Collaborative lifting trajectory planning method for the multi-machine system of revolving floating cranes based on point-to-point theory

Abstract

The revolving floating crane (RFC) is widely used in large-scale offshore projects, serving as essential equipment for offshore platform construction, deep-water salvage, and related operations. As offshore projects scale up with greater weight and volume, requirement of RFC multi-machine collaborative operations is increasing. Conventional manual operations with poor coordination may compromise working safety and efficiency of the RFC multi-machine system. Therefore, intelligent unmanned operations offer an effective solution and the corresponding prerequisite is an effective and reliable lifting trajectory planning method for RFC multi-machine system. To address this, this study proposes a collaborative lifting trajectory planning (CLTP) method for multi-machine system of the RFCs based on Point-to-Point (PTP) theory, and develops an inverse dynamics solution strategy for determining each RFC’s working states. Considering constraints such as system dynamics, safety, and performance, and with the objectives of minimizing energy consumption and lifting time, the CLTP model is established. The superiority of the proposed trajectory planning method is validated by comparing with the conventional method. To verify the efficacy of the proposed RFC multi-machine trajectory planning method, numerical experiments are performed under various working conditions, including different target points, load masses, and numbers of RFCs. Results show that the CLTP method is feasible and reliable, meeting the practical needs of RFC multi-machine operations and supporting the corresponding intelligent implementation.