Learning the inverse kinematics of a robot manipulator using the Bees Algorithm

Abstract

In this paper, the Bees algorithm was used to train multi-layer perceptron neural networks to model the inverse kinematics of an articulated robot manipulator arm. The Bees Algorithm is a recently developed parameter optimisation algorithm that is inspired by the foraging behaviour of honey bees. The Bees Algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. Three neural networks were trained to reproduce a set of input/output numerical examples of the inverse kinematics of the main three joints of an articulated robotic manipulator. The results prove the remarkable robustness of the Bees Algorithm, which consistently trained the neural networks to model the kinematics data with very high accuracy. The learning results obtained by the proposed algorithm are compared to the results obtained by the standard Backpropagation Algorithm and an Evolutionary Algorithm. The comparative study highlights the superior performance of the proposed Bees Algorithm over the other algorithms.