Intelligent modeling and decision making for the control of industrial robot system based on neuro fuzzy approach

Abstract

An important problem that faces the manufactures in the industry is how automatically backs up a truck like mobile robot to a specified point on a loading dock while loading and unloading. The task of controlling the mobile robot is an imperative problem. For tackling, this challenging problem, the neuro fuzzy control technique can be used. Here a truck like mobile robot is being considered. The ability to move ia an intuitive skill for human beings. The scopes for adopting artificial intelligent tools based modeling for decision making in robot systems are to increased reliability, flexibility, accuracy, productivity and profitability. A neuro fuzzy controller steering a truck while backup to a loading dock is demonstrated. A computational benefit of parallel nature is not only offered by the neuro fuzzy systems but also for the learning ability. The architecture also provides learning controls using feed forward neural networks. In order to achieve the robot goal autonomously, servo systems commands are generated by the intelligent decision-making controller. In the loading zone, from any initial position, designed neuro fuzzy controller is capable to guide the truck to dock shown by simulation results.