Using aggregate motion in multi-agent teams to solve search and transport problems

The aggregate movement of animals has inspired computational models that have proven useful for controlling navigation in teams of mobile agents. Additionally, strategies adopted from social insects have been employed in a multitude of problems to perform distributed problem solving. In this work, we combine collective movements with general problem solving capabilities to build multi-agent teams that perform search and collection tasks that require not only navigational skills but also a coordinate team strategy. The results show that agents undertaking collective movements are better able to self-coordinate and propagate information than those moving independently.