Performance of digital pheromones for swarming vehicle control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Collective intelligence and bush fire spotting
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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With recent and ongoing improvements to unmanned aerial vehicle (UAV) endurance and availability, they are in a unique position to provide long term surveillance in risky environments. This paper presents a swarm intelligence algorithm for executing an exhaustive and persistent search of a non-trivial area of interest using a decentralized UAV swarm without long range communication. The algorithm allows for an environment containing arbitrary arrangements of no-fly zones, non-uniform levels of priority and dynamic priority changes in response to target acquisition or external commands. Performance is quantitatively analysed via comparative simulation with another leading algorithm of its class.