Study of configuration matching strategy in self-reconfigurable robots based on the graded optimization mechanism

The self-reconfigurable theory is an important research domain in the study of self-reconfigurable robots. The modules in existence only have limited degrees of freedom to move so that the study of the self-reconfigurable theory becomes more and more complex and difficult in the process of self-reconfiguration. Before starting their self-reconfiguration, robots take configuration matching that searches the most common topology between initial configuration and goal configuration, and thus self-reconfigurable efficiency can be improved and complexity can be reduced. The problem is how to carry out configration matching. In order to solve the problem, this paper proposes a self-reconfigurable configuration matching strategy based on the graded optimization mechanism. The first step is to search the common connection between matching scheme and goal configuration. The second step, whose objective function is constructed by configuration connectivity, is to search common topology according to the results of the first step. By setting two optimization objectives mentioned above, the process of configuration matching is simplified. The matching algorithm is tested by the genetic algorithm and the result shows that it is feasible.