Development of a Simulator Training Platform for Fish Farm Operations

Author:

Holmen Ingunn Marie1,Thorvaldsen Trine1,Aarsæther Karl Gunnar1

Affiliation:

1. SINTEF Ocean, Trondheim, Norway

Abstract

The Norwegian aquaculture industry is accident prone compared to other industries and employees report a high number of near-accidents. Furthermore, escape of fish is a challenge for the industry. There is a potential for increased safety for both humans and fish if operators can practice operations in a safe environment. Existing simulators are not suited for this context. This paper presents results from a research and development project aimed at developing a realistic simulator-based training platform for demanding fish farm operations. Three objectives guided the development process. First, a description of operations, aimed at identifying challenges and training needs, which formed the basis for selecting training scenarios well suited for aquaculture. Second, the development of mathematical models that could be used in the simulator were developed, and finally, a curriculum for training course modules to complete the platform. Platform thus points to the integration of the simulator and the practical and theoretical education of operators. In this article, the first and second part of the process are presented and discussed.

Publisher

American Society of Mechanical Engineers

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Digital Prototyping of a Stocked Cage with Multi-Sensor Integration;2023 11th International Conference on Control, Mechatronics and Automation (ICCMA);2023-11-01

2. Real-Time Prediction of Fish Cage Behaviors under Varying Currents using Deep Neural Network;2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA);2023-08-18

3. Virtual prototyping of offshore operations: a review;Ship Technology Research;2020-10-15

4. Development and Validation of a Scenario-Based Drilling Simulator for Training and Evaluating Human Factors;IEEE Transactions on Human-Machine Systems;2020-08

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