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DORIS - A MOBILE ROBOT FOR INSPECTION AND MONITORING OFOFFSHORE FACILITIES

Mauricio Galassi∗, Anders Røyrøy†, Guilherme P.S. de Carvalho‡, Gustavo M. Freitas‡,Pal J. From§, Ramon R. Costa‡, Fernando Lizarralde‡, Liu Hsu‡, Gustavo H.F. de

Carvalho‡, Jose F.L. de Oliveira‡, Amaro A. de Lima¶, Thiago de M. Prego¶, Sergio L.Netto‡, Eduardo A.B. da Silva‡

∗Petrobras/CENPES - Research and Development Center

†TPD RD New Development Solutions, Statoil ASA

‡Dept. of Electrical Eng. - COPPE/UFRJ

§Dept. of Mathematical Sciences and Technology - Norwegian University of Life Sciences

¶Dept. of Telecommunications - CEFET/RJ

Emails: [email protected], [email protected],

[email protected], [email protected], [email protected],



[email protected], [email protected], [email protected]

Abstract— DORIS is a research project which endeavors to design and implement a mobile robot for remotesupervision, diagnosis, and data acquisition on offshore facilities. The proposed system is composed of a rail-guided robot capable of carrying different sensors through the inspected area. This paper presents a generaloverview of the robot and a description of the developed mechanical designs and signal processing algorithms.Initial results validate the mechanical concepts considered so far and indicate that the signal processing algorithmsare capable of detecting, in real time, multiple foreign objects and audio anomalies from a standard scenario.

Keywords— Mobile robots; Field robotics; Security and safety of HMS.

Resumo— DORIS e um projeto de pesquisa que se empenha em implementar um robo movel para supervisaoremota, diagnostico, e aquisicao de dados em instalacoes offshore. O sistema proposto e composto de umrobo guiado por um trilho e capaz de levar diferentes sensores atraves do ambiente inspecionado. Esse artigoapresenta uma visao geral do robo e uma descricao do projeto mecanico e dos algoritmos de processamento desinais desenvolvidos. Resultados iniciais validam os conceitos mecanicos considerados ate entao e indicam que osalgoritmos de processamento de sinais sao capazes de detectar, em tempo real, multiplos objetos abandonados eanomalias de audio nos sinais adquiridos em um cenario padrao.

Palavras-chave— Robotica Movel; Robotica de Campo.

1 Introduction

Safety and efficient operation are imperative fac-tors to offshore production sites and a main con-cern to all Oil & Gas companies. A promising so-lution to improve both safety and efficiency is toincrease the level of automation on the platformsby introducing robotic systems.

During the last decade, several Oil & Gascompanies, research groups, and academic com-munities have shown an increasing interest in theuse of robots for operation on offshore facilities.

Recent studies project a substantial decreasein the level of human operation and an increase inautomation on future offshore oil fields (Skourupand Pretlove, 2009). The studies also point outthe potential increase in efficiency and productiv-ity with robot operators, besides the improvementof Health, Safety, and Environment (HSE) condi-tions, as robots can replace humans in tasks per-formed in unhealthy, hazardous, and confined ar-eas (From, 2010). In (Anisi et al., 2010), it is

considered the use of robots in Oil & Gas facili-ties in operations that require both high precisionand strength, regardless of weather conditions.

Among the research groups interested in off-shore robotics, Fraunhofer IPA is pioneer inproposing and demonstrating the applicability ofmobile robots for offshore inspection and mainte-nance tasks in loco (Bengel et al., 2009). One ex-ample is MIMROex (Bengel and Pfeiffer, 2007),capable of navigating safely, building maps, andexecuting inspection tasks autonomously through-out the topside of platforms.

Another robotic device applied in offshore en-vironments is Sensabot (NREC/CMU, 2012), ca-pable of safely inspect and monitor hazardous andremote production facilities. The robot can sus-tain high temperatures, is able to reach areas withdifficult access, and is certified to operate in ex-plosive and toxic environments.

SINTEF-ICT is another group interested inmanipulators applied to the oil and gas industry.Inspection and maintenance operations in a simu-

Anais do XX Congresso Brasileiro de Automática Belo Horizonte, MG, 20 a 24 de Setembro de 2014

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lated production process are performed by the co-operation of a gantry-mounted manipulator and afloor-mounted robot (Kyrkjebø et al., 2009).

