Forward Kinematic Analysis of Spatial Parallel Robots Using a Parallel Evolutionary Neural Networks

The forward kinematics problem of parallel robots due to the resulting system of nonlinear equations has always been a challenge in the field of robotics. In this paper, an effective hybrid method is proposed based on the classification, neural networks, and particle swarm optimization algorithm for solving the forward kinematics problem of parallel robots. In order to increase the accuracy, the workspace of a parallel robot is divided into several subspaces. Then, the ECOC classifier is used to determine the subspace corresponding to the problem. Finally, the solutions of the forward kinematics problem are estimated using two evolutionary neural networks in each subspace. The proposed method is implemented on a 4-PUU parallel robot to estimate the pose of the moving platform through a path and its results compared with the results of the other neural network-based methods. The obtained results indicate that the proposed method provides a real-time solution with acceptable accuracy for the forward kinematics problem of the parallel robots.