SoftICE presenting intelligent virtual prototyping at ECMS 2017

SoftICE members Robin T. Bye and Ibrahim A. Hameed will be presenting some recent research results on intelligent virtual prototyping of maritime winches in two scientific papers to be presented at the 31st European Conference on Modelling and Simulation (ECMS) 2017 in Budapest, Hungary, on 23–26 May. The papers are co-authored by the two abovementioned researchers together with SoftICE colleagues Ottar L. Osen and  Webjørn Rekdalsbakken, as well as Birger Skogeng Pedersen (Mechatronics Lab, NTNU):

  • Robin T. Bye, Ibrahim A. Hameed, Birger Skogeng Pedersen, and Ottar L. Osen. An intelligent winch prototyping tool. In Proceedings of the 31st European Conference on Modelling and Simulation (ECMS ’17), May 2017. Download pdf.
  • Ibrahim A. Hameed, Robin T. Bye, Birger Skogeng Pedersen, and Ottar L. Osen. Evolutionary winch design using an online winch prototyping tool. In Proceedings of the 31st European Conference on Modelling and Simulation (ECMS ’17), May 2017. Download pdf.

A Prezi presentation of the first paper is available here:

Interactive Prezi presentation: An Intelligent Winch Prototyping Tool (ECMS’17)The full papers are available for download here: | Publications

The paper abstracts are provided at the end of this blog post.

Intelligent computer-automated design of cranes and winches

W build on our earlier work on intelligent computer-automated product design, where we have used methods from artificial intelligence (AI) such as genetic algorithms (GAs), particle swarm optimisation (PSO), and simulated annealing (SA) to optimise offshore crane design. Within a matter of only minutes, the algorithms were able to outperform the design of a real and delivered offshore crane with respect to some desired key performance indicators (KPIs). A human being would likely spend days or weeks to obtain the same results.

Generic and modular product optimization system.

Here, we focus on an intelligent winch prototyping tool (WPT):

Intelligent Winch Prototyping Tool (WPT)

We perform several test with various algorithms and are able to optimize a set of winch design parameter values that yield winch designs with suitable torque profiles:

Torque profiles for winch. The black profile has been optimized by means of a GA.

Abstract: An intelligent winch prototyping tool

In this paper we present a recently developed intelligent winch prototyping tool for ptimising the design of maritime winches, continuing our recent line of work using
artificial intelligence for intelligent computer-automated design of offshore cranes. The tool consists of three main components: (i) a winch calculator for determining key
performance indicators for a given winch design; (ii) a genetic algorithm that interrogates the winch calculator to optimise a chosen set of design parameters; and (iii) a web graphical user interface connected with (i) and (ii) such that winch designers can use it to manually design new winches or optimise the design by the click of a button. We demonstrate the feasibility of our work by a case study in which we improve the torque profiles of a default winch design by means of optimisation. Extending our generic and modular software framework for intelligent product optimisation, the winch calculator can easily be interfaced to external product optimisation clients by means of the HTTP and WebSocket protocols and a standardised JSON data format. In an accompanying paper submitted concurrently to this conference, we present one such client developed in Matlab that incorporates a variety of intelligent algorithms for the optimisation of maritime winch design.

Abstract: Evolutionary winch design using an online winch prototyping tool

This paper extends the work of a concurrent paper on an intelligent winch prototyping tool (WPT) that is part of a generic and modular software framework for intelligent computer-automated product design. Within this framework, we have implemented a Matlab winch optimisation client (MWOC) that connects to the WPT and employs four evolutionary optimisation algorithms to optimise winch design. The four algorithms we employ are (i) a genetic algorithm (GA), (ii) particle swarm optimisation (PSO), (iii) simulated annealing (SA), and (iv) a multi-objective optimisation genetic algorithm (MOOGA). Here, we explore the capabilities of MWOC in a case study where we show that given a set of design guidelines and a suitable objective function based on these guidelines, we are able to optimise a particular winch design with respect to some desired design criteria. Our research has taken place in close cooperation with two maritime industrial partners, Seaonics AS and ICD Software AS, through two innovation and research projects on applying artificial intelligence for intelligent computer-automated design of maritime equipment such as offshore cranes and maritime

