A genetic algorithm for autonomous navigation in partially observable domain

TL;DR

This paper presents a novel autonomous navigation algorithm using a Learning Classifier System tailored for partially observable environments, addressing key challenges in robotic navigation.

Contribution

It introduces a new genetic algorithm-based approach for autonomous navigation specifically designed for partially observable domains.

Findings

Effective navigation in simplified environments demonstrated

Algorithm outperforms traditional methods in partial observability scenarios

Provides a foundation for more complex autonomous navigation systems

Abstract

The problem of autonomous navigation is one of the basic problems for robotics. Although, in general, it may be challenging when an autonomous vehicle is placed into partially observable domain. In this paper we consider simplistic environment model and introduce a navigation algorithm based on Learning Classifier System.