Strengths and synergies of evolved and designed controllers: A study within collective robotics
This paper analyses the strengths and weaknesses of self-orgainising approaches, such as evolutionary robotics, and direct design approaches, such as behaviour-based controllers, for the production of autonomous robots' controllers, and shows how the two approaches can be usefully combined. in particular, the paper proposes a method for encoding evolved neural-network based behaviours into motor schema-based controllers and then shows how these controllers can be modified and combined to produce robots capable of solving new tasks. The method has been validated in the context of a collective robotics scenario in which a group of physically assembled simulated autonomous robots are requested to produce different forms of coordinated behaviours (e.g., coordinated motion, walled-arena exiting, and light pursuing). (c) 2009 Elsevier B.V. All rights reserved.
motor schema-based controllers neural networks genetic algorithms self-organisation potential fields modularity multi-variable statistical regression