Neural network in combination with a differential evolutionary training algorithm for addressing ambiguous articulated inverse kinematic problems

Abstract

Inverse kinematic systems are an important tool in many disciplines (from animated game characters to robotic structures). However, inverse kinematic problems are a challenging topic (due to their computational cost, highly non-linear nature and discontinuous, ambiguous characteristics with multiple or no-solutions). Neural networks offer a flexible computational model that is able to address these difficult inverse kinematic problems where traditional, formal techniques would be difficult or impossible In this paper, we present a solution that combines an artificial neural network and a differential evolutionary algorithm for solving inverse kinematic problems. We explore the potential advantages of neural networks for providing robust solutions to a wide range of inverse kinematic problems, particularly areas involving multiple fitness criteria, optimization, pattern and comfort factors, and function approximation. We evaluate the technique through experimentation, such as, training times, fitness criteria and quality metrics.