More information

We have previously presented some details of our work on intelligent virtual prototyping of cranes and winches in earlier blog posts:


The SoftICE lab at NTNU in Ålesund wishes to thank ICD Software AS for their contribution in the software development process, and Seaonics AS for providing
documentation and insight into the design and manufacturing process of offshore cranes. We are also grateful for the support provided by Regionalt Forskningsfond
(RFF) Midt-Norge and the Research Council of Norway through the VRI research projects Artificial Intelligence for Crane Design (Kunstig intelligens for krandesign
(KIK)), grant no. 241238, and Artificial Intelligence for Winch Design (Kunstig intelligens for vinsjdesign (KIV)), grant no. 249171.


Parties interested in research collaboration, testing our software, or more information are encouraged to contact us.

The SoftICE Lab

NTNU Ålesund students win prestigious automation engineering award on USVs for aqua farm inspection

Better late than never!

In February 2017, bachelor students of automation engineering at NTNU Ålesund, Albert Havnegjerde, Vegard Kamsvåg og Sveinung Liavaag, won the prestigious Norwegian national award for the best bachelor thesis 2016 on automatic control given by the Norwegian Society for Automatic Control (NFA).

Jury member Rune Volden from Ulstein Power & Control hands over the award to NTNU Ålesund students Sveinung Liavaag and Albert Havnegjerde. Vegard Kamsvåg was unable to attend. Image courtesy: NFA

The students also co-wrote a paper based on their work together with SoftICE members Robin T. Bye and Ottar L. Osen (student supervisor) that was presented at IEEE Techno-Ocean 2016 and subsequently published in the proceedings:

  • Ottar L. Osen, Albert Havnegjerde, Vegard Kamsvåg, Sveinung Liavaag, and Robin T. Bye. A Low Cost USV for Aqua Farm Inspection. In Proceedings of IEEE Techno-Ocean ’16, pages 291–298, October 2016.

In their work, the students employed rapid prototyping to develop a low cost (~2000 EUR) remotely controlled unmanned surface vessel (USV) intended for inspection of aqua farms whilst incorporating a dynamic positioning (DP) system.

This work was partly financed by an internal educational project called Research-based and Innovation-driven Learning through Final Year Projects (Forskningsbasert og innovasjonsdrevet læring gjennom avsluttende oppgave – FILA).

The full paper and the conference presentation is available for download here: | Publications

Abstract: A Low Cost USV for Aqua Farm Inspection

This paper describes the rapid prototyping of a low cost remotely controlled unmanned surface vessel (USV) intended for inspection of aqua farms. There is an increased focus on inspection of ocean-based aqua farms due to three major challenges: escaping fish, sea lice, and algae. Escaping fish may bring diseases to other fish or interbreed with wild fish and damage their gene material. Sea lice is a parasite that may seriously damage the fish, lower its food quality, and if not treated, can spawn and multiply into an epidemic. Finally, algae blooms may lower oxygen levels and kill the fish. To proactively counter these challenges, aqua farm operators need to regularly inspect the fish cages for holes, the water for algae, and the fish for sea lice. Modern ocean-based aqua farms are usually constructed with two rows of sea cages separated by a gangway in the middle, often with a small operation and machinery building at one end. Staff visually inspect the cages from above and from the nearside by walking up and down the gangway. Inspection of the outer side of a cage will normally require a boat with a human inspector on board, whereas subsea inspection will normally require a human diver. Here, we propose a USV design solution for this kind of inspection that provides the aqua farm operator with a remotely controlled unmanned boat and subsea video feed. A working prototype has been designed in less than six months and successfully tested at sea.

Index Terms—USV; ROV; dynamic positioning; low cost; commercial off-the-shelf; rapid prototyping; aquaculture.