Socialization of Industrial Robots: An Innovative Solution to improve Productivity

Abstract

Recently, interacting between humans and machines has been considered as an important factor to develop industries. In this paper, a novel intelligent approach to improve productivity in industrial environments involving both workers and industrial robots is presented. The introduced approach contains an integrated combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and the inverse kinematics method named the Socialization of Industrial Robots (SIR). In this approach, staffs can control and justify robots based on environment conditions and their technical experiences. To evaluate and test the proposed method, a famous six-degree of freedom robotic manipulator called the Stanford University Arm is modeled and simulated in MATLAB. The results of simulation have demonstrated that the proposed approach can be counted as a practicable method to develop industrial systems.