Optimization of Tilt, List, and Skew of Sea-to-Shore Gantry Cranes

Abstract

Automation of sea-to-shore gantry cranes and use of artificial intelligence is a trend that most ports are trying to adopt in order to cope with handling of high volume of containerized cargo. Low target achievements in container handling have resulted in low ship turn around. The aim of automation was to either offload or load high volumes of containers in order to increase productivity and ship turn around. This study developed a vision-based real-time monitoring system integrated with an adaptive PID controller to detect tilt, list, and skew of spreader in sea to shore gantry cranes with high accuracy. The system integrated cameras, counters, sensors, and encoders that enable real-time monitoring and control of movements of cranes. A mathematical model was developed to describe a three-dimensional movement of a crane spreader in order to perfect tilt, list and skew. Due to automation, the time to handle a container reduced from 8 min to 2.5 min. This resulted into crane performance improvement of 68.8%. The developed monitoring system reduced operational time, enhanced safety and reduced costs.