An evolutionary behavior programming system with dynamic networks for mobile robots in dynamic environments

A behavior-based approach has been effectively applied for the design of robot control systems, and evolutionary algorithms have been implemented as an approach to generate the robot control systems automatically. In this paper, we propose the integration of both concepts as an automatic behavior programming system. By adapting the idea of behavior analysis, behavioral modules and interactions are presented in order to be able to represent behavior-based control systems in a programming paradigm. Then, by manipulating the program codes without human intervention, the processes of Genetic Programming (GP) are applied to discover the possible behavior-based control systems, which successfully solve the given problems. Moreover, with the intention of improving the learning performance in dynamic environments, the new idea of turning on/off each node in the network stochastically, called a Dynamic Network (DN), is applied. Experimental results show the potential of our approach.