Coordinating UAVs and AUVs for Oceanographic Field Experiments:Challenges and Lessons Learned

Margarida Faria1, Jose Pinto1, Frederic Py2, Joao Fortuna1, Hugo Dias1, Ricardo Martins1

Frederik Leira3, Tor Arne Johansen3, Joao Sousa1 and Kanna Rajan2

Abstract— Obtaining synoptic observations of dynamicocean phenomena such as fronts, eddies, oxygen minimumzones and blooms has been challenging primarily due tothe large spatial scales involved. Traditional methods of ob-servation with manned ships are expensive and, unless thevessel can survey at high-speed, unrealistic. Autonomousunderwater vehicles (AUVs) are robotic platforms thathave been making steady gains in sampling capabilities andimpacting oceanographic observations especially in coastalareas. However, their reach is still limited by operatingconstraints related to their energy sources. Unmannedaerial vehicles (UAVs) recently introduced in coastal andpolar oceanographic experiments have added to the mix inobservation strategy and methods. They offer a tantalizingopportunity to bridge such scales in operational oceanog-raphy by coordinating with AUVs in the water-columnto get in-situ measurements. In this paper, we articulatethe principal challenges in operating UAVs with AUVsmaking synoptic observations for such targeted water-column sampling. We do so in the context of autonomouscontrol and operation for networked robotics and describenovel experiments while articulating the key challenges andlessons learned.

I. INTRODUCTION

Sampling of the coastal ocean is substantially chal-lenging, even at small observation scales. The state ofthe art entails deploying sensors distributed over buoys,unmanned marine vehicles, manned vehicles and adapt-ing the sampling strategy to the observations. Adaptationis targeted at adjusting the spatial and temporal reso-lutions to the properties of the sampled field. This isnot a trivial task. First, observations need to be commu-nicated over communication challenged environments.Second, data once obtained requires assimilation that iscomputationally challenging as even prediction modelsdo not fully capture domain knowledge from an expert.Third different sensors and vehicles may have differentcommand and data interfaces making interoperabilityquite difficult. And finally, state of the art unmannedvehicles while automated, lack inferential reasoningcapabilities, thus requiring human guidance for effectiveadaptation. These are some of the reasons why there is

1M. Faria, J. Pinto, J. Fortuna, H. Dias, R. Martins and J.Sousa are with the Faculty of Engineering, Porto University, Portugal{margaridacf,zepinto,jtasso}@fe.up.pt

2F. Py and K. Rajan are with the Mon-terey Bay Aquarium Research Institute, California{fpy,kanna.rajan}@mbari.org

3F. Leira and T. Johansen are with the Center for Au-tonomous Marine Operations and Systems (AMOS), Depart-ment of Engineering Cybernetics, Norwegian University of Sci-ence and Technology, Trondheim {frederik.s.leira,tor.arne.johansen}@itk.ntnu.no

Fig. 1. Conducting UAV and AUV operations from the PortugueseNavy support vessel NRP Bacamarte, Sesimbra, Portugal, July 2013.

a significant gap between operational deployments andsimulation studies.

Autonomous platforms such as powered autonomousunderwater vehicles (AUVs) or slower moving glid-ers have extended the reach of traditional ship-boardmethods for obtaining oceanographic measurements. Asa consequence, scientists are able to characterize awider swath of a survey area in less time. Yet, suchmeasurements are not synoptic since they do not matchthe mesoscale (> 50km2) observation capability nec-essary to understand the bio-geochemistry and organ-ism transport important for science, one example arecoastal ecological studies. Coupled with limited oceanmodel quality, the capability to understand our changingocean is called into question. With recent advancesin unmanned aerial vehicles (UAVs), payload sensors,and their cost-effectiveness in field operations, theseplatforms offer a tantalizing hope of further extendingthe reach of oceanographers.

The paper describes the necessary infrastructure andsoftware architecture to operate networked vehicles atsea. Operations are supported by the use of mixed-initiative control, a novelty in our field. As a first,we detail the use of Artificial Intelligence (AI) basedcontrol to command both a UAV from the ground andan AUV onboard. The paper describes the results of afield experiment which used the architecture at sea froma support vessel. We conclude with lessons learned and

future work.

