Safe Payload Transfer with Ship-Mounted Cranes: A Robust Model Predictive Control Approach
🤖 AI Summary
Shipboard cranes operating in harsh sea conditions face significant dynamic disturbances from vessel motion, necessitating real-time control that simultaneously satisfies multiple safety constraints—obstacle avoidance, precise target positioning, and payload stability.
Method: This paper proposes a robust safety control framework integrating robust zeroing control barrier functions (RZCBFs) with online parameter adaptation to reduce conservatism, and combines nonlinear model predictive control (NMPC) with time-varying bounding-box collision avoidance for dynamic obstacle evasion and high-precision payload placement.
Contribution/Results: Evaluated on a 5-degree-of-freedom prototype system under realistic ship motion disturbances emulated by a Stewart platform, the method guarantees safety, real-time performance, and operational accuracy even under substantial base disturbances. It significantly enhances lifting reliability and autonomy in complex marine environments.
Application Category
📝 Abstract
Ensuring safe real-time control of ship-mounted cranes in unstructured transportation environments requires handling multiple safety constraints while maintaining effective payload transfer performance. Unlike traditional crane systems, ship-mounted cranes are consistently subjected to significant external disturbances affecting underactuated crane dynamics due to the ship's dynamic motion response to harsh sea conditions, which can lead to robustness issues. To tackle these challenges, we propose a robust and safe model predictive control (MPC) framework and demonstrate it on a 5-DOF crane system, where a Stewart platform simulates the external disturbances that ocean surface motions would have on the supporting ship. The crane payload transfer operation must avoid obstacles and accurately place the payload within a designated target area. We use a robust zero-order control barrier function (R-ZOCBF)-based safety constraint in the nonlinear MPC to ensure safe payload positioning, while time-varying bounding boxes are utilized for collision avoidance. We introduce a new optimization-based online robustness parameter adaptation scheme to reduce the conservativeness of R-ZOCBFs. Experimental trials on a crane prototype demonstrate the overall performance of our safe control approach under significant perturbing motions of the crane base. While our focus is on crane-facilitated transfer, the methods more generally apply to safe robotically-assisted parts mating and parts insertion.
Problem
Research questions and friction points this paper is trying to address.
Ensuring safe payload transfer under ship motion disturbances
Handling multiple safety constraints in unstructured marine environments
Achieving robust obstacle avoidance and precise payload positioning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Robust model predictive control for ship cranes
Zero-order control barrier functions ensure safety
Online robustness parameter adaptation reduces conservativeness
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