An evolutionary computing approach to generating useful and robust robot team behaviours

Designing control processes for mobile robot teams is a very difficult task. Consequently, methods for the teams to learn or adapt on their own are highly desirable. Genetic algorithms (GAs), which is a class of techniques inspired by biological evolution, is a mainstream method to accomplish this self-adaptation. However, a major problem of the genetic algorithm approach is the brittleness of the evolved solution(s). The solutions provided by GAs can only work properly in those environments closely resembling the training environment. A slight change in the environment can render a very poor performance. In short, the solutions lack robustness/generality and scalability. This paper discusses a design methodology to obtain robust solutions with the GA approach. The effectiveness of this methodology is demonstrated by an example. We have designed a mobile robot team with stable performance in a variety of environments.