II. RELATED WORK

Coordinating AUVs and UAVs vehicles in oceanicconditions has proved to be challenging. While vehicle-specific commercial solutions with proven field capabili-ties are in routine use especially within the military, theyrely on mature waypoint-based command and controltechniques.

Demonstrated applications with autonomous vehiclesin realms spanning from civil engineering [1], agri-culture [2], oceanography [3] and others [4] exist. Acontrol law for cooperation between UAVs and terrestrialrobots for large area coverage is described in [5]. Theseapproaches use limited forms of autonomy which isprone to human errors since missions are defined in low-level behavior patterns.

The use of deliberative AI methods is less common,however. A number of different approaches for multi-vehicle task assignment in field operations have beenattempted. Extensive work has been done by [6] devel-oping a human readable Mission Specification Languagebased on Temporal Action Logic. In his delegation-based framework, an operator can specify behaviorthrough high level goals that get distributed amongagents. A partial order planner is then used to searchfor task assignment among UAVs, while an onboardforward-chaining planner generates actions for the agentin temporal order. A hierarchical 3-tier architecture hasbeen developed and tested by [7]. It uses a collisionavoidance layer at lower abstraction levels, a middlelayer for path planning and an abstract layer for taskassignment. This is where the optimal task schedulingis determined and allocated, it relies on a negotiationbased algorithm for task distribution. The architecturedeveloped by [8] has an on-board deliberative layer fordistributed decision-making and an executive layer fortask execution. Task planning can be done online oroffline using a temporal planner that solves instancesof the Travelling Salesman Problem using a brute-forcemethod. The distribution of tasks among UAVs is thendone using a market-based approach that does load-balancing across UAVs.

The approach described here builds on the work by[9]. These authors use an onboard planer to generateand execute Lagrangian survey plans around a movingdrifter with an AUV.

In most of these instances, the system was designedand tested for a single vehicle type. Network and controlof a heterogeneous group of vehicles has proven to bedifficult especially in the field and we believe we areamong the first to address it here.

III. ARCHITECTURAL APPROACH

To overcome the challenge of adjusting to both thefaster, short lasting UAV and the slower, longer lastingAUV we used the flexible network setup shown in Fig.2. Operators are onboard a support vessel, interactingwith the overall system via NEPTUS consoles. Theseare connected to a Manta communications gateway

which provides long-range wifi, underwater (acoustic),satellite (Iridium) communications and internet accessvia 3G, when available. The consoles receive the stateof all systems in the network, those connected directlyto nearby Manta gateway as well as systems in remotelocations. When removed from a gateway, the systemsends its position to a central service through 3G/GSMor satellite. Execution of a plan is carried out by DUNE,deliberation of the plan is T-REX’s responsibility, whileplan selection is in the hands of the human controlleroperating NEPTUS.

a) Communications: To cope with hardware het-erogeneity we use Inter-Module Communications1

(IMC) [10], a real-time message-based protocol that isused across all systems including unmanned vehicles,operator consoles, Manta and back-end servers. Typi-cally, all IMC-compatible nodes use a discovery mech-anism to disseminate available services to the networkand, after discovery, communicate peer-to-peer.

During execution, all hardware nodes store incomingand outgoing messages by serializing them into a logfile, for posterior mission analysis. The common formatallows merging of different missions for a more inte-grated view of the data.

b) Web services: A centralization point, the HUBweb service receives information from any source with aweb, satellite or GSM connection. Data such as oceano-graphic conditions, vehicle or ship locations need to beparsed, integrated and made available through an APIto NEPTUS and other applications. The HUB can in turnsend data out through either satellite links or the web.Consequently, teams at remote locations and without di-rect wifi connection can coordinate positions of differentvehicles using the HUB as a relay while exchanging“virtual positions” (a desired position is requested byone team and updated by another). Not only can allteams see requests and updates, but they can use multiplepathways to receive the same information, leading to arobust and reliable communication mechanism.

c) DUNE: The Uniform Navigation Environment(DUNE)2 [11] is the embedded software used onboard

Spot Iridium

Hub

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WavyDrifter

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Accustic

IMCIMC

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Fig. 2. Network connectivity across platforms.

