Autonomous Collision Avoidance at Sea: A Survey
Related Experiment Video
Updated: Oct 18, 2025

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA
Published on: October 31, 2011
Hans-Christoph Burmeister1, Manfred Constapel1
1Fraunhofer Center for Maritime Logistics and Services, Hamburg, Germany.
Abstract:
In this survey, results from an investigation on collision avoidance and path planning methods developed in recent research are provided. In particular, existing methods based on Artificial Intelligence, data-driven methods based on Machine Learning, and other Data Science approaches are investigated to provide a comprehensive overview of maritime collision avoidance techniques applicable to Maritime Autonomous Surface Ships. Relevant aspects of those methods and approaches are summarized and put into suitable perspectives. As autonomous systems are expected to operate alongside or in place of conventionally manned vessels, they must comply with the COLREGs for robust decision-support/-making. Thus, the survey specifically covers how COLREGs are addressed by the investigated methods and approaches. A conclusion regarding their utilization in industrial implementations is drawn.
More Related Videos
07:43Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop
Published on: July 2, 2018
09:32Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
Published on: November 20, 2017
Related Concept Videos
Buoyancy and Stability for Submerged and Floating Bodies
Types of Global Positioning System Surveys
Design Example: Measuring Distance Between Two Points with Obstructions
Elastic Collisions: Case Study
Local Attraction
Buoyancy