ

L

2x2: ð12Þ

A linear Kalman filter with time-varying state andmeasurement equations can be used to estimate thesystem state, which is initialized according to equations

x1 ¼ h ¼rS þ rB

2ð13Þ

and

x2 ¼ tga ¼rS � rB

L: ð14Þ

4. Modeling and handling of uncertainty

In executing the task of bottom-following, uncer-tainty mainly concerns the characteristics of the seabed(roughness and changes in slope) and the behavior ofacoustic range sensors (noise and outliers).

Usually, the bottom profile is locally modeled as asmooth curve, while the high-spatial frequency compo-nents can be taken into account by increasing thecovariance of the range measurements. An example isgiven in (Caccia et al., 1999b), where different values forthe echo-sounder covariance have been adopted depend-ing on smooth ice-canopy or rough seabed tracking.Smooth changes in the seabed slope can be embedded inthe system noise, while sudden variations in the bottomprofile can be handled by multi-hypothesis approachesas discussed in Caccia et al. (1999a).

Examples of filtering of the altimeter noise are givenin Bennett et al. (1995) and Caccia et al. (1999b), where‘‘moving window’’ and Kalman filter schemes areproposed. Outliers are usually detected through thres-hold-based mechanisms, by re-initializing the altitudeestimator when a long sequence of similar rangemeasurements does not satisfy a suitable innovationtest. In any case, conventional outlier rejection techni-ques proved to be unreliable in the case of a UUVequipped with echo-sounders provided with only onetarget range measurement and operating in shallowwater, i.e. in a water column shorter than the sonarmaximum operating range.

M. Caccia et al. / Control Engineering Practice 11 (2003) 461–470 463

As shown in Fig. 2, where data collected by Romeoduring an under-ice mapping mission near the Antarticcoast are presented (Caccia et al., 1999b), the echo-sounders can track virtual targets given by multiplerebounds of the acoustic wave between the sea bottomand the top. Multi-path effects are clearly visible arounda time equal to 450 s when the vehicle operated in awater column of about 10m.

Off-line post-processing of the recorded data showedthat sonar behavior could be simply modeled as follows(Caccia, 1999):

r ¼ h þ nH ¼ h þ n h þ dCTD þ z � cð Þ; ð15Þ

where r is the measured range, z is the vehicle depth,dCTD is the depth-meter off-set with respect to thevehicle’s bottom, c is the thickness of the ice-canopy, ifpresent, H is the height of the water column, and n is thenumber of acoustic wave rebounds between the bottomand the top (see Fig. 3). It is worth noting that the sameeffect has been experienced during very shallow waterpool trials with multiple reflections of the acoustic wavesbetween the bottom and the surface. In this case, c ¼ 0;and

h ¼r� n dCTD þ zð Þ

n þ 1: ð16Þ

In order to evaluate the number of paths and altitude attime t; a bank of residual generators

en tð Þ ¼ h r tð Þ; n tð Þð Þ � #h t=t � Dt� �

ð17Þ

can be implemented, where h r tð Þ; n tð Þð Þ is computedaccording to Eq. (16). A minimum range decision maker

#h tð Þ ¼ h #n tð Þð Þ : #n tð Þ ¼ arg minn

enðtÞj j ð18Þ

is adopted, with the constraints hðtÞ > 0 and hðtÞ þdCTDp #H ¼ #h t=Dt

� �þ dCTD þ #zðtÞ; i.e.

nðtÞorðtÞ

dCTD þ #zðtÞand nðtÞX

rðtÞ þ dCTD � #HðtÞ#HðtÞ

:

Fig. 2. From top to bottom: vehicle heading, depth, surge speed and altimeter and ice-canopy detector range measurements.

Fig. 3. Echo-sounder multi-path nomenclature.


Since sudden changes in the vehicle’s altitude are notphysically admissible, the expected range #h t=t � Dt

� �is

assumed equal to #h t � Dtð Þ:

5. Experimental results

Experiments have been carried out in a swimmingpool using the Romeo ROV (Caccia et al., 2000b)equipped with a couple of echo-sounders: one TritechPA500-6 (500KHz, operating range between 0.2 and50m, beamwidth 61 conical) and one Tritech PA200-20(200KHz, operating range between 0.74 and 100m,beamwidth 201 conical) mounted on the vehicle’s bowand stern, respectively. As shown in Fig. 4, the poolprofile presents a maximum slope of about 221.

