Wave-Aware Control of Workspace-Constrained Shipboard Robots for Motion Compensation in Rough Seas

Abstract

In this work, we introduce a predictive control framework enabling shipboard robots to execute highly dynamic maneuvers for motion compensation in rough sea conditions. Such offshore operations poses significant challenges to traditional feedback controllers in maintaining workspace constraints under extreme wave disturbances, while real-time feasibility remains a challenge for model-based planning methods, given the limited predictability of future dynamics and the computational demands of extended planning horizons. To address these challenges, we propose a hierarchical planner and a model predictive controller that integrate real-time deterministic wave forecasting with ship motion prediction to enable anticipatory maneuver planning and execution in dynamic offshore environments. We apply this framework to a Stewart-Gangway system onboard a service operation vessel, featuring a 6-DoF parallel mechanism designed for precise motion control in offshore operations. Numerical experiments demonstrate that our approach significantly outperforms traditional reactive methods in stabilizing shipboard platforms under mild sea states. Most importantly, it effectively extends the operational capabilities of the platform across a broader spectrum of sea conditions. Our study demonstrates how wave forecasting can be leveraged to enhance the operational capabilities of shipboard robotic platforms through predictive control and by exploiting their inherent agility.