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.

翻译：在非结构化运输环境中确保船载起重机的实时安全控制，需在维持有效载荷转移性能的同时处理多重安全约束。与传统起重机系统不同，船载起重机因船舶在恶劣海况下的动态运动响应而持续承受显著外部扰动，这些扰动会影响欠驱动起重机动力学并可能引发鲁棒性问题。为应对这些挑战，我们提出一种鲁棒安全的模型预测控制（MPC）框架，并在一个5自由度起重机系统上进行验证，其中斯图尔特平台模拟了海面运动对支撑船舶造成的外部扰动。起重机载荷转移操作必须避开障碍物并将载荷精确放置于指定目标区域内。我们在非线性MPC中采用基于鲁棒零阶控制屏障函数（R-ZOCBF）的安全约束来确保载荷安全定位，同时使用时变边界框进行碰撞规避。我们提出一种新的基于优化的在线鲁棒参数自适应方案，以降低R-ZOCBF的保守性。在起重机原型机上进行的实验验证了我们的安全控制方法在起重机基座显著扰动运动下的整体性能。尽管研究聚焦于起重机辅助的转移任务，但所提方法可更广泛地应用于机器人辅助零件配合与装配的安全控制。