In this paper, we describe the DORIS project,which aims to develop a mobile robot to performmonitoring, inspection, and simple interventiontasks in an offshore platform. To this end, thesystem must be able to move throughout the mon-itored environment carrying different sensors, an-alyzing sensor data in loco or storing it for a pos-terior analysis, and interpreting the results. Thesensors can identify abnormalities such as intrud-ers in restricted areas, abandoned objects, smoke,fire, and liquid and gas leakages. Furthermore, therobot is able to make machinery diagnosis, readinstruments, and perform interventions on valvesand other equipment using an embedded manipu-lator.

In the following sections, we present anoverview of the DORIS project with particular fo-cus on the mechanical designs and the signal pro-cessing algorithms. Preliminary results with theprototypes tested so far validate the consideredmechanical concepts, and the capability of the sig-nal processing algorithms to detect, in real-time,multiple abandoned objects and audio anomaliesin the recorded audio signals of a noisy back-ground.

2 General Overview and Main Challenges

The proposed system is composed of a robot withcameras, microphones, gas, vibration and tem-perature sensors, and a manipulator arm. Therobotic device is guided by a rail and both therobot and the rail follows a modularity concept.Additional robot modules can be annexed to in-clude other sensors, and the rail track can be mod-ified by adding or replacing rail segments, thus en-abling operation in different areas of the platform.

The robot will be controlled autonomously orby teleoperation. Task managing can be eitherin automatic (programmed using a mission inter-face) or manual mode (real-time remote opera-tion). The teleoperation and monitoring capabili-ties guarantee online access to the embedded sen-sors, providing information about the surroundingenvironment and the robot operating conditionswith real-time processing. Figure 1 illustrates theoperation in a production plant.

The DORIS project can be divided into fivesubsystems: electronics, power supply, software,mechanics and signal processing.

The electronics subsystem is responsible forproviding embedded computational support forthe robot control, signal processing, task manag-ing, and local and remote communication. Thedevice motion is controlled through drivers thatcan receive position, velocity, or current setpoints.

The power supply system uses military-class

(a) Robot’s operational scenario in a production plant

(b) Detailed zoom of the robot

Figure 1: Illustration of the DORIS robot operat-ing in a production plant.

lithium-ion batteries, which have a small size anda high energy capacity. Four batteries are used topower the motors and two to power the other elec-tronics components. It is essential to monitor thebatteries’ behavior so that faults can be avoided.The power management interface is implementedthrough System Management Bus (SMBus) con-nections, allowing the electronics system to receiveall possible information about each battery state.

The main objective of the software subsystemis to allow the implementation of high- and low-level control of the robot. The tools used to de-velop DORIS software architecture must considertwo important factors: they have to be commer-cially available, and provide modular functional-ities. These requirements led to the adoption ofQt as the graphical interface framework, RobotOperating System (ROS) as the communicationmiddleware (Quigley et al., 2009), and Ubuntuas the operating system.

The software provides autonomous control(programmed tasks) and remote control through aGraphical User Interface (GUI) in the Host Con-trol Base (HCB) computer. The HCB is composedof a set of processes running in parallel denomi-nated ROS nodes, which can communicate witheach other. To deal with this environment, a newsoftware architecture called Robot Package Soft-ware is proposed, dividing the software into tools(graphical windows) and components (processingand communication unities), and grouping theminto a dynamic library.


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Considering the robot functionalities and theaggressive offshore environment, several chal-lenges should be addressed. Temperatures in off-shore facilities can vary between −30◦C to 50◦C,relative humidity can reach 100%, and there maybe splash water, salty air, storms, and high exten-sive corrosion (Graf and Pfeiffer, 2007).

Concerning robustness and safety required tooperate in classified areas, the robot must besealed against water and objects, resistant to awide temperature range, protected from impactand vibration, electrically shielded to avoid explo-sion by ignition, and equipped with a monitoringsystem.

Another challenge is that the embedded com-puters must run heavy signal processing al-gorithms, requiring high computational power.However, the power supply subsystem must effi-ciently provide power and maintain a low level ofpower consumption.

Further complications arise because the sys-tem is designed to move in confined areas and haveefficient wireless communication with operators,providing online information of sensors data. Fi-nally, the robot must have a modular and flexibledesign, employing plug and play extensions.

3 Mechanical Design

The DORIS robot must move in a 3D space per-forming horizontal, vertical, and curved move-ments. Thus, the robot’s mechanical system mustbe flexible and able to keep its orientation stable.It also has to avoid sliding and move relativelyfast, in case of emergency situations.

