Robotic swarm control through artificial pheromone trails

Abstract

The extraordinary capability of swarming ant species in route finding and foraging efficiency through trail trail development has been studied for many years. Scientists have been able to capture the behavior of individual ants in control algorithms and used the resulting artificial swarm of ants to solve difficult problems. These problems include function optimization, sorting, clustering and route finding. So far, the route finding capabilities, where agents solely rely on local available information, have only been demonstrated in virtual discrete domains. The aim of this thesis is the development of an agent based control method that allows a swarm of robots to establish a trail between a source and target, placed in an unknown domain. The method solely relies on information locally available to the agent. No direct communication among the agents is necessary as agents mark the environment to relay information. Simulations are performed in a Python and C-based computer program, developed in tandem with this study. A versatile controller is presented that, in conjunction with suggestions for the swarm size and pheromone characteristics, can be implemented on real robots to explore unknown environments in a multitude of scenarios.