Vessel Motion Sensing

Abstract

Offshore structures, like windmills and oil or gas platforms, require regular maintenance and operators to continue working. Rough seas and weather conditions caused a problem to get people on and off these offshore structures until in 2009 Ampelmann came with a solution to safely transfer people offshore. This people transfer is done by means of a motion compensating platform and gangway. With the Ampelmann solution workers are able to safely access these offshore platforms, even in rough weather and sea conditions. The vessel motions must be known to allow compensation. Currently the vessel motions are measured by Ampelmann using a highly accurate and expensive fibre optic gyroscope called a Motion Reference Unit (MRU). The MRU measures translational change in velocity by means of accelerations and rotational change in position by means of angular velocity. These changes are used to determine the position and orientation of the vessel in six Degrees of Freedom (DoF). Using a rate of change measurement system to measure position and orientation is known to introduce drift. Over time this drift influence will grow and the position and orientation of the vessel with respect to the target platform becomes inaccurate. A new vessel motion measurement system is introduced called the LiDAR Reference Unit (LRU), with Light Detection And Ranging (LiDAR). The LRU overcomes the drift problems by measuring the position of the offshore target with respect to the vessel. The LRU combines measurement information from multiple sensors to create a robust, accurate and cheaper solution. The system combines a scanning Laser Range Finder (LRF) and a micro-electromechanical Inertial Measurement Unit (IMU). To determine the vessel motions, the position of the target platform is measured and followed in every consecutive LRF scan. Obstructions caused by moving people or small details on the target platform create measurement scan point outliers. These outliers could cause mismatching of LRF scan point clouds. To avoid mismatching a landmark detection algorithm is used. Common shapes on platforms are circles and straight lines. A RANdom SAmple Consesnsus (RANSAC) algorithm is used to extract lines and arcs from the LRF scan point cloud. Only the scan points of the arcs and lines are used to measure the vessel position and orientation to the target platform. The position and orientation are determined using an Iterative Closest Point (ICP) matching algorithm. The sensor measurement data of the LRF and IMU are combined with a model based Kalman filter. The feasibility of the proposed LRU is tested with a simulation model for the system. To verify the results of the simulation model and determine the accuracy of the LRU a test setup is created. Simulated vessel motions are used to move the measurement system. The LRF scans the target platform and the measured results are compared to the position data of the measurement setup. The robustness of the motion extraction algorithm is tested by creating an obstruction of a person walking around the target platform while measuring. The vessel motions and target platform position with respect to the vessel can be accurately measured with the proposed LRU. The LRU system does not experience drift and future innovations for the Ampelmann system could be integrated with the LRU, due to the known position and orientation of the vessel to the target platform.