The robotics literature shows that guidedrobots are the most suitable motion concept forDORIS. Versatrax Vertical Crawler uses threerubber tracks to move inside a pipe (Inuktun,2014). POBOT (Fauroux and Morillon, 2010)and Pruning Climbing Robot (Kawasaki et al.,2008) are capable of moving on vertical structuresusing a self-locking property to keep the positionusing friction between the wheels and the rail.UT-PCR (Baghani et al., 2005) is a light-weightrobot that moves vertically on a rail with ordinarywheels being pressed against the rail by springs.ARTIS (Christensen et al., 2011), developed byDFKI Robotics Innovation Center, is a modularrail guided robot that moves on a rectangular crosssectioned rail and performs inspection and main-tenance tasks in ballast water tanks.

We propose a tubular rail for DORIS locomo-tion. The use of a pre-specified path reduces con-cerns as localization and obstacle avoidance andallows the robot to move relatively fast throughits workspace. Motion is simple, as the robot hasonly one degree of freedom (DoF). The use of a raillimits the robot workspace, but it may be installedto pass through key areas and its modularity al-

lows for path modification. The track correspondsto a closed circuit, allowing the robot to performperiodic inspection and monitoring tasks.

The main objectives of the mechanical projectare to design the rail, the traction and passivemodules, and the joints used to couple them. Thedesign must allow the robot to move smoothlyin a 3D space and to make full stops anywhereon the rail. Considering the severe corrosion andweather conditions in offshore environments, thechoice of materials are imperative to the successof the project and certified solutions must be con-sidered.

The robot is composed of four modules at itsdefault configuration, but it is conceived so thatother modules can be added. The total weight ofthis configuration is estimated at 50 kg and weexpect to have a maximum speed of 1m/s.

The first adopted concept considers a tubularrail with an attached rack. The idea is inspired bythe Thyssenkrupp Flow R© II stairlift. Traction isprovided by conical wheels supported on the tube,and auxiliary mechanisms with springs improvesstability. The joint to couple two modules resem-bles to a spine, being composed of multiple disksguided by steel cables with springs attached to itsends, which turn the joint flexible. This design isillustrated in Fig. 2.

Figure 2: Design of the first concept, consideringa rail with an attached rack.

The main advantage of this design is the ab-sence of sliding due to the use of a rack and pinionmechanism. However, the rack has a complex ge-ometry, which is difficult to machine, limits therobot speed and has low efficiency. Therefore, thefollowing premise was adopted for further designs:the rail must be designed to be as simple as pos-sible, leaving the complexity to the robot. Thisis also motivated by the fact that the rail may belong so that its cost should be kept to a minimum.

The following designs incorporate the use ofgimbals with wheels as guides for the module onthe rail. Two gimbals, one coupled to the otherwith orthogonal pivot axes, are mounted on themodule’s base, providing pitch and yaw rotations.


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The internal gimbal comprises four equally spacedwheels that closely encompass the rail.

The second concept uses two sets of gimbalsand a centralized traction system, composed oftwo groove wheels mounted on a prismatic basethat can slide horizontally and vertically on guidebars through linear bearings. This 2DoF pris-matic mechanism is necessary to compensate forrail curvatures. A clamping system is designed topress the traction wheels against the rail, applyingadjustable radial forces to compensate the robot’sweight. The passive modules comprises only abase and the two sets of gimbals only. Doublecardan joints are designed to couple two modules,as depicted in Fig. 3.

Figure 3: Design with gimbals and two tractionwheels mounted on a 2DoF prismatic base.

A prototype based on this design was built tovalidate the considered concepts. The tests’ re-sults, which are presented in Section 5, show thatthe use of gimbals is an proper choice concerningstability, guidance, and support. Furthermore, itis possible to have a smooth vertical motion ap-plying radial forces by the clamping mechanism.

An important advantage of this design is thesimplicity of the rail. However, the prismaticmechanism can lock in some situations, which isnot ideal. Moreover, this model has a high weight(the traction module alone is estimated to weight20kg) and the clamping mechanism is complex.

A test was set up to analyze the behavior ofpolyurethane wheels and the results show thatpolyurethane is an appropriate material to pro-vide grip.

4 Signal Processing Algorithms

The following signal processing capabilities are de-vised for the DORIS robotic platform:

• Video: use of multiple cameras (visible-light,infrared, fisheye and stereo) to detect videoanomalies such as abandoned objects, smoke,fire, liquid leakage, and intruders.

