ESR 7 – Real-time multi-objective voyage optimization algorithms based on on-line machine learning for efficient autonomous navigation

Host institution: CUT (SE)
Project title/WP: Real-time multi-objective voyage optimization algorithms based on on-line machine learning for efficient autonomous navigation (WP2)
Supervisory board: Prof. W. Mao (CUT, SE), Prof. J.W. Ringsberg (CUT, SE), M. Persoons (PERI, BE)
Objectives:

• Develop online machine learning algorithms to upgrade a ship’s speed-power and maneuverability models for sail planning and sailing performance monitoring
• Develop a 3D multi-objective sail planning algorithms to consider real-time estimated-time-of-arrival, surrounding traffic and future weather conditions for safe autonomous inland shipping

Expected Results:

• Self-upgrading ship speed-power and maneuverability models to describe a ship’s sailing capability at different sailing scenarios
• Online 3D multi-objective path and speed planning/updating algorithms in terms of ETA, collision and ground avoidance
• Real-time ship sailing performance monitoring, analysis and auto-navigation

Planned secondment(s):

• KU Leuven (BE): Understanding and implementing their theoretical hydrodynamic models to describe a ship’s speed-power and maneuverability for further development and integration into online machine learning algorithms (M18-20, secondment mentor: P. Slaets)
• PERI (BE): Integrate the information available in inland ECDIS systems inside the voyage optimization algorithms (M33-35, secondment mentor: M. Persoons)

Enrolment in the Doctoral degree: CUT (SE)