Inverse kinematics and geometrically bounded singularities prevention of redundant manipulators: An Artificial Neural Network approach

This article presents an Artificial Neural Network (ANN) approach for fast inverse kinematics computation and effective geometrically bounded singularities prevention of redundant manipulators. Here, some bounded geometrical concepts are properly utilized to establish some characterizing matrices, to yield a simple performance index, and a null space vector for singularities avoidance/prevention and safe path generation. Then, a properly trained ANN is used in a novel scheme for the computation of inverse kinematics. This scheme includes the proposed null space vector being also computed by another properly trained ANN. The efficiency of the proposed ANN approach is demonstrated in the successful singularities prevention of a planar redundant manipulator performing a benchmark test.