Research on Metamorphic Algorithm of Modular Self-Reconfigurable Robots Based Cellular Automata

This paper deals with the motion planning for the structure configuration of Modular self-reconfigurable Robotic System based cellular automata, which consists of a large number of autonomous robotic units called " cells." we present an approach to the self-reconfiguration problem where the initial configuration is transform to the desired configuration, the algorithms are inspired by cellular automata and are based geometry rules that each module evaluates independently relative to its local neighborhood. Facing the vast configuration and rules spaces of cellular automata, we propose one of the most efficient evolutionary techniques based genetic algorithms to extract rules and search configuration. Hamming distance, is the difference between configurations, is applied to the fitness function of genetic algorithms. By driving the distance between the present and goal configuration to zero, sequences of configurations are generated.