Genetic-Algorithm Seeding Of Idiotypic Networks For Mobile-Robot Navigation

TL;DR

This paper introduces a genetic algorithm-based method for evolving diverse, adaptable behaviour sets in robot navigation systems using idiotypic networks, significantly enhancing flexibility and diversity in robot responses.

Contribution

It presents a novel encoding of robot behaviours combined with a genetic algorithm to develop multiple diverse behaviour sets for adaptive navigation.

Findings

Rapid development of successful behaviour sets within 25 minutes

Greater diversity achieved with multiple autonomous populations

Effective navigation around maze using evolved behaviours

Abstract

Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jernes idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, pre-engineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.