• Audio: detection of audio anomalies of im-pulsive nature, such as an explosion or the di-agnosis of rotating machines based on energy

and pitch (fundamental frequency) signaturesusing a single or a array of microphones.

• Vibration analysis: Use of acceleration sen-sors to diagnose the operation mode of rotat-ing machines, performing possible fault clas-sification, such as misalignment and unbal-ancing operation.

• Gas sensor: detection of gas leakages.

• 3D mapping: environment 3D modeling usinga laser sensor.

The main idea of all these signal processingfeatures is to make the robot perform an initialreference lap around the closed rail track, beingmanually validated by a system operator. In thesubsequent laps, all signal processing algorithmscompare the newly acquired signals with the ref-erence data to detect any form of anomaly, as in-dicated above. Once an anomalous behavior isdetected, an alarm is flagged to the system, whichstores all associated data for immediate or futurediagnosis, as represented in Fig. 4.

Processing Processing

MeasurementAudio

Algorithms

Video

Algorithms

Alarm

Event

Processor

OperatorPositioning

Sensors

Events Events

Events

Storage

Actions

Actions Actions

Processing

Algorithms

Figure 4: Diagram of signal processing capabilitiesincorporated to the DORIS robotic platform.

4.1 Video Signal Processing

The initial goal of the video processing techniquesis to identify abandoned objects in the proposedscenarios. To do so, a reference video, withoutabandoned objects, must be properly compared toa target video, which possibly contains abandonedobjects. For this comparison to be effective, thevideos must be precisely synchronized. Below fol-lows a more detailed description of our abandonedobject detection method:

• Initial Video Alignment:

To perform the initial video alignment, a max-imum likelihood approach, based on the videosmotion data and a motion model for the robot,is employed. First, the homographies between theconsecutive frames of the given video sequence arecalculated, and from them, the translational mo-tion of the camera is extracted. By integrating thehorizontal component of the camera motion along


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the track, the horizontal camera displacement isobtained as a function of the frame number.

The obtained curve is noisy due to cameravibration, but one can obtain a noiseless motionmodel by performing the least-squares fitting of apiece-wise linear model composed of two straightlines of opposite angular coefficients. In this way,we obtain a template for the DORIS robot move-ment. By performing a matching between themovement template and the noisy curve being pro-duced by the robot in the target video sequence,the video synchronization is obtained.

• Geometric Registration Between Frames:

Considering that the target and referencevideo sequences have been properly aligned intime, the speeded-up robust feature (SURF) algo-rithm is employed to identify the points of interest(PoI) on two corresponding frames of both videosequences (Bay et al., 2008). In the following, acorrespondence is determined in a point-by-pointlevel among the two PoI sets previously identi-fied, first by eliminating the ones that greatly de-viate from the translational movement restriction,and finally by using the random sample consen-sus (RANSAC) algorithm (Kong et al., 2010)(Hartley and Zisserman, 2003). Based on thesepoint correspondences, an homography (Konget al., 2010) (Hartley and Zisserman, 2003) iscomputed on the reference frame to allow a propercomparison with the corresponding frame of thetarget video.

• Image Comparison:

As the simple subtraction between the reg-istered frames does not work due to the exces-sive amount of details in the cluttered environ-ment being surveilled, the image comparison isperform by calculating the normalized cross cor-relation (NCC) between the two images. This isdone only in the frame regions where the absolutevalue of the difference between the two registeredframes is larger than a threshold. A second thresh-old is used to binarize the result, producing areasthat are candidates to have abandoned objects inthe target frame. A multiscale approach, withvariation of both the NCC window size and thedownsample factor to be employed in the framesdimensions, is used in order to allow the detectionof objects of different sizes.

• Object Detection:

In order to further reduce both false posi-tives and false negatives, the temporal filteringdescribed in (Kong et al., 2010) is employed onthe binary NCC images. After that, to increaseeven further the detection robustness, it is useda voting procedure in which a detection occursonly if the number of times a pixel is a candidate

of being part of an abandoned object, in sequen-tial frames, is larger than a given threshold, em-pirically set. It must be noted that, in order tocorrectly align the images to be compared in thetemporal filtering and voting steps, homographiesmust be calculated between the used frames.

