Evolution of Heterogeneous Multirobot Systems Through Behavioural Diversity

Heterogeneity is present in many collective systems found in nature and considered fundamental for effective task execution in several complex, real-world scenarios. Evolutionary computation has the potential to automate the design of multirobot systems, but to date, it has mostly been applied to the design of homogeneous systems. We have recently demonstrated that novelty search can overcome deception and bootstrapping issues in the evolution of homogeneous robot swarms. In this research, we study how evolutionary techniques based on behavioural diversity (such as novelty search) can contribute to the evolution of heterogeneous multirobot systems. The results obtained so far show that novelty search can overcome open issues in the cooperative coevolution of multiagent systems, and lead to more effective and diverse solutions.