Development and Navigation of a Mobile Robot for ... - Semantic Scholar

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 56, NO. 9, SEPTEMBER 2009

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Development and Navigation of a Mobile Robot for Floating Production Storage and Offloading Ship Hull Inspection Luciano Luporini Menegaldo, Gustavo Andre Nunes Ferreira, Melquisedec Francisco Santos, and Rodrigo Siqueira Guerato

Abstract—This paper describes the current development status of a mobile robot designed to inspect the outer surface of large oil ship hulls and floating production storage and offloading platforms. These vessels require a detailed inspection program, using several nondestructive testing techniques. A robotic crawler designed to perform such inspections is presented here. Locomotion over the hull is provided through magnetic tracks, and the system is controlled by two networked PCs and a set of custom hardware devices to drive motors, video cameras, ultrasound, inertial platform, and other devices. Navigation algorithm uses an extended-Kalman-filter (EKF) sensor-fusion formulation, integrating odometry and inertial sensors. It was shown that the inertial navigation errors can be decreased by selecting appropriate Q and R matrices in the EKF formulation. Index Terms—Floating production storage and offloading (FPSO), inertial navigation, mobile robots, nondestructive inspection, robot localization, sensor fusion.

I. I NTRODUCTION

O

NE of the leading petroleum engineering technical challenges is deep-water offshore oil exploration. Several platform configurations are possible, among them the so-called floating production storage and offloading (FPSO) platforms. They are usually former oil tankers that receive a complete preprocessing plant over the deck. According to Moan et al. [1], until the year 2004, about 100 FPSOs were operating worldwide, most of them in tropical seas. FPSOs are particularly prone to corrosion [2], either galvanic, chemical, or anaerobic (bacterial corrosion) due to the following: long-term anchoring over the production basin, few docked revisions, and to the fact that many FPSOs are often built from old and naturally wearied vessels. Deep storage tank corrosion, which occurs under the petroleum sludge deposits, is also a problem, due to the sea water pumped together with the oil.

Manuscript received June 7, 2008; revised June 11, 2009. First published June 26, 2009; current version published August 12, 2009. This work was supported in part by the Financiadora de Estudos e Projetos (FINEP) and in part by the Conselho Nacional de Pesquisa e Desenvolvimento (CNPq), from the Brazilian Ministry of Science and Technology, through a CT-PETRO/ CT-ENERG grant. L. L. Menegaldo and R. S. Guerato are with the Department of Mechanical and Materials Engineering, Military Institute of Engineering, Rio de Janeiro, Brazil (e-mail: [email protected]; [email protected]). G. A. N. Ferreira and M. F. Santos are with Subsin Engineering, Rio de Janeiro, Brazil (e-mail: [email protected]; [email protected]; [email protected]). Digital Object Identifier 10.1109/TIE.2009.2025716

To manage this situation, inspection, evaluation, and repair activities are performed periodically according to Classification Society Rules like Bureau Veritas [3]: global visual inspection, close-up inspection, thickness measurement in predefined and custom sites, and reviewing checks. Several sets of inspection procedures are performed periodically, usually every 5, 2.5, and 1 year, or when needed. The following two main inspection paradigms can possibly be adopted: 1) docking, emptying the tanks, cleaning, and doing visual inspection, and 2) applying nondestructive testing (NDT) techniques in situ without discontinuing the oil production operations. The first approach is conservative, safer, and more reliable but requires stopping production, causing a larger economic impact [2]. The second, however, must be performed by specialized personnel like scuba divers and mountaineers for the dry part [4], using NDT probes like ultrasound (US), X-ray, thermal cameras, electrochemical sensors, and remote magnetic field measurements [5]. Usual measurement sampling is one sample per hull square meter [6], what may be not sufficient in some cases [7]. A possible strategy to increase reliability is refine the inspection grid. Doing this manually, in such a huge surface, is also expensive, due to diver and mountaineer cost. Therefore, some attempts have been carried out for using a remotely operated vehicle (ROV) or robotic crawlers to perform automated inspections. Despite the need for some special features for large ship hulls, these crawlers are quite similar to those used to inspect other metallic walls. For large fuel tanks, Neptune [8] from Carnegie Mellon University is a sophisticated tracked robot that can be used in explosive environments. Maverick developed by Solex Environmental Systems, Inc. [9] has similar applications, but is propelled by magnetic wheels. Both robots perform US inspections and navigate inside the tank by a sonar positioning system. Sogi et al. [10] developed a magnetic wheel crawler to inspect spherical gas storage tanks. Authors claim that the number of working hours was reduced by 1/6 when compared to manual inspection. The robot SURFY [11] was developed at the University of Catania, Italy. It uses eight suction cups to adhere to the wall surface, and carries US and other NDT sensors. Other examples of systems for metallic wall inspecting are the robots FURY [12], RobTank Inspec [13], and Robug II [14]. For the specific application on ship hull inspection, some authors apply adapted or custom built ROVs to perform visual and NDT analysis (see, e.g., [15] and [16]). However, these

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solutions are restricted to operate only underwater, making no inspection of the dry hull parts. One of the few solutions able to inspect both dry and underwater parts is presented by Carvalho et al. [7], at the Federal University of Rio de Janeiro and Petrobras Research Center (CENPES). This robot fixates over the hull through four magnetic wheels. It carries a video camera and an eight-channel US system. Other groups worldwide that have developed robots for the same application can be cited: the French company Cybernetix [17], the group from the Industrial Automation Institute (IAI-CSIC) from Spain [18], and the English group from London South Bank University [19]. This paper describes the current development stage of a new robot, which is able to perform US inspections, and possibly other NDT techniques, like visual and eddy currents, in the outer surface of ship hulls, namely, FPSOs docked offshore. Robot current version can be used on dry part of the hull. It consists of a crawler driven by two magnetic tracks specially designed to offer a high friction coefficient, in order to operate vertically over the dry part of the hull, where there is no thrust force from the water. In addition, the system uses two inertial measurement units (IMUs): one inside the robot and the other fixed to the ship deck. This system, together with dead reckoning, is integrated in an extended-Kalman-filter (EKF) sensor-fusion navigation algorithm to estimate the relative robot attitude and position. Some preliminary laboratory tests are presented and discussed. This paper is a modified version of the study in [20] previously presented at the AMC’08 Conference, May 26–28, Trento, Italy. Most of robot description and navigation equations were not repeated here. The new parts of the paper are related mainly to reducing navigation errors by choosing Q and R matrices of the EKF formulation. II. ROBOT D ESCRIPTION In this section, the robot systems will be described briefly, as most of the technical details and figures can be found in [20]. In the current stage of development, all the subsystems have been implemented and tested in laboratory, as well as the entire robot. However, the underwater packing stage is still being carried out, and all tests were performed in dry metallic walls. A. Mechanical Systems In previous works [20], [21], magnetic wheel crawlers, in different configurations, were built and tested to find a possible mechanical platform for the robot. In the current version, the robot uses a pair of magnetic tracks, which presents several interesting features: ability to surpass obstacles like barnacles and welds, large shear force, large contact area, differential drive and operation in dry part, since no negative hydrodynamic suction is needed. On the other hand, the overall normal force considering the entire track is greatly reduced, since the detachment of the track occurs link by link starting from the most frontal to the next, as a kind of “positive feedback.” The final version of the magnetic track was assembled using a

commercial carrier track and sprockets with polyvinyl chloride brackets where three in-line rare earth magnets were embedded. B. Electronic Systems The electronic system has two main processing cores, one laptop used by the operator and one embedded PC/104, both running Labview applications and connected through an Ethernet network. Outside the robot, and connected to the ship power source, there is a system comprising of terminals, electrical cable bobbins, circuit breakers, and a differential–residual electric safeness system. Ethernet signal is transmitted by a power line communication system. Therefore, a 220-V ac is transmitted to the robot, inside which the electric current is rectified and lowered. This arrangement allows the use of a much thinner and lighter cable. The laptop is also connected to a joystick and to a Microstrain 3DM-GX1 IMU to measure ship movements. A Labview application front end shows the operator the camera images, US signal, and attitude from an IMU in the robot (identical to the IMU fixed on the ship) and stores all sensor data. Inside the robot, the embedded PC connects to two video cameras, to an IMU unit, and to the US board. A serial RS-485 network controls two custom microcontrolled boards that generate reversible pulsewidth-modulated power signals to the dc motors connected to the tracks. These boards also read absolute encoder signals connected to the motor shaft, closing a proportional velocity-control loop, and provide hodometry signal. There is also a step motor that drives the US probe linear actuator. The robot can be controlled automatically by setting the total motor shaft angular displacement or by a joystick. In the PC/104, a series of custom stand-alone Labview VIs controls video, US, IMU, and odometry data acquisition, as well as all motor driving system hardware. A US inspection system is being currently used as the NDT technique. It can measure precisely hull thickness, whose samples are used to build B- or C-Scan representations. The US probe is moved by a step motor that drives a linear 1-DOF manipulator installed in the rear of the robot. III. NAVIGATION S YSTEM A fundamental aspect of robotic ship hull inspection is to provide a precise estimation of where each NDT measurement has been performed. The problem of robot localization has been extensively studied in robotics literature (see, e.g., [22]–[24], among others). To solve our localization problem, several techniques can be applied, either absolute or relative [22], [25]. Absolute techniques, like sonar beacons, can usually provide more accurate position estimation but require arduous preparation and are expensive. In this paper, we investigate the use of relative navigation, applying an EKF as a sensor-fusion technique [26] to estimate robot position by reading encoders and IMU data. The main objective of applying this technique is to reduce incremental error that typically occurs with odometers. In fact, some source of external absolute measurements is always required, if large areas must be roamed, unless a very precise, expensive, and heavy inertial navigation system is used.


MENEGALDO et al.: DEVELOPMENT AND NAVIGATION OF A MOBILE ROBOT FOR FPSO SHIP HULL INSPECTION

However, reducing error in relative navigation will decrease the needs for external measurements. Since the magnetic tracks provide a high adhesion force and very low slipping, the kinematical model can be formulated as a wheeled differential drive vehicle [27]. State equations taken from the study in [28] were used to model kinematics, as well as the observation model. The model and the algorithm is described in [20]. In the future, the navigation scheme could be integrated with the detection of natural or artificial landmarks over the hull, like welds between the plates, painted marks, radio-frequency identification tags [29], or optical devices [30]. By performing tests on horizontal and vertical steel surfaces, we obtained a significant improvement of localization precision using the EKF, when compared to only dead reckoning (see details in [21]). A free wheel with an encoder to measure wheel rotation (displacement) was also used in the sensor-fusion formulation. Here, the navigation algorithm was applied to a horizontal metallic plate. For other plate positions or shapes, other choices for Kalman filter parameters and switching between the multiple models could probably give more accurate results.

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Fig. 1. Real and EKF-estimated trajectories. True is the robot trajectory measured manually, ekf is the trajectory reconstruction obtained with the same Q and R matrices of the study in [20], and aekf is the trajectory reconstruction with new Q and R matrices.

where A. Choice of Q and R Matrices An important question regarding the EKF performance is tuning Q and R, i.e., the process and measurement covariance error matrices, respectively. This is usually done in practical works by trial and error and employing a previous experience [25], [28], [31], [32]. Some simplifying assumptions are usually done, like considering these matrices to be constant diagonal or symmetric. Essentially, they must express the relative information reliability between the system and the observation models [33]. If observations are more reliable than system model, Q elements should be greater than those of R, and vice versa. For the present problem, which states and state equations are described in [20], Q matrix was chosen as Q = diag σx2 , σy2 , σθ2 , σv2 , σω2

(1)

where diag means diagonal matrix with all other terms zero and σi is the maximum expected error for each state variable. After some trial-and-error tuning work using the experimental data, the chosen Q matrix was Q = diag(0.0025, 0.0025, 0.04, 0.01, 0.0016).

(2)

The R matrix is found by considering the errors given by the IMU manufacturer, the geometry of the wheels, and encoder resolution R = diag(0.07, 0.07, 0.4, 0.006).

(3)

These matrices were used in the EKF to estimate the trajectory in the previous conference paper [20]. Here, an update procedure for R was used, applying the method proposed by Hide et al. [34] ˆ ˆ Γ(k) − C(k) · P − (k) · C T (k) R(k) =Θ

(4)

ˆ Γ(k) = 1 · Θ N

k

Γ(j) · ΓT (j)

(5)

j=k−N +1

and Γ(k) is the EKF updating vector Γ(k) = z(k) − C(k) · x− (k).

