A comparative study of soft computing methods to solve inverse kinematics problem

Abstract

Robot arms are essential tools nowadays in industries due to its accuracy through high speed manufacturing. One of the most challenging problems in industrial robots is solving inverse kinematics. Inverse Kinematic Problem concerns with finding the values of angles which are related to the desired Cartesian location. With the development of Softcomputing-based methods, it's become easier to solve the inverse kinematic problem in higher speed with sufficient solutions rather than using traditional methods like numerical, geometric and algebraic. This paper presents a comparative study between different soft-computing based methods (Artificial Neural Network, Adaptive Neuro Fuzzy Inference System & Genetic Algorithms) applied to the problem of inverse kinematics. With the help of proposed method called minimized error function, both ANN and ANFIS are able to outperform other methods. The experimental test are done using 5DOF robot arm and analyzing the results proved the simulation results. Keywords: ANFIS, Forward kinematics, GA, Inverse kinematics, Meta-heuristic, NN, Robot arm, Soft-computing