Maintaining Diversity in Robot Swarms with Distributed Embodied Evolution

Abstract

In this paper, we investigate how behavioral diversity can be maintained in evolving robot swarms by using distributed Embodied Evolution. In these approaches, each robot in the swarm runs a separate evolutionary algorithm, and populations on each robot are built through local communication when robots meet; therefore, genome survival results not only from fitness-based selection but also from spatial spread. To better understand how diversity is maintained in distributed EE, we propose a postanalysis diversity measure, that we take from two perspectives, global diversity (over the swarm), and local diversity (on each robot), on two swarm robotic tasks (navigation and item collection), with different intensities of selection pressure, and compare the results of distributed EE to a centralized case. We conclude that distributed evolution intrinsically maintains a larger behavioral diversity when compared to centralized evolution, which allows for the search algorithm to reach higher performances, especially in the more challenging collection task.