Most of the maritime accidents are caused by human-related operational mistakes. Harsh sailing environments at sea also challenge the capability of crewmembers to operate a ship in an energy efficient and environment-friendly manner. It may even lead to serious human health problems, ship and cargo damage/loss, etc. On the other hand, the shipping industry has to put huge investment to hire and train crewmembers to work onboard. Therefore, autonomous shipping concept has been discussed for decades to achieve sustainable maritime transport. However, theoretical models associated with large uncertainties cannot give a precise prediction of a ship’s navigation and manoeuvring capability. It also limits the practical application of autonomous shipping. Today’s ships are installed with a large number of sensors to monitor their sailing performance, which can be directly used for unmanned ship navigation.
In this big data based autonomous shipping project, we will develop a complete system for autonomous shipping, based on both theoretical navigation prediction models and big data techniques for handling large real-time ship sensor data. A 3-meter autonomous vessel (AUV) will be constructed to test some major functions and navigation skills, e.g., follow arbitrary ship routes, avoid a collision, self-parking, remote control and communications. More importantly, a lot of potential industrial applications and benefits to use the AUV technologies will be pinpointed and tested at the end of the project.
Docent vid Chalmers, Mechanics and Maritime Sciences, Marine Technology
Universitetslektor vid Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Docent vid Chalmers, Space, Earth and Environment, Microwave and Optical Remote Sensing
Funding Chalmers participation during 2018–2019
Areas of Advance