This paper introduces a flexible and general control system architecture that allows for modelling, simulation and control of different models of maritime cranes and, more generally, robotic arms by using the same universal input device regardless of their differences in size, kinematic structure, degrees of freedom, body morphology, constraints and affordances. The manipulators that are to be controlled can be added to the system simply by defining the corresponding Denavit-Hartenberg table and their joint limits. The models can be simulated in a 3D visualisation environment, which provides the user with an intuitive visual feedback.The presented architecture represents the base for the research of a flexible mapping procedure between a universal input device and the manipulators to be controlled. As a case study, our first attempt of implementing such a mapping algorithm is also presented. This method is bio-inspired and it is based on the use of Genetic Algorithms (GA). Using this approach, the system is able to automatically learn the inverse kinematic properties of different models.Related simulations were carried out to validate the efficiency of proposed architecture and mapping method.

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Four case studies are presented to demonstrate the potential of JOpenShowVar. The first two case studies are openloop applications, while the last two case studies describe the possibility of implementing closed-loop applications. In the first case study, the proposed interface is used to make it possible for an Android mobile device to control a Kuka KR 6 R900 SIXX (KR AGILUS) manipulator. In the second case study, the same Kuka robot is used to perform a two-dimensional line-following task that can be used for applications like advanced welding operations and similar. In the third case study, a closed-loop application is developed to control the same manipulator with a Leap Motion Controller that supports hand and finger motions as input without requiring contact or touching. In the fourth case study, a bidirectional closed-loop coupling is established between a Force Dimension omega.7 haptic device and the same Kuka manipulator. Related experiments are carried out to validate the efficiency and flexibility of the proposed communication interface.Index Terms-Robot interface, Kuka industrial robots, input device.

The main goal of the Functional Mock-up Interface (FMI) standard is to allow the sharing of simulation models across tools. To accomplish this, FMI relies on a combination of XML-files and compiled C-code packaged in a zip archive. This archive is called a Functional Mock-up Unit (FMU). In theory, an FMU can support multiple platforms, but not necessarily in practice. Furthermore, software libraries for interacting with FMUs may not be available in a particular language or platform. Another issue is related to the protection of intellectual property (IP). While an FMU is free to only provide the C-code in its binary form, other resources within the FMU may be unprotected. Distributing models in binary form also opens up the possibility that they may contain malicious code. In order to meet these challenges, this paper presents an open-source co-simulation framework based on FMI, which is language and platform independent thanks to the use of well-established remote procedure call (RPC) technologies. One or more FMUs are wrapped inside a server program supporting one or more language independent RPC systems over various network protocols. Together, they allow cross-platform invocation of FMUs from multiple, including previously unsupported, languages. The client-server architecture allows the effective protection of IP while also providing a means of protecting users from malicious code.

A benchmark framework for advanced control methods of maritime cranes is presented based on the use of the Functional Mockup Interface (FMI). The system integrates different manipulator models, all the corresponding hydraulic systems, various vessels, and the surrounding environment for visualisation. Different control methods can be transparently implemented and tested. A set of routine tests, different cost functions and metrics are provided -taking into account several factors, including position accuracy, energy consumption, quality, and safety for both the cranes and the surrounding environment. The concept of operation profiles (OP) is introduced, allowing for definition of different standard transporting and lifting operations. By considering task-oriented routines, this benchmark suite allows the comparison of different control methods independently from the specific crane model to be controlled.Two alternative control methods for maritime cranes based on the use of artificial intelligence (AI) are extensively compared. The first method is based on the use of genetic algorithms (GA), while the second method involves the use of particle swarm optimisation (PSO). Simulation results are presented for both methods.