(6)

This approach gives an estimate of the measurement covariance error matrix from N previous samples. In the present case, a set of N = 80 samples was used in the update. This was the minimal set, empirically determined, which the algorithm converged. IV. R ESULTS Tests were performed using a horizontal steel platform (5 m × 3 m × 2 mm). The robot was controlled to make a curved reference trajectory, starting from one of the platform corners. An ink pen was fixed in the robot, and the trajectory traced over the platform was measured manually. This was considered as the “real” trajectory. For one sample test, the results are shown in Fig. 1, and the error is shown in Fig. 2. Both estimated trajectories, using the previous approach without R update [20] (ekf) and the one introduced here (aekf), are presented. To better investigate the role of the algorithm in reducing the errors that should be expected by using only odometry, a series of experiments with different trajectories was performed. Since the surface of our steel test facility is limited to only 5 m × 3 m, the experiments were performed on a nonmagnetic floor. The conclusions from these experiments are only indicative, but may indicate up to what extent the odometry errors can be reduced. Due to slippage, the error levels are naturally greater in this new condition. Fig. 3 shows the results for three kinds of trajectories: a straight line (both motors on fed with the same


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Fig. 2. Error in x- (upper) and y-directions between real and EKF-estimated trajectories, for both sets of Q and R matrices.

voltage), a small-radius curve (one motor on, one off), and a two-round closed circular path. In the last, the motors were both on, with different voltages. Observing the straight line, a significant drift can be observed caused mainly by the low quality of the encoder used. The proposed algorithm reduced significantly the navigation error. In the small-radius curve, the trajectory has not been reproduced accurately neither by the encoder alone or using the algorithm. However, the resulting displacement error was corrected, even if the curve shape was not exact. For the circular case, the encoder solution gave a reasonable result, which was improved by the aekf. It must be noted that the Q and R matrices have been tuned independently for each test. Therefore, for practical applications, different calibration procedures should be recommended just before performing the test, over the same surface that will be inspected. From these calibrations, different Kalman gains can be found and selected in the whole trajectory reconstruction, depending on the characteristics of the actual maneuver. V. C ONCLUSION The proposed robot for ship hull inspection has shown a satisfactory locomotion and adhesion behavior in laboratory

Fig. 3. Comparison between real, odometer-reconstructed, and EKFreconstructed trajectories, (upper) for straight line, (middle) curved path, and (lower) two-round circle. For the circular trajectory, only the main points have been measured externally: Begin, end, crossing of the first round about the starting point, and maximum radius of the trajectory.


MENEGALDO et al.: DEVELOPMENT AND NAVIGATION OF A MOBILE ROBOT FOR FPSO SHIP HULL INSPECTION

dry metallic wall tests. One important constraint of the current robot version is the low displacement speed due to the high track adhesion. A new version with stronger actuators is being tested to increase inspection velocity. For the chosen test maneuver, the EKF sensor-fusion navigation approach with R matrix updating gave more precise estimation of the robot position in the y-direction, when compared to previous results without the update [20], [21]. Essentially, the new approach reduced the y error to the same relative amount of the x-direction. Further reducing both coordinates should require better quality sensors, in particular the encoders. In addition, tuning the Q and R matrices depends on the characteristics of the maneuver, requiring calibration. Other test conditions and approaches for updating simultaneously the Q and R matrices are currently being tested. Some construction drawbacks, like gear backlashes, and poor encoder resolution and reliability may have worsened the estimation, which should lead to high errors for real-life large hull surfaces. In this case, an associated absolute navigation reset should be used. In addition, better resolution encoders must be employed, either in the track or in the free wheel. ACKNOWLEDGMENT The authors would like to thank IFIEX, Pq R Mnt/ 1, AGRJ (units of Brazilian Army), and Petrobras for allowing us to use their facilities. R EFERENCES [1] T. Moan, E. Ayala-Uraga, X. Wang, and J. Paik, “Reliability-based service life assessment of FPSO structures,” Trans. Soc. Nav. Archit. Mar. Eng., vol. 112, pp. 314–342, 2004. [2] R. O. Carneval, F. C. R. Marques, and M. A. O. Smith, “Inspeção de cascos de navios do tipo FPSO (alternativas possíveis),” in Proc. XIX CONAEND—Brazilian Conf. Non-Destructive Test., São Paulo, Brazil, Aug. 2000. CD-ROM. [3] Rules and Regulation for Classification of Steel Ships, Bureau Veritas 2001. [4] D. Constantinis and D. Mortlock, “Floating production: Inspection, evaluation methods help FPSOs, FSOs avoid drydocking,” Offshore Mag., vol. 61, no. 5, May 2001. [Online]. Available: http://www.offshoremag.com/index/current-issue/s-offshore/s-volume-61/s-issue-5.html [5] “Condition assessment of aged ships,” in Proc. 16th Int. Ship Offshore Struct. Congr., J. K. Paik, F. Brennan, C. A. Carlsen, C. Daley, Y. Garbatov, L. Ivanov, C. M. Rizzo, B. C. Simonsen, N. Yamamoto, and H. Z. Zhuang, Eds., Southampton, U.K., Aug. 2006, vol. 2, pp. 265–375. [6] A. A. Carvalho, R. C. S. B. Suita, I. C. Silva, and J. M. A. Rebello, “Desenvolvimento de um sistema automatizado para inspeção ultra-sônica em casco de navio,” in Proc. COBENGE—Brazilian Conf. Eng. Educ., Porto Alegre, Brazil, 2001, pp. 48–53. [7] A. A. Carvalho, L. V. S. Sagrilo, I. C. Silva, J. M. A. Rebello, and R. O. Carneval, “On the reliability of an automated ultrasonic system for hull inspection in ship-based oil production units,” Appl. Ocean Res., vol. 25, no. 5, pp. 235–241, Oct. 2002. [8] H. Schempf, B. Chemel, and N. Evertt, “Neptune: Above-ground storage tank inspection robot system,” IEEE Robot. Autom. Mag., vol. 2, no. 2, pp. 9–15, Jun. 1995. [9] D. R. Hartsell, “Putting the maverick fuel-tank inspection robot to the test,” IEEE Robot. Autom. Mag., vol. 6, no. 3, pp. 54–64, Feb. 1999. [10] T. Sogi, Y. Kawagushi, H. Morisaki, K. Ohkawa, N. Kai, and H. Ayakawa, “Inspection robot for spherical storage tanks,” in Proc. 26th Annu. IEEE IECON, Nagoya, Japan, Oct. 2000, pp. 393–398. [11] G. L. Rosa, M. Messina, G. Muscato, and R. Sinatra, “A low-cost lightweight climbing robot for the inspection of vertical surfaces,” Mechatronics, vol. 12, no. 1, pp. 71–96, Feb. 2002.

