This paper proposes a flexible transfer system (FTS) as one of the self-organizing manufacturing systems. The FTS is composed of autonomous robotic modules, which transfer a palette carrying an object. Through the self-organization of a multilayer strategic vector field corresponding to a task, the FTS can generate quasi-optimal transfer path in fully distributed way. We apply the learning automata for path generation algorithm. Simulation is conducted to evaluate the basic performance of the system and the results show the feasibility of application. Also, the developed hardware is explained.

The flexible transfer system (FTS) is a self-organizing manufacturing system composed of autonomous robotic modules, which transfer a palette carrying machining parts. The central issue is realization of both higher efficiency and flexibility to cope with environmental change, such as a sudden change of machining plan or breakdowns of the modules. Through the self-organization of a multi-layered strategic vector field corresponding to a task, the FTS can generate a quasi-optimal transfer path with learning automata. Also, the optimal planning is attempted by use of genetic algorithms, and is based on the global information on the system. We propose a hybridization method between the distributed and centralized approaches. Simulation is conducted to evaluate the basic system performance and the results show the effectiveness.