Modeling and analysis of a parallel continuum robot using artificial neural network

Continuum robotics has become a popular area of research, with many applications derived. Though some of the designs and relative modeling have been applied in actual occasions, new modeling approaches are still required for some new designs in structure. A parallel continuum robot (PCR) prototype imitating the bionic trunk from FESTO is designed in this paper. Since the structure design of PCR adopts a cone shape to enhance stiffness, the prototype shows stronger nonlinearity than continuum robots which are modeled based on piecewise constant curvature model. Due to the structure of the PCR, this paper analyzes its nonlinearity via Euler-Bernoulli beam formulations. Three kinds of artificial neural networks (ANN) are established to identify the nonlinear parallel rod system, namely, back propagation (BP), racial basis function (RBF) and a hybrid approach using genetic algorithm (GA) to optimize the weights of BP. After training by the datum obtained from the optotrak certus 3020 active tracking system, the result shows that increasing a suitable amount of iterant training data will heighten the generation ability of the network, and the best approach to model the parallel system is the hybrid approach. For forward kinematics, the mean position error of the end-effector is 0.3%, and for inverse kinematics the mean position error is 0.43%, so, such an approach can solve the forward and inverse kinematics problem of the PCR in an acceptable precision.