Applied Machine Learning for Maritime Automation
This research addresses critical challenges in maritime automation, focusing on reducing the labor-intensive setup of in-the-loop simulation environments necessary for early commissioning testing. By applying advanced machine learning and AI techniques, my work aims to create software tools and theoretical frameworks that streamline virtual commissioning and simulation-assisted testing.
Currently, setting up simulation environments in ship commissioning requires extensive manual effort, which slows down testing and demands significant resources. My research seeks to overcome these limitations by enabling the automated creation, optimization, and validation of testing environments through AI-driven tools. This approach not only reduces time and labor but also ensures that systems meet performance standards well before deployment, supporting higher efficiency and safety standards in maritime automation systems.
Research Overview
My research focuses on streamlining virtual commissioning and simulation-assisted testing in maritime automation. By developing AI-driven tools, I aim to automate the setup and testing of simulation environments, reducing the need for manual intervention and enhancing efficiency in early-stage commissioning.
Key areas of my work include:
- Simulation Automation: Leveraging AI to generate and optimize simulation environments, minimizing labor-intensive setup.
- Real-Time Compliance: Ensuring that AI-based simulations meet the strict real-time performance requirements critical to maritime control systems.
Collaborations and Projects
My research is conducted within the Virtual Sea Trial project, a Business Finland-funded initiative that brings together university and industry experts to improve automation processes in maritime systems. I also collaborate with the NorMASS Consortium, focused on advancing autonomous surface ship technology.