Learning-Based Estimation of Forward Kinematics for an Orthotic Parallel Robotic Mechanism

Abstract

This paper introduces a 3D parallel robot with three identical five-degree-of-freedom chains connected to a circular brace end-effector, aimed to serve as an assistive device for patients with cervical spondylosis. The inverse kinematics of the system is solved analytically, whereas learning-based methods are deployed to solve the forward kinematics. The methods considered herein include a Koopman operator-based approach as well as a neural network-based approach. The task is to predict the position and orientation of end-effector trajectories. The dataset used to train these methods is based on the analytical solutions derived via inverse kinematics. The methods are tested both in simulation and via physical hard-ware experiments with the developed robot. Results validate the suitability of deploying learning-based methods for studying parallel mechanism forward kinematics that are generