LiDAR-Based State Estimation for Offshore Crane Lower-Block Localization
T.R. Zunderman (TU Delft - Electrical Engineering, Mathematics and Computer Science)
K.G. Langendoen – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
D. Boskos – Graduation committee member (TU Delft - Mechanical Engineering)
Lo Stouten – Mentor (Huisman Equipment BV)
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Abstract
The rapid growth of the offshore wind energy sector demands the deployment of increasingly large wind turbines, requiring specialized heavy-lift vessels equipped with massive rotary cranes. Safe and efficient load transfer during maritime operations is severely challenged by lower-block (hook) sway, induced by vessel and crane motions. Automated anti-sway control can mitigate these operational risks, but relies on accurate real-time lower-block position and velocity feedback. While state-of-the-art lower-block localization approaches typically assume a rigid crane structure or require hook-mounted active hardware, this thesis presents a real-time state estimation framework based on vessel- and boom-mounted sensors. At the massive scale of offshore cranes, structural boom elasticity introduces significant bending deflections that corrupt joint-encoder measurements. To account for these dynamics, a multi-body non-linear model incorporating a flexible boom alongside wave-induced vessel motion is derived. This model serves as the predictor step for a multi-rate filtering architecture that fuses boom-mounted 3D LiDAR data, encoders, and a vessel Motion Reference Unit.
Both Extended (EKF) and Unscented (UKF) Kalman Filters are implemented and evaluated. By explicitly accounting for structural flexibility, the framework successfully limits tracking errors, achieving a position RMSE of 1.67-3.58 cm and a velocity RMSE of 0.66-5.52 cm/s, comfortably satisfying the requirements of 5.0-10.0 cm and 5.0-10.0 cm/s per axis, respectively. The EKF systematically outperforms the UKF, yielding an average 5.2% lower position RMSE and an 11% reduction in computational runtime, demonstrating the framework's viability for real-time industrial anti-sway control.