In most swarm systems, agents are either aware of the position of their direct neighbors or they possess a substrate on which
they can deposit information (stigmergy). However, such resources are not always obtainable in real-world applications because
of hardware and environmental constraints. In this paper we study in 2D simulation the design of a swarm system which does
not make use of positioning information or stigmergy.
This endeavor is motivated by an application whereby a large number of Swarming Micro Air Vehicles (SMAVs), of fixed-wing
configuration, must organize autonomously to establish a wireless communication network (SMAVNET) between users located on
ground. Rather than relative or absolute positioning, agents must rely only on their own heading measurements and local communication
with neighbors.
Designing local interactions responsible for the emergence of the SMAVNET deployment and maintenance is a challenging task.
For this reason, artificial evolution is used to automatically develop neuronal controllers for the swarm of homogenous agents.
This approach has the advantage of yielding original and efficient swarming strategies. A detailed behavioral analysis is
then performed on the fittest swarm to gain insight as to the behavior of the individual agents.
Keywords Swarm intelligence - Swarming without positioning - Micro Air Vehicles (MAVs) - Communication relay - Artificial evolution - Situated communication - SMAVNET