4.2 Audio Signal Processing

The main goal of the audio signal processing blockis the detection of audio events in an acousticallyadverse environment. Among the possible audioanomalies to be detected, we consider impulsiveevents, such as an explosion or any other abnor-mal background noise, and the machine monitor-ing through energy and pitch tracking.

The main challenges for achieving such goalsinclude high reverberation level in case of enclosedspaces, and significant background noise of possi-bly non-stationarity nature.

5 Prototypes and Preliminary Results

Two prototypes have been built to test mechani-cal and signal processing concepts. The first oneis based on the Roomba robot and was developedto test video and audio anomalies detection em-ploying signal processing techniques.

The second protoype, DoriAna, was built totest the proposed mechanical concepts and trac-tion system. The real scale prototype, made withlow cost materials, was tested in horizontal andvertical motion on a rail composed of straight andcurved modules.

5.1 Roomba

To build the first prototype, a commercialRoomba from iRobot was used. It is adapted withsupports, guide wheels, a netbook to command itsmovements, and embedded sensors such as a cam-era, a microphone, and a laser range finder. Thedevice performs a back-and-forth movement insidea cable tray with speeds up to 0.5m/s.

Firstly, tests were performed in a laboratoryenvironment and then in an emergency diesel gen-erator plant at CENPES, the research center fromPetrobras S.A. (Fig. 5). This last cluttered sce-nario was essential to allow initial algorithms re-search and development, given the real world dif-ficulties that emerge.

The produced video database was used inthe study of computer vision techniques to detectabandoned objects in the surveilled scenario witha moving camera. The acquired audio databasewas used in the research of algorithms to de-tect audio anomalies of impulsive nature, eventu-ally diagnosing machinery malfunction, also tak-ing into consideration that the sensors were in amoving platform.


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Figure 5: Roomba based prototype moving in acable tray at an emergency diesel generator plant.

To test the video processing algorithms, morethan 14 hours of raw video were recorded, pro-ducing about 60 videos containing abandoned ob-jects, with 6 containing 15 objects each, and theremaining containing a single object. A total of24 different objects were used. In this database,were varied the objects’ size, types and positionalong the robot’s path, the amount of objects inthe same scene, and the illumination.

Figure 6 shows results of the detection of mul-tiple objects of different sizes in the same scene.

(a) (b)

Figure 6: (a) Backpack and box, umbrella, andbottle reference frame and (b) detection.

To develop and test the audio processing al-gorithms, a large database was devised emulat-ing the adverse audio environment of an oil plat-form and the following events of interest: (i) A re-frigeration pump, operated in two distinct modes,acted as the background noise; (ii) A fixed loud-speaker reproduced audio signals such as speech,whistling noise of a tea kettle, and 13 industrialmachines, including the sound of rotating ma-chines with different fundamental frequencies. Afixed microphone was set close to the backgroundpump and another microphone was used on themoving platform to acquire the signal of interest(heavily corrupted by a reverberating version ofthe background signal), as illustrated in Fig. 7.

Using this database, the following audiocapabilities were devised for future integration onthe DORIS system:

Pitch detection: In this case, the moving micro-

Figure 7: Recording scenario employed in audiodatabase development emulating event of inter-est (played in a loudspeaker) heavily corrupted bystrong background noise.

phone was able to capture a change on the operat-ing regime of the background pump by monitoringits fundamental frequency (pitch) along time, asdepicted in Fig. 8, despite no significant changeon the background-sound energy.

Figure 8: Background noise captured by a movingmicrophone and associated pitch analysis, indicat-ing a regime change around 18 s.

Background-noise filtering: In this case, anevent of interest (the sound generated by an indus-trial rotating machine) is heavily corrupted by thenoise signal generated by the background pump.A first lap performed by the prototype, however,is able to model the background noise, and ob-serve that it is restricted to the frequency inter-val f ∈ [200, 600] Hz. Once this interference isreduced or practically eliminated by a simple dig-ital filter, the event of interest is easily detectedby a spectral analysis, as seen in Fig. 9, allowing asubsequent analysis of its general characteristics.

The robotic platform was also able to success-fully build a 3D map of the plant in real time.Odometry, laser measurements and camera imagesare combined to build a 3D point cloud, whereeach point is associated to a color defined by RGBvalues. The 3D point cloud is processed basedon probabilistic maps using Octomap ROS node(Hornung et al., 2013), which returns a represen-tation of the environment.

