
Integrated Flexible Maritime Crane Architecture for the Offshore Simulation Centre AS (OSC): A Flexible Framework for Alternative Maritime Crane Control Algorithms
Author(s) -
Filippo Sanfilippo,
Lars Ivar Hatledal,
Arne Styve,
Kristin Ytterstad Pettersen,
Houxiang Zhang
Publication year - 2016
Publication title -
ieee journal of oceanic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 97
eISSN - 1558-1691
pISSN - 0364-9059
DOI - 10.1109/joe.2015.2439891
Subject(s) - geoscience , power, energy and industry applications
The Offshore Simulator Centre AS (OSC) is the world's most advanced provider of simulators for demanding offshore operations. However, even though the OSC provides very powerful simulation tools, it is mainly designed for training purposes and it does not inherently offer any flexible methods concerning the control methodology. In fact, each crane model is controlled with a dedicated control algorithm that cannot be modified, accessed, or replaced at runtime. As a result, it is not possible to dynamically switch between different control methods, nor is it possible to easily investigate alternative control approaches. To overcome these problems, a flexible and general control system architecture that allows for modeling flexible control algorithms of maritime cranes and more generally, robotic arms, was previously presented by our research group. However, in the previous work, a generic game engine was used to visualize the different models. In this work, the flexible and general control system architecture is integrated with a crane simulator developed by the OSC taking full advantage of the provided domain-consistent simulation tools. The Google Protocol Buffers protocol is adopted to realize the communication protocol. This integration establishes the base for the research of alternative control algorithms, which can be efficiently tested in a realistic maritime simulation environment. As a validating case study, an alternative control method based on particle swarm optimization (PSO) is also presented. Related simulations are carried out to validate the efficiency of the proposed integration.