Cooperative Robot Control in Flexible Manufacturing Cells: Centralized vs. Distributed Approaches

Abstract

This paper introduces a novel approach for the control of flexible manufacturing units by means of cooperatively interacting industrial robots. For fast adoption to the actual production requirements, we embed a learning module into the manufacturing cell. This learning module allows the robots to learn to solve the given task with respect to a given optimization objective. Simultaneously, the robots learn to efficiently cooperate and find an optimal collective behavior while solving the task. To this end, we develop two different control algorithms based on reinforcement learning. The first approach is based on a centralized agent which coordinates the learning behavior of the whole manufacturing cell. In the second approach, a learning agent is assigned to each robot allowing for more flexibility and reducing the state-action space of the reinforcement learning problem at hand. The approaches are applied to a laboratory testbed using two cooperating industrial robots which should learn to optimize the throughput of the manufacturing cell. A comparison of both approaches shows the improved performance of the decentralized learning agents compared to the centralized one both in goal achievement and learning speed.