Genetic algorithm seeding of idiotypic networks for mobile-robot navigation

Whitbrook, Amanda; Aickelin, Uwe; Garibaldi, Jonathan M.

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. Jerne’s 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, preengineered 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.

Citation

Whitbrook, A., Aickelin, U., & Garibaldi, J. M. Genetic algorithm seeding of idiotypic networks for mobile-robot navigation.

• Conference Name: 5th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2008)
• End Date: May 15, 2008
• Deposit Date: Jan 30, 2009
• Peer Reviewed: Peer Reviewed
• Keywords: Mobile-robot navigation, genetic algorithm, artificial immune system, idiotypic network
• Public URL: https://nottingham-repository.worktribe.com/output/1016345
• Publisher URL: http://www.icinco.org/icinco2008/cfp.htm