An immune network approach for self-adaptive ensembles of autonomic components: a case study in swarm robotics

Abstract

We describe an immune inspired approach to achieve self-expression within an ensemble, i.e. enabling an ensemble of autonomic components to dynamically change their coordi-nation pattern during the runtime execution of a given task. Building on previous work using idiotypic networks, we con-sider robotic swarms in which each robot has a lymph node containing a set of antibodies describing conditions under which different coordination patterns can be applied. Anti-bodies are shared between robots that come into communi-cation range facilitating collaboration. Tests in simulation in robotic arenas of varying complexity show that the swarm is able to learn suitable patterns and effectively achieve a forag-ing task, particularly in arenas of high complexity.