Experimental validation of a neuro-fuzzy controlled mobile manipulator for the inspection of automobile exhaust system

Abstract

This study establishes a new strategy to locate the coordinates of a crack in an automobile exhaust system using a 6 Degree of Freedom (DOF) mobile robot manipulator, utilizing a fuzzy control model and an Artificial Neural Network (ANN). The fuzzy position control model was applied as an alternative to traditional inverse kinematics approaches, and an ANN was used to correct the position of the robot based on a manipulator workspace. A Carbon Dioxide (CO₂) sensor was mounted on the base of the mobile robot to detect exhaust gas leakage. Several uncertain manipulator parameters affect precision and performance. These parameters are created from the tolerances and variations in the actuators and controls for production and assembly. To ensure manipulator end-effector location, a fuzzy control model with an Internet Protocol (IP) camera-based observational error model is applied to compensate for the robot parameter errors and improve the location accuracy of the robot manipulator. To demonstrate the effectiveness of the proposed control model, a simulation was implemented during the uncertainty in the robot link length. 6 DOF mobile robot manipulator was equipped at a low cost to help in the experimental validation to establish the robustness of the proposed controller in real-world applications.