We report on recent work in which we employed artificial evolution to design neural network controllers for small, homogeneous teams of mobile autonomous robots. The robots were evolved to perform a formation-movement task from random starting positions, equipped only with infrared sensors. The dual constraints of homogeneity and minimal sensors make this a non-trivial task. We describe the behaviour of a successful system in which robots adopt and maintain functionally distinct roles in order to achieve the task. We believe this to be the first example of the use of artificial evolution to design coordinated, cooperative behaviour for real robots.

Insects are able to navigate reliably between food and nest using only visual information. This behavior has inspired many models of visual landmark guidance, some of which have been tested on autonomous robots. The majority of these models work by comparing the agent's current view with a view of the world stored when the agent was at the goal. The region from which agents can successfully reach home is therefore limited to the goal's visual locale, that is, the area around the goal where the visual scene is not radically different to the goal position. Ants are known to navigate over large distances using visually guided routes consisting of a series of visual memories. Taking inspiration from such route navigation, we propose a framework for linking together local navigation methods. We implement this framework on a robotic platform and test it in a series of environments in which local navigation methods fail. Finally, we show that the framework is robust to environments of varying complexity.

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