Testing of wireless sensor performance in Vessel-to-Vessel Motion Compensation

Tørdal, Sondre Sanden; Lovsland, Per-Ove; Hovland, Geir

doi:10.1109/iecon.2016.7793951

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Here a high precision Laser tracker was utilized to obtain positional data samples of the EM-8000, and the CGA approach was benchmarked against conventional methods. Tørdal et al (2016) used the Motion-Lab to investigate the kinematics related to 6-DOF VVMC between two floating vessels, and to test the sensor accuracy and performance of the installed MRUs in a medium-scale 1-DOF VVMC scenario.…”

Section: Methodsmentioning

confidence: 99%

The Norwegian Motion-Laboratory

Tørdal¹,

Olsen²,

Hovland³

2018

MIC

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This paper contains an overview of the equipment currently available in the Norwegian Motion Laboratory, a description of the IT networking infrastructure in the laboratory, a GitHub link to open source code developed, description of the PyQt-based graphical user interface, presentation of robot forward and inverse kinematics, presentation of equations of motion for the suspended load motion and a description of the full system kinematics. The paper ends with a list of research experiments and publications from the laboratory to date.

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Section: Methodsmentioning

confidence: 99%

The Norwegian Motion-Laboratory

Tørdal¹,

Olsen²,

Hovland³

2018

MIC

Self Cite

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“…During the calculation of the displacement according to Equation (10), it should be noted that speed over ground vector v is provided by the auxiliary sensor on the vessel, such as a log indicator [ 25 , 26 ]. The acceleration vector a could be calculated by Equation (17).…”

Section: Improved Displacement Correction Position Estimation Methmentioning

confidence: 99%

An Improved Positioning Method for Two Base Stations in AIS

Jiang

Zhang

2018

Sensors

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Resilient position, navigation, and timing (PNT) data is indispensable information in the field of e-navigation. An automatic identification system (AIS) based ranging mode (R-Mode) is put forward to develop a terrestrial backup system in order to overcome the vulnerability of the global navigation satellite system (GNSS). In general, at least three base stations are required in the traditional R-Mode positioning method. However, the geometric distribution of existing base stations is not considered for positioning, as AIS is a communication system. In some cases, a vessel can only receive signals from two base stations. In this paper, an improved position estimation method based on displacement correction is therefore proposed to solve this problem. Compared with the prior displacement correction position estimation (DCPE) method, the proposed method can improve positioning accuracy effectively by adopting a more precise motion model for the vessel, including an accelerated motion and a turning motion model. Moreover, the motion model is employed adaptively to correct the displacement of the vessel. Finally, the proposed method is verified and the performance is analyzed and compared by simulation. This study can extend the application region of AIS R-Mode.

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“…In order to use the acquired measurements from both the MRUs placed onto the two vessels, a third sensor is needed (Tørdal et al (2016)) to measure the absolute position and orientation H 1 2 between the two body-fixed coordinate frames {b 1 } and {b 2 } of MRU1 and MRU2 (see Figure 2). A camera vision tracking method capable of measuring all 6 DOFs between the camera body fixed coordinate system {b c } and the sec-ondary MRU's body-fixed coordinate system {b 2 } is proposed as a possible solution.…”

Section: Vision Tracking Of Aruco Cubementioning

confidence: 99%

“…However, in VVMC it is not enough to only measure the motion of each vessel independently, but rather to measure the relative motion between them. It is therefore believed from our previous experience and research presented by Tørdal et al (2016), that a third sensor like a camera vision system capable of measuring the absolute motions between the two floating vessels can be used together with two wirelessly connected MRUs placed onto the two vessels in order to track the relative motions in real-time. Extensive research within the field of motion tracking using camera vision has already been carried out, and efficient programming libraries such as OpenCV (Itseez (2015)) provide a lot of functions which can be used for tracking rigid objects seen by a camera.…”

Section: Introductionmentioning

confidence: 99%

Relative Vessel Motion Tracking using Sensor Fusion, Aruco Markers, and MRU Sensors

Tørdal¹,

Hovland²

2017

MIC

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This paper presents a novel approach for estimating the relative motion between two moving offshore vessels. The method is based on a sensor fusion algorithm including a vision system and two motion reference units (MRUs). The vision system makes use of the open-source computer vision library OpenCV and a cube with Aruco markers placed onto each of the cube sides. The Extended Quaternion Kalman Filter (EQKF) is used for bad pose rejection for the vision system. The presented sensor fusion algorithm is based on the Indirect Feedforward Kalman Filter for error estimation. The system is self-calibrating in the sense that the Aruco cube can be placed in an arbitrary location on the secondary vessel. Experimental 6-DOF results demonstrate the accuracy and efficiency of the proposed sensor fusion method compared with the internal joint sensors of two Stewart platforms and the industrial robot. The standard deviation error was found to be 31mm or better when the Arcuo cube was placed at three different locations.

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Testing of wireless sensor performance in Vessel-to-Vessel Motion Compensation

Cited by 6 publications

References 6 publications

The Norwegian Motion-Laboratory

The Norwegian Motion-Laboratory

An Improved Positioning Method for Two Base Stations in AIS

Relative Vessel Motion Tracking using Sensor Fusion, Aruco Markers, and MRU Sensors

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