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Luciano Luporini Menegaldo received the degree in mechanical engineering and the M.Sc. degree from the State University of Campinas, Campinas, Brazil, in 1994 and 1997, respectively, and the Ph.D. degree from the University of São Paulo, São Paulo, Brazil, in 2001. From 2001 to 2003, he was with the São Paulo State Institute for Technological Research. He was a Visiting Professor at the University of São Paulo, State University of Campinas, and University of Ribeirão Preto, Ribeirão Preto, Brazil. In 2008, he spent a sabbatical semester with the Robotics and Sensor Fusion Laboratory, “Università Roma Tre,” Rome, Italy. Currently, he is an Associate Professor at the Military Institute of Engineering, Rio de Janeiro, Brazil. He coordinates several Brazilian-government-granted research projects. His main research interests include the dynamics and control of mechatronic devices and biomechanical systems.

Gustavo Andre Nunes Ferreira received the degree in electrical engineering from São Paulo State University, Ilha Solteira, Brazil, in 1999, and the M.Sc. degree in mechatronic engineering from the University of São Paulo, São Paulo, Brazil, in 2003. He is currently an Electronic Senior Engineer with Subsin Engineering, Rio de Janeiro, Brazil, engaged in research on sensor-fusion-applied robot localization. His research interests include embedded systems, control of mechatronic devices, and robot localization.

Melquisedec Francisco Santos received the degree in mechanical engineering from Paraíba Federal University, João Pessoa, Brazil, in 1993, the M.Sc. degree in mechanical engineering from the State University of Campinas, Campinas, Brazil, in 1997, and the Ph.D. degree from the University of São Paulo, São Paulo, Brazil, in 2003. He is currently an Engineering Consultant with Subsin Engineering, Rio de Janeiro, Brazil. His research interests include riser analysis and subsea installation equipment, pipeline and structural analysis, and subsea technology.

Rodrigo Siqueira Guerato received the Electronics Technology degree and Graduate degree in physics from the São Paulo Federal Center of Technological Education, São Paulo, Brazil, in 2001 and 2007, respectively. He has extensive professional experience in electrical engineering, having worked on the development of various electrical and electronic systems for industrial process and mobile inspection robots. He is currently a Researcher with the Military Institute of Engineering, Rio de Janeiro, Brazil. His main research interests include embedded electronic systems for mobile robots.
