The Study of Ship Collision Avoidance Route Planning by Ant Colony Algorithm

Maritime traffic is becoming more complex every day. At present, due to technological advances and to new maritime regulations, there is increasing demand for new nautical marine instruments to be installed into the bridge, and the breadth of navigational information complicates on-duty officers’ decisions. Therefore, if decision support tools can be used to help deal with navigational decision-making, human errors arising from subjective judgments can be reduced, and sea transport safety improved. This research uses the concept of e-navigation as a framework, positioning collision avoidance path planning as the main theme, and applies an Ant Colony Algorithm (ACA) in the field of artificial intelligence to construct a collision avoidance model that imitates optimization behaviors in real-life applications. This model combines navigational practices, a maritime laws/regulations knowledge base and real-time navigation information from the AIS to plan a safe and economical collision avoidance path. Through using such planning, recommendations can be made for collision avoidance and return to course. Lastly, a Geographic Information System (GIS) was used as the platform for a navigation decision support system, combining related navigation information, collision avoidance models and electronic charts. This is a source of reference for VTS (Vessel Traffic Service) operators and on-duty officers to assess collisions in territorial waters, achieving objectives such as warning and pre-collision preparations.