Solving the forward kinematics problem in parallel manipulators using an iterative artificial neural network strategy

In this paper we address the forward kinematics problem of a parallel manipulator and propose an iterative neural network strategy for its real-time solution to a desired level of accuracy. Parallel manipulators are closed kinematic structures that possess requisite rigidity to yield a high payload to self-weight ratio. Because of this unique feature, they have been employed in manufacturing, flight simulation systems, and medical robotics. However, it is this closed kinematic structure that has led to difficulty in their kinematic control, especially the forward kinematics control. The iterative neural network strategy we propose employs a trained neural network and an error compensation algorithm in the feedback loop. The proposed strategy was tested with data from a real-world flight simulation system. Results show that solutions with a maximum error in the position and orientation parameters of 0.25 mm and 0.01°, respectively, can be achieved in less than five iterations (or about 1 second). Because of the nature of this strategy, it is possible to implement it in a hardware form, which can result in a multi-fold reduction in the solution time for the same accuracy level.