Fuzzy logic based reinforcement learning of admittance control for automated robotic manufacturing

Abstract

An approach to admittance control using fuzzy logic based reinforcement learning is proposed for the robotic automation of typical manufacturing operations. Use of fuzzy logic enables the knowledge of the manufacturing process operator to be incorporated into the controller design, which is then further refined using reinforcement learning techniques. Automated robotic deburring offers an attractive alternative to manual deburring in terms of reduced costs and improved quality of the finished parts, and hence it is used as an example of a typical manufacturing task. Simulation results are presented which demonstrate the effectiveness of the proposed controller in controlling the automated robotic deburring task.