1https://github.com/LSTS/imc2https://github.com/LSTS/dune

our unmanned vehicles, data loggers and MANTA gate-ways. It is composed of independent tasks that com-municate by broadcasting and subscribing to IMC mes-sages. DUNE tasks can be diverse; some are responsiblefor interfacing hardware with IMC (either sensors oractuators), others are part of the chain of command eithertranslating higher-level goals into low-level actuationcommands or interpreting low-level sensor readings intostate estimates. To integrate any sensor or actuatorhardware, only the tasks dedicated to its managementneed to know the specific details. Each system usesa task configuration adjusted to its purpose, availablesensors, actuators and communications.

The DUNE control architecture requires that a singlemaneuver controller is driving the vehicle at any giveninstant. To achieve complex behavior, a plan supervisoris capable of parsing a (scripted) plan and instantiatingmaneuver controllers according to that specification. Al-ternatively, external controllers like T-REX (describedbelow) guide the vehicle by instantiating the FollowRef-erence maneuver controller. When this controller isactive, it accepts references with the desired state fromauthorized sources and transforms them into vehicleactions. The desired state has elements like coordinates,altitude/depth or speed.

d) NEPTUS: is an open-source software infrastruc-ture3 used to build graphical interfaces for networkedvehicle systems. The main user interfaces are the Oper-ator Console and the Mission Revision and Analysis tool[11]. Fig. 3 is an example of an AUV operator console,consisting of a set of plug-ins such as visual widgets (amap, a mission tree, a vehicle state display), map layers(cartography, live ship positions), daemons (operationallimits monitor), popups (multi-system listing) and mapinteractions (planning, map editing, measuring, real-time strategy etc. ). Vehicles can be controlled using fourinteraction styles: point-and-click to manually executea maneuver, scripted plans for subsequent execution,real-time planning where the user designs a plan localto NEPTUS that can be edited on the fly and finallydeliberative planning, an advanced AI-based capability

Fig. 3. Neptus console interface. ’A’ shows GPS trackers, ’B’ thepath of the UAV, ’C’ icons for an UAV in green and an AUV in white,’D’ for two MANTAs and an AIS marker for the Bacamarte

3https://github.com/LSTS/neptus

to synthesize, execute and re-plan on the fly.To improve operator responsiveness and situational

awareness, NEPTUS allows each console to be adaptedto a particular mission, vehicle or operator function. Amission file is a configuration that stores all the missionelements: map features, vehicle configurations, plans andchecklists. All elements can be edited and shared as IMCmessages.

e) T-REX: is an open source on-board adaptivecontrol system4 that integrates AI-based planning andstate estimation in a hybrid executive. The overall sys-tem is a composition of application specific functionalcomponents called reactors, interacting with each otherthrough timelines or state variables. Goals are dispatchedto other reactors down the hierarchy while observationsrecording the state of the world are posted up thishierarchy. Timelines are populated with observations(temporal predicates) posted by their owner reactors. Inthis system, a goal is a projected future observation.When a reactor receives a goal in a timeline, it willattempt to decompose it by potentially deliberating overa series of other (sub)goals and eventually report it asan observation, in nominal execution. The role of theT-REX agent then, is to ensure that all the reactors willbe able to interact concurrently so that they are informedof state evolution that may impact them, have a sufficientamount of time to synthesize plans and coordinate plandispatch across reactor boundaries. Details of T-REXare beyond the scope of this paper and can be found in[12], [13], [14].

f) UAVs: Three identical low-cost UAV platforms(Fig. 1) were deployed and recovered from our supportvessel. Fig. 4 shows the main components of this UAVplatform. The X8 flying wing is an electric vehiclebased on a RC model which holds an ISEE IGEPv2single board computer running DUNE. It has a wingspanof 212 cm, a take-off weight of around 3.5 Kg andis able to fly up to one hour continuously. It usesthe ArduPilot, an open-source low-cost autopilot. Tointegrate it with DUNE a specialized driver task wascreated which translates telemetry and guidance com-mands. The main communication channel is a 5 GHzwifi with line-of-sight ranges of up to 10 Km. Different

ArduPilot

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Fig. 4. The UAV toolchain used in the experiments.

