An Improved Inverse Kinematics Solution for a Robot Arm Trajectory Using Multiple Adaptive Neuro-Fuzzy Inference Systems

Inverse kinematics of robots is a critical topic in the robotics field. Although there are conventional ways solving inverse kinematics, soft computing an important technology that has lately gained prominence due to its ability reduce complexity problem. This paper presents solution using multiple adaptive neuro-fuzzy inference systems (MANFIS). Different models were established by employing various methods identification. Subtractive Clustering (SCM), Fuzzy C-Means (FCM), and Grid Partitioning (GP) three used this study. work being carried out on 5-DOF articulated robot arm, which commonly industry. A mathematical model built based Denavit-Hartenberg (DH) approach. Following confirmation kinematic findings match actual observed values two types data sets generated: random set systematic trajectory. The then utilized train evaluate ANFIS choose optimal develop MANFIS model. Thus, prediction experimental compared assess performance