Safe Payload Transfer with Ship-Mounted Cranes: A Robust Model Predictive Control Approach
Ersin Das, William A. Welch, Patrick Spieler, Keenan Albee, Aurelio Noca, Jeffrey Edlund, Jonathan Becktor, Thomas Touma, Jessica Todd, Sriramya Bhamidipati, Stella Kombo, Maira Saboia Da Silva, Anna Sabel, Grace Lim, Rohan Thakker, Amir Rahmani, Joel Burdick
AI summary
Problem
Ship-mounted cranes face severe safety and robustness challenges due to ocean-induced ship motions and underactuated dynamics, making traditional control methods too conservative or unsafe for precise payload transfer.
Approach
The method combines nonlinear model predictive control with robust zero-order control barrier functions and time-varying bounding boxes, using a novel online optimization scheme to dynamically adjust robustness parameters in real-time.
Key results
- Online adaptive tuning scheme for R-ZOCBF parameters
- Time-varying bounding boxes for dynamic collision avoidance
- Smooth safety function for precise target insertion
- Experimental validation on a 5-DOF crane prototype under simulated ship motions
Why it matters
Provides a practical, safety-guaranteed control framework for offshore logistics and robotic assembly tasks in highly disturbed environments.
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 sup- porting 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. Exper- imental 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.