Optimising Pheromone Communication in a UAV Swarm


Communications in multi-robot systems is a key factor, especially when aiming for real-world applications. In this article we address the optimisation of the communications in a swarm of unmanned aerial vehicles for surveillance applications. More precisely a genetic algorithm is introduced to optimize the exchange of pheromone maps used in the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model which enhance the vehicles' routes in order to achieve unpredictable trajectories as well as maximise area coverage.

Proceedings of the Genetic and Evolutionary Computation Conference Companion