A Study on Solving the Inverse Kinematics of Serial Robots using Artificial Neural Network and Fuzzy Neural Network

One of the most important problems in robotics is the computation of the inverse kinematics (IK). This apparently simple task is necessary to determine how to move each joint in order to reach a desired end-effector pose in Cartesian space. However, the associated forward kinematics can be a highly nonlinear, non-bijective, and multidimensional function for which it may be difficult or even impossible to find closed-form solutions for its inverse – especially as the number of Degrees of Freedom (DoF) increases. Several approaches have been taken using non-linear approximators to solve IK problems. In this paper, we present a study on solving the inverse kinematics of multiple robotic arms using Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). For this study, we experimented with 4, 5, 6 and 7 DoF serial robots, with combinations of prismatic and revolute joints. Unlike other task-oriented solvers, our goal was not to predict poses based on specific trajectories (linear or otherwise), but instead to learn the entire robot workspaces. This goal better addresses real-world uses of robotic IK, where any end-effector pose should be reachable from any current pose. From the experiments conducted, we conclude that both ANN and ANFIS converged to some degree to the underlying inverse kinematics function, however approximation errors and the time and effort required to achieve those results may not justify their use vis-a-vis other methods in the literature.