Authors:
Amanda M. Whitbrook
;
Uwe Aickelin
and
Jonathan M. Garibaldi
Affiliation:
ASAP Research Group, School of Computer Science, University of Nottingham, United Kingdom
Keyword(s):
Mobile-robot navigation, genetic algorithm, artificial immune system, idiotypic network.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Modeling, Simulation and Architectures
;
Reasoning about Action for Intelligent Robots
;
Robotics and Automation
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, 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 su
ccessful 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.
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