Robot kinematics learning computations using polynomial neural networks

Abstract

The group method of data handling (GMDH), a data analysis technique for identification of nonlinear complex systems, is a feature-based mapping neural network. It is also an example of a polynomial neural network (PNN). The PNN can be trained to interpolate an unknown function by observing few samples. The PNN, a major type of neural network used in airborne combat applications is used to interpolate robot forward and inverse kinematics computations (FKC and IKC). The FKC and IKC are used, respectively, to find the mapping from the joint space to the Cartesian space and to find the mapping from the Cartesian space to joint space. A PPN simulation software package has been developed for solving both FKC and IKC. The simulation is performed in a two-degree-of-freedom manipulator. The solutions of the FKC and IKC networks are compared with the analytic equations. The PNN learns successfully the indicated path. The simulation results show that the PNN can interpolate the indicated path with better than 99.87% accuracy when the PNN network has been trained on 361 data pairs. The approach can be expanded to six-degree-of-freedom manipulators. Detailed GMDH algorithms for constructing the PNN kinematics models are discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>