At first, the performances of the bottom-followingguidance laws defined in Section 2 have been evaluated,without adopting any technique to handle sonar

dropouts and multi-paths. The vehicle’s altitude hasbeen referred to the bow echo-sounder in the case ofauto-altitude and computed as shown in Section 3 whenboth the echo-sounders have been used to estimate thebottom slope. The vehicle’s surge with respect to thewater has been estimated in open loop on the basis ofthe identified Romeo’s dynamic model (Caccia et al.,2000a).

Results for experiments in which Romeo executedtraverses at constant heading and surge of 0.2m/s arereported in the following.

The use of an integrator in the guidance law tocompensate the unmodeled kinematics of Eq. (2) inexecuting the auto-altitude task is shown in Figs. 5 and6, where the vehicle’s altitude, heave and depth are,respectively, plotted from top to bottom. The dottedand bold lines refer to the reference and estimatedvalues, respectively. In case no integral actionis executed a steady-state tracking error de ¼ d ’h=kP;

Fig. 4. Pool profile.

Fig. 5. P-auto-altitude: Romeo’s altitude, heave and depth (u ¼ 0:2m=s; aMAX ¼ 221).


d ’h ¼ �u tan a; of about 0.16m for kP ¼ 0:5; occurswhen the vehicle flies over the sloping bottom profile(see Fig. 5, time between 105 and 135 s). The unmodeledkinematics is compensated by the integral guidance lawin the experiment reported in Fig. 6 (see the time intervalbetween 85 and 110 s). It is worth noting that after about111 s, the vehicle moves closer to the bottom as aconsequence of the guidance module’s reaction to echo-sounder dropouts. These trials are the actual realizationof the simulative tests described in Caccia and Veruggio(2000), which produced quite similar results.

Improvements of performance obtained by feed-forwarding the altitude variations induced by theinteractions between the vehicle’s surge and bottomslope are shown in Figs. 7 and 8 for the P- and PI-typeguidance law, respectively. In these experiments, thevehicle moved upward the pool slope at constantheading with a surge of 0.2m/s and a desired altitudeof 1.2m.

The top plots show the range measurements obtainedby the bow and stern echo-sounders and the estimatedaltitude as discussed in Section 3. In these conditions, asimple threshold-based mechanism has been sufficient tohandle isolated sonar dropouts.

Since currents are negligible in pool, the estimate ofthe feed-forward action is quite good, and the action ofthe guidance integrator is very small. In any case,persistent dropouts in sonar measurements due to multi-path effects could occur in very shallow water andwould require to be detected and compensated in order

to avoid catastrophic crashes against the bottom. In theexperiment described in Figs. 9–11, Romeo executedthree traverses moving alternatively downwards andupwards the pool slope at a constant estimated surge of0.34m/s and maintaining a constant altitude of 1m witha PI-bottom-following algorithm (see Fig. 9).

Multi-paths have been managed by combining thealtitude and slope estimator described in Section 3 withthe multi-path model and management techniquesdiscussed in Section 4. Raw sonar range data have beenpre-processed in order to detect and compensate multi-paths before estimating the vehicle’s altitude and bottomslope.

Fig. 10 shows the raw and pre-processed bow andstern range data, represented by the dotted and boldlines in the two upper plots; the pre-processed bow andstern ranges and the estimated altitude represented bythe dotted, solid and bold lines, respectively, in the thirdplot; and the estimated bottom slope (lower plot). Multi-path effects are clearly visible in the first phase of theexperiment.

As shown in the magnified view of the recorded datareported in Fig. 11, the bow sonar tracked a multi-pathtarget in the time interval from 75 to about 80.5 s and insome shorter intervals that followed. After about 82 s,Romeo started to move downwards the pool slope, andthe bow sonar reduced the number of multi-path tracksin different local working conditions. It is worth notingthat the stern sonar worked to the extent of its minimumoperating range of 74 cm: thus, this sonar provided out-

Fig. 6. PI-auto-altitude: Romeo’s altitude, heave and depth (u ¼ 0:2m=s; aMAX ¼ 221).


of-range measurements when the Romeo’s stern was tooclose to the bottom (see, for instance, the time intervaljust before 90 s when Romeo was moving downwards

the slope). In these conditions, the altitude and bottomestimator worked using only the bow sonar range andthe estimated surge and heave data.

Fig. 7. P-bottom-following: Romeo’s altitude, heave and depth (h ¼ 1:2 m; u ¼ 0:2m=s; aMAX ¼ 221).

Fig. 8. PI-bottom-following: Romeo’s altitude, heave and depth (h ¼ 1:2 m; u ¼ 0:2m=s; aMAX ¼ 221).


Fig. 9. PI-bottom-following with multi-path handling: Romeo’s estimated altitude, slope, surge, heave and depth (h ¼ 1 m; u ¼ 0:34 m=s;aMAX ¼ 221).

Fig. 10. Multi-path handling: pre-processing and estimation data.


6. Conclusions

This paper shows how the development of hierarch-ical control architectures, able to uncouple the robot’skinematics (guidance) and dynamics (velocity control),allows the easy design and integration of a class ofLyapunov-oriented guidance laws for executing the taskof bottom-following with and without surge and slopecompensation. At present, the main technical issue is toincrease bottom-followers’ reliability in order to copewith environmental and sensor uncertainty. In particu-lar, the research presented in this paper focused on thedesign of altitude and bottom profile estimatorsintegrated with multi-path detection and recoveryalgorithms handling the uncertainty on the origin ofthe measurements in conventional, commercial echo-sounders, which provide only the nearest/strongesttarget-echo. Alternatively, this problem can be tackledthrough the development of acoustic altimetersto be easily integrated on-board unmanned underwatervehicles, and which could provide the ranges ofall the detected echoes. The automatic selectionof the suitable guidance law according to theperformances of the altitude estimators would lead tosafe and reliable autonomous short-range bottom-following.

Acknowledgements

This work has been partially supported by PNRA(Programma Nazionale di Ricerche in Antartide), Task4a-Robotica e Telescienza in Ambiente Estremo. Theauthors wish to thank Giorgio Bruzzone and EdoardoSpirandelli for their support given in Romeo’s hardwaredevelopment and during at-sea and pool trials.

References

Bennett, A. A., Leonard, J. J., Bellingham, J. G. (1995). Bottom

following for survey-class autonomous underwater vehicles.

Proceedings of the ninth international Symposium on unmanned

untethered submersible technology, Durham, USA (pp. 327–336).

Caccia, M. (1999). Handling underwater environment uncertainty

through acoustic devices’ redundancy and multi-path modeling.

CNR-IAN Technical Report, No. 345/99.

Caccia, M., Bruzzone, G., & Veruggio, G. (1999a). Active sonar-based

bottom following for unmanned underwater vehicles. IFAC

Control Engineering Practice, 7(4), 459–468.

Caccia, M., Bono, R., Bruzzone, G., & Veruggio, G. (1999b). Variable

configuration UUVs for marine science applications. IEEE

Robotics and Automation Magazine, 6(2), 22–32.

Caccia, M., & Veruggio, G. (2000). Guidance and control of a

reconfigurable unmanned underwater vehicle. IFAC Control

Engineering Practice, 8(1), 21–37.

Fig. 11. Multi-path handling: pre-processing and estimation data (particular).


Caccia, M., Indiveri, G., & Veruggio, G. (2000a). Modelling and

identification of open-frame variable configuration unmanned under-

water vehicles. IEEE Journal of Oceanic Engineering, 25(2), 227–240.

Caccia, M., Bono, R., Bruzzone, G., & Veruggio, G. (2000b).

Unmanned underwater vehicles for scientific applications and

robotics research: The Romeo project. Marine Technology Society

Journal, 24(2), 3–17.

Fossen, T. I. (1994). Guidance and control of ocean vehicles. England:

Wiley.

Santos, A. S., Simon, D., Rigaud, V. (1995). Sensor-based control of a

class of underactuated autonomous underwater vehicles. Proceed-

ings of the third IFAC workshop on control applications in marine

systems, Trondheim, Norway (pp. 107–114).

Smith, C., Terribile, A., & Finotello, R. (1998). ARAMISFscientific

user requirements document. Report SPE-T1-001, EU Project

MAS3-CT97-0083, Aramis.

Yoerger, D. R., Bradley, A. M., Walden, B. B., Singh, H., &

Bachmayer, R. (1998). Surveying a subsea lava flow using the

autonomous benthic explorer (ABE). International Journal of

Systems Science, 29(10), 1031–1044.


bottom-following for remotely operated vehicles

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