Figure 10 shows the panoramic view of theenvironment and the 3D map with relatively high


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(100 Hz)

Figure 9: Detection of an event of interest in 100Hz after background noise filtering.

precision. The map can be employed by a robotfor task planning and execution, providing infor-mation for collision avoidance with obstacles inthe environment. It is also possible to process the3D colored point cloud looking for specific pat-terns in the environment, such as green and yellowpipes, or black valves.

5.2 DoriAna

DoriAna is a prototype developed to test the me-chanical design of the traction module, the pas-sive module, and the joint that couples them. Atubular track built using straight and curved seg-ments was installed in the GSCAR laboratory, inCOPPE/UFRJ. The track comprises all possiblemovements that the robot must make.

The traction module consists of a woodenbase, two sets of aluminum gimbals, polyurethanewheels, a machined prismatic mechanism thatuses linear bearings to displace the tractionwheels, and a clamping mechanism that uses abicycle brake system to apply radial forces on thetraction wheels. The passive module comprisesonly a base with two sets of gimbals. Two cou-pling joints are considered for the tests: one thatuses a spring and a steel cable, and a double Car-dan joint.

The main objective of DoriAna (Fig. 11) is totest the following mechanical concepts:

• The use of gimbals for guidance, stability, andweight support;

• The traction system mounted on a prismaticmechanism;

• The clamping system, verifying whether theapplied force is sufficient to support the robotin vertical sections;

• The two joints used to couple the modules.

Initial tests performed with the prototypeshow good performance of the gimbals in termsof stability. Even though the gimbals may shakeslightly due to irregularities on the rail surface andasymmetrical positioning of the guide wheels, thebase keeps a steady orientation while moving.

(a)

(b)

Figure 11: (a) Traction and passive module of Do-riAna prototype moving on a vertical curved sec-tion. (b) Rail installed in GSCAR/UFRJ.

The force applied by the bicycle brake sys-tem was appropriate to hold and move the robotthrough vertical sections, showing that it is possi-ble to achieve motion using only friction by apply-ing a radial force. The joint with spring and steelcable performed better than the double Cardanjoint.

As for the traction system, the conclusion wasthat the prismatic mechanism is not satisfactory,given that it is prone to lock and the weight of thetraction system led to loss of contact between thegrooved wheels and the tube. This results sug-gest investigating an alternative concept for thetraction system.

6 Conclusions

In this paper, we presented the DORIS project,which endeavors to develop an offshore facilitiesinspection and monitoring robot. The prototypeis based on rail guided modules powered by a bat-tery system and equipped with multiple sensorsthat enable detection of anomalies, such as aban-doned objects and gas leakage.

A prototype was built to validate anomalydetection under movement in a real environmentsimilar to an offshore platform. Tests proved thatthe device is able to detect multiple objects in avideo stream. Initial results with audio processing


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Figure 10: Panoramic image of the platform and the corresponding 3D map built by the prototype.

algorithms indicate the possibility of detecting au-dio abnormalities in a noisy background scenario.The prototype was also capable of building a 3Dmap of the surrounding environment.

Another prototype was built based on the me-chanical design to test related concepts. Prelimi-nary results show good overall performance of theguidance system using gimbals. It was proved thepossibility of using just wheels on a tubular rail toachieve vertical motion by applying radial forces.A joint composed of a spring and a steel cableachieved good transmission of traction betweenthe modules. The bad performance of the pris-matic system led to the adoption of a differenttraction concept.

Currently, a new mechanical concept is underdevelopment. In future works, all DORIS subsys-tems will be tested and integrated, and, finally,the complete robotic mobile monitoring system,composed of traction, sensing, battery, and ma-nipulator modules, will operate in a real offshoreplatform environment.

Acknowledgments

This work is supported primarily by Petrobras S.A. andStatoil Brazil Oil & Gas Ltda under contract COPPETEC0050.0079406.12.9 (ANP-Brazil R&D Program), and inpart by CNPq and FAPERJ.

The authors wish to thank all other members ofDORIS project, including Alex Neves, Renan Freitas,Marcos Xaud, Igor Marcovistz, Gabriel Casulari, ThiagoBraga, Fernando Coutinho, Allan da Silva, Lucas Thomaz,Gabriel Ramalho and Raphael da Silva from Federal Uni-versity of Rio de Janeiro. We also wish to thank Aud-eri Santos, Pedro Panta, Felipe Noel and Jose Almir fromALIS Tecnologia for their mechanical consulting services.

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