4https://code.google.com/p/trex2-agent/

camera configurations were tested including an IP, adigital compact camera and an analog Infra-Red camera(Fig. 5(a)). The Infra-red camera used was a FLIR Tau2336, connected to an analog sending device working at1.3 GHz. An analog receiver device was located on theground, where the video feed was captured and stored.

The UAV was deployed using a crossbow/catapultmechanism and can land either on a grass/dirt/asphaltstrip or on a capture net used in confined spaces like shipdecks such as seen in Fig. 1. Regular operations requiredone operator who uses NEPTUS and T-REX to controlthe vehicle while in autonomous operations mode and asafety pilot for take-off, landing and emergency manualcontrol.

g) AUVs: Three AUVs designed at the Univ. ofPorto were deployed, with two simultaneously in thewater at most. These AUVs have ∼ 6 hours of oper-ational capacity when running underwater at 3 knots.They weigh 20 Kg, have a 15 cm diameter and lengthof 180 cm. Each vehicle runs DUNE in its AMD GeodeLX 800 processor with 1024 MB of RAM memory.It can communicate using Iridium, 802.11 wifi, GSMand acoustic modems. For navigation it relies on aMicrostrain 3DM-GX3 AHRS and a NavQuest 600MicroDVL (Doppler Velocity Log). For water columnmeasurements it uses an RBR XR620 CTD. The vehicleis deployed and recovered manually, either from the sideof a ship (Fig. 1) or from a RHIB.

IV. EXPERIMENTAL RESULTS

Our field experiments were conducted off Sesim-bra, Portugal from July 8th to July 17th onboard thePortuguese Navy vessel NRP Bacamarte. During thisexperiment, cameras onboard UAVs were used to detectfeatures of interest and AUVs were used to providecontextual information around drifters entrained in thesefeatures. One AUV would sample around a drifter’sLagrangian frame of reference [15].

A. UAV flight operationFor the UAVs to visually confirm a target, like a drifter

in the water, we used a spiral pattern encoded as a num-ber of increasingly larger circles. Doing so guaranteesthat the camera on the UAV will capture footage in anangle free of the Sun’s specular reflection. It covers thearea where the target might have drifted starting fromits last known position. Human operators can task theUAV to engage this behavior by sending a point on theconsole’s map as a target. Since the updated positionsof all the systems are shown on the map, it is simpleto send the desired position to the UAV. As a resultof this interaction, NEPTUS creates a high-level goalencoded as an IMC message with coordinates, desiredspeed and altitude. This message is sent to a shore-side instance of T-REX that integrates the objective intoits current sequence of planned UAV manoeuvres usingthree reactors: Platform, Navigator and Spotter. ThePlatform reactor translates IMC messages into T-REXobservations and vice-versa. This reactor is also respon-sible for commanding DUNE using guidance messages

sent to the FollowReference maneuver controller. TheNavigator reactor is responsible for enabling the vehicleto travel from one waypoint to the next adapting theplan to hardware failures or navigation requirements.The Spotter reactor (specific to UAVs) is responsible forgenerating a sequence of circular motions that achievethe desired search pattern. T-REX reactors are keptupdated with IMC messages about the position and speedof the vehicle, state of plan execution, operational limitsand connectivity. Only the Spotter reactor knows thecomplete plan and issues commands to the Platformreactor based on the progress of the actions allowingplan adjustment and to queue of waypoints.

B. AUV under task deliberationAUV control, deliberation, execution and adaptation

of plans is done onboard using T-REX with a numberof reactors. The Navigator and Platform reactors areidentical to those used in the UAV. The Yoyo reac-tor generates a set of waypoint references in betweentwo locations. These references command the vehicleto change its depth while moving in the directionof the destination waypoint in a see-saw pattern formaking high-density upper water-column observations.The Drifter reactor, also specific to AUV control, isresponsible for generating a survey pattern that makesthe vehicle encircle a drifter in its Lagrangian frameof reference [15]. The Proxy reactor allows a model tobe distributed over multiple T-REX instances, allowinga timeline to be synchronized across the network (andthrough Iridium) – such an approach allowed us to main-tain the human operator in control by having her/himselecting the drifter to be followed.

The generation of the survey pattern is shared betweentwo reactors. The Drifter generates the horizontal pathsolely based on the last known drifter position andvelocity (speed and heading). The generated path isusually a distorted box centered on the drifter position.The distortion applied to this box results from applyingthe velocity of the drifter to them, thus making the AUVkeep up with expected movement of the drifter. The Yoyoreactor, generates the vertical references by followinga state machine. Its latency of execution allows it tofunction in shallow areas with unknown bathymetrysince it will stop descending as soon as the bottom isnear the vehicle which is detected using Dopple VelocityLog measurements. The approach described here buildson the work by [9].

C. Other Operational resultsThe toolchain consisting of DUNE, NEPTUS, HUB,

T-REX and IMC made it possible to monitor andcontrol several vehicles, often simultaneously. Gate-ways, running DUNE were capable of bridging differentcommunication methods including underwater acoustics,wifi and the Web (through 3G/HDSPA). NEPTUS con-soles allowed multiple operators to follow the operationand control vehicles individually. Additional informationlike cartographic maps, real-time ship traffic and meteo-rological forecasts were overlaid in the consoles, allow-

ing operators and scientists to make accurate predictionsand informed decisions.

An unexpected outcome of the architecture, processand methods presented here, was the flexibility to dy-namically target our combined assets towards an op-portunistic science problem of tracking riverine fronts.Such fronts often have a steep thermal gradient whichcan be detected with an IR camera. Science input fromoceanographers present showed the existence of suchfronts, albeit weak in nature and close to shore. Weused the X8 with the IR camera to map an area in nearproximity to the support vessel and observed a thermalsignature on the video feed suggesting the existence ofsuch a gradient (Fig. 5(a)). A drifting buoy was thendeployed at this location as a marker for the movingfrontal mass.

A subsequent part of this experiment involved anAUV executing Lagrangian patterns around the driftingsensor, since the drifter was now entrained in the front.Using this method, it was possible to confirm the ex-istence of frontal zones by comparing the temperatureand salinity gradients of survey yoyo’s. The acquireddata strongly suggested the presence of different bodiesof water (see figure 5(b)).

Considering the widespread area of operation and theopportunistic nature of an experiment of this type, theHUB ’s web services were essential to aggregate infor-mation from disparate sources with all the associateddiverse input channels.

V. LESSONS LEARNED

We describe the successful deployment of multipleheterogeneous robotic platforms for coordinated obser-vation and tracking in the coastal ocean. The experi-ments were conducted from a support vessel under chal-lenging conditions, not without failures, including last-minute procedure changes. The challenges encounteredcould be labeled mostly as logistical or operational;however some key technical issues were also found.

The experiment was part of a larger scale deploymentencompassing tests and scenarios to be executed by thePortuguese Navy. This was both an advantage and achallenge; while it brought together people with differ-ing skills and experience to do opportunistic science, italso meant multiple objectives had to be satisfied with alimited number of shared robotic assets. One solutionto this problem was to have a tight schedule whereeach asset was used for a specific end; this howeverresulted in reduced flexibility for our engineering tests.Nature too played a part, especially since our UAVsrelied on stable weather conditions including some windlift to fly from the support vessel, impacting coordinatedaerial/sub-surface measurements.

