Evolution of Hierarchical Controllers for Multirobot Systems
Instituto de Telecomunicacoes & Instituto Universitario de Lisboa (ISCTE-IUL), Lisbon, Portugal, Miguel Duarte, Sancho Moura Oliveira, Anders Lyhne Christensen · 2014
Decentralized control for multirobot systems is difficult to design by hand because the behavioral rules for individual robots cannot, in general, be derived from a desired collective behavior. System designers have therefore resorted to evo-lutionary computation as a means to heuristically synthesize self-organized behaviors for robot collectives. Evolutionary computation is typically applied by putting the rules gov-erning the individual robots under evolutionary control and by assigning fitness scores based on collective performance. Scaling evolutionary approaches to complex tasks has, how-ever, proven challenging due to issues related to bootstrap-ping and premature convergence. In this paper, we show how hierarchical task decomposition and the combination of evolved and preprogrammed control can overcome these is-sues. We apply our approach to a complex multirobot task that requires a high degree of coordination and collective de-cision making, and we synthesize controllers capable of solv-ing the task.