Improved Collision Avoidance Method for Autonomous Surface Vessels Based on Model Predictive Control Using Particle Swarm Optimization

Abstract

Most collision accidents are caused by human errors in an encounter situation between vessels. Autonomous operations have received much interest among researchers to reduce human errors resulting from improper vessel maneuvering actions. In this paper, an intelligent collision avoidance method that considers not only the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) but also complex encounter situations with multiple obstacle vessels was proposed for autonomous surface vessels (ASVs). The results were verified by comparing the performance with that of existing methods. To take timely maneuvering actions to prevent the collision of vessels involved in an encounter, fuzzy comprehensive evaluation, and model predictive control using particle swarm were applied to path planning. As a result, ASVs can take proper action to avoid collisions in compliance with COLREGs based on the proposed method. The improved collision avoidance method exhibits better performance than existing methods.

Maritime Search Path Planning Method of an Unmanned Surface Vehicle Based on an Improved Bug Algorithm

“…With the characteristics of autonomy, flexibility, intelligence, and information technology, USV provides a new way for the development of sea search work [1]. Due to the complicated and changing sea environment, path planning and obstacle avoidance technology are essential for USV to conduct a search assignment at sea [2,3].…”