The IMC protocol has been designed so that differentvehicles and consoles can exchange real-time data. How-ever, due to the simultaneous operations of a numberof vehicles and the available bandwidth, it becamenecessary to split the wireless network originating onthe support vessel into two different frequencies, oneoperating at 5.8 GHz for the UAV team and the other

(a)

(b)

Fig. 5. (a) Aerial infra-red imagery extracted from real-time videofeed showing a riverine front with two distinct bodies of waterindicated by the arrows. (b) Thermal gradient in the CTD data andthe distinct water mass (within the white box) of the AUV survey ofthe frontal zone. Black lines indicate the yoyo pattern executed by thevehicle in the water column.

at 2.4 GHz for the AUV. This isolation lead to loss ofsituational awareness as each team only had access totheir own vehicles. With the HUB the position of all thesystem became visible supporting team synchronization.Yet another communication related problem encounteredhad to do with the radio transmitter used with the IRcamera, which introduced static noise into the videofeed. A potential solution would be to store the analogvideo signal directly on the UAV using a frame grabber.

The large difference in operational speeds for UAVs,RHIBS and AUVs showed that it is difficult to re-task acollective of vehicles unless there is tight human oper-ator coordination. In future deployments which mightrequire co-located sampling in aerial and underwaterenvironments, we believe that using several spatiallydistributed AUVs will lead to faster observations tocompensate for their relative speed. However, this wouldrequire a careful balancing of assets’ operating area andmonitoring of resources such as onboard energy.

Deliberative planning onboard AUVs relieves the op-erator of validating any manually defined plans. How-ever, we found that having the AUV fully autonomousmakes it difficult for operators to follow the behavior ofan AUV in real-time. Limited observability led to lackof opacity of what the AUV is doing at any instance oftime. For this reason, we ensured that the operator was

an integral part of the deliberation process. For instance,in scenarios associated with contextual surveys arounda drifter, the operator console received and plotted alldrifter positions. The operator was therefore situationallyaware and made responsible for selecting which of themany drifters to target and which tasks and partial planswere to be sent to the AUV’s onboard planner.

Overall, tolerance to communication faults and redun-dancy of communication means proved key in this fieldexperiment. In particular, the UAV mitigation scenarioin the event of communication loss (in which the UAVloiters around a ’safe point’ and altitude) providedsecurity for bolder tests.

VI. CONCLUSIONS

In the context of this experiment, we successfullydemonstrated the integration of deliberative techniquesin T-REX with DUNE with the help of motion-controlcommand sets based on concepts such as trajectories,paths, and waypoints. We are now working on tighterintegration and underlying theory development relatedto guarantees for safe command execution to ensurethat the planner will not lead to unexpected/unsafebehaviors. Towards this end, we are investigating theuse of approximations of the vehicle’s reach sets whichare computed off-line [16] and of controllers based onthe interpolation of the grid representation of underlyingvalue functions [17] to address some of the questionsof integrating different methods within an appropriatecontrol and computation framework. Further, the integra-tion of a deliberative on-board planner with a low levelvehicle control system raises several questions at theintersection of AI and control systems; for instance whatare ’good’ abstractions of low-level controlled behaviorsfor planning purposes?

The complete toolchain was used for the very firsttime in the experiment. Logistical and operational chal-lenges related to such experimentation in the field willcontinue to be a significant part of such deployments.However, we have taken preliminary steps to seamlesslyinsert humans and fully autonomous systems to controlheterogeneous vehicles in real world environments. Andin this context, the flexibility given by the HUB to inte-grate information coming from diverse sources provedto be a valuable asset for situational awareness.

The experiment also supported the possibility formultiple T-REX instances controlled by a centralizeddeliberation engine, which we hope to investigate fur-ther using such quasi-centralized schemes to decomposehigh-level objectives and constraints.

ACKNOWLEDGEMENTS

This work was supported in part by the PortugueseNavy. We thank 1st Lt. Ruben Robalo Rodrigues and 1stLt. Cordeiro Cavaleiro. Porto University authors werefunded by ERDF - Transnational Programme - AtlanticArea (Project NETMAR), and EU FP7 ICT, (ProjectNoptilus). MBARI authors were funded by NSF grantNo. 1124975 and NOAA grant No. NA11NOS4780055as well as a block grant from the David and Lucile

Packard Foundation to MBARI. NTNU was funded bythe Research Council of Norway and Statoil through theCenter of Excellence on Autonomous Marine Operationsand Systems, grant 223254.

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