Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and LocalNavigation

摘要

Navigation through obstacles such as mine fields is an important capability forautonomous underwater vehicles. One way to produce robust behavior is to perform projective planning. However, real-time performance is a critical requirement in navigation. What is needed for a truly autonomous vehicle are robust reactive rules that perform well in a wide variety of situations, and that also achieve real-time performance. In this work, SAMUEL, a learning system based on genetic algorithms, is used to learn high-performance reactive strategies for navigation and collision avoidance. (AN).