Neural network learning of the inverse kinematic relationships for a robot arm

A methodology is presented whereby a neural network is used to learn the inverse kinematic relationship for a robot arm. A two-link, two-degree-of-freedom planar robot arm is simulated, and an accompanying neural network which solves the inverse kinematic problem is presented. The method is based on Kohonen's self-organizing mapping algorithm using a Widrow-Hoff-type error correction rule as introduced by H. Ritter et al. (1988, 1990). The authors have specifically addressed a number of issues associated with the inverse kinematic solution, including the occurrence of singularities and multiple solutions. Simulation results for a planar two-degree-of-freedom arm provide evidence that this approach is successful. The approach is a significant improvement over other neural net approaches documented in the literature.