Interaction of robot swarms using the honeybee-inspired control algorithm BEECLUST

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan, sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. In this work we investigated how robust a robot swarm acts against disturbances caused by another robot swarm, both using the BEECLUST algorithm, which is inspired by honeybee behaviour. For our investigation we simulated an environment with an ambient illuminance, a light spot and a shadow spot. In such an environment we tested two different castes of Jasmine III robots whereas each caste had to perform a different task. One swarm aggregates at places of high illuminance (light spot) and the other one at places of low illuminance (shadow spot). We show that small swarm populations can benefit from the presence of another robot swarm. Medium populated swarms are affected neither positively nor negatively. Large swarm populations act robust against disturbances caused by other robot swarms as long as no jamming effects occur. In this article we show that the BEECLUST algorithm provides all features for making collective decisions. Furthermore we show that the robustness of the BEECLUST algorithm allows us to control a heterogeneous robot swarm in environments which demand differing controller strategies and swarm intelligent behaviour. 1. Introduction Swarms of autonomous entities (animals, robots) show interesting features: huge numbers of agents act and interact in high densities and often perform their tasks in separated specialized cohorts [1]. The regulation of the agents' behaviours is often self-organized and swarm intelligent [2–4], features that are mainly achieved by various interlinked feedback loops within the swarm system. A prime example for such self-organized and swarm-intelligent systems would be the nectar source selection by honeybees or the trail formation in ants [5]. The requirements for identifying a system as swarm intelligent were defined by Millonas [6] and in other terms also by Sahin [7]: