Vector based Control Routines for Swarms of Path Finding Robotic Devices

Colin Chibaya


Swarm intelligence systems where robotic devices encoded with primitive actions executed at individual levels in order to cause swarm level emergent behaviour are appealing to the fields of nanotechnology and bioinformatics. Interaction between robotic devices allow improved swarm level properties with features more than the sum of the contributions of the individual robotic devices that form the swarm. However, it is challenging to pinpoint particular primitive actions which drive robotic devices towards deliberately engineered emergent behaviour. We propose an XSet model inspired by the behaviours of message passing agents. The proposed XSet model supports direct device to device interactions in which implicit communication spaces arise. In this context, an XSet puts together primitive actions, parameters, and meta information which stipulates when primitive actions are useful to robotic devices. We assess path finding and path following abilities of message passing robotic devices and compared the measures thereof to the relative performances of the stigmergic counterparts. Better message passing performances are observed when time in simulation is sufficiently long, when the population of robotic devices in the swarm is high. Besides giving a new swarm control model, message passing XSets bring us closer to more generalized swarm control rules.


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