Learning-Based Approaches for Forward Kinematic Modeling of Continuum Manipulators

Abstract

Abstract Forward kinematic model (FKM) is an essential module in the control law design of manipulator robots. Unlike rigid manipulators where it can be easily established, it remains a real challenge for their continuum counterparts. Model-based and learning-based approaches are commonly used for the forward kinematic modeling of continuum manipulators. Model-based approaches generally lead to imprecise FKM models due to several modeling assumptions, while learning-based approaches generally yield acceptable performance. However, the choice of an appropriate learning model remains a challenging task. In the framework of the forward kinematic modeling of continuum manipulators, this paper proposes an experimental and structural comparative study of the commonly used learning models, namely the multilayer perceptron (MLP), radial based functions (RBF), support vector regression (SVR), and Co-Active adaptive neuro-fuzzy inference system (CANFIS). The Compact Bionic Handling Assistant (CBHA) robot is used as an experimental platform and the predictions of the different learning models are compared respectively to a high precision motion capture system. According to the comparative study, we noted better accuracy for SVRs, rapid convergence for RBFs, and a good compromise between learning time and accuracy for MLPs. CANFIS offers accuracy close to that of SVRs but with much shorter learning time.