Federated Learning-Based Framework to Improve the Operational Efficiency of an Articulated Robot Manufacturing Environment

Abstract

Although articulated robots with flexible automation systems are essential for implementing smart factories, their high initial investment costs make them difficult for small and medium-sized enterprises to implement. This study proposes a federated learning-based articulated robot control framework to improve the task completion of multiple articulated robots used in automated systems under limited computing resources. The proposed framework consists of two modules: (1) a federated learning module for the cooperative training of multiple joint robots on a part-picking task and (2) an articulated robot control module to balance the efficiency of limited resources. The proposed framework is applied to cases with different numbers of joint robots, and its performance is evaluated in terms of training completion time, resource share ratio, network traffic, and completion time of a picking task. Under the devised framework, the experiment demonstrates object recognition by three joint robots with an accuracy of approximately 80% at a minimum number of learning rounds of 76 and with a network traffic intensity of 2303.5 MB. As a result, this study contributes to the expansion of federated learning use for articulated robot control in limited environments, such as small and medium-sized enterprises.