Guidance of Unmanned Surface Vehicle Fleet Using Genetic Algorithm-Based Approach.

In this contribution, we provide an approach to guidance of a fleet of unmanned surface vehicles. While the vehicles are themselves completely autonomous in their operational locomotion and obstacle avoidance, the task assignment for each vehicle is provided by a global planning process, based on the genetic algorithm. Various possibilities to the genetic algorithm are proposed and tested using a fleet of six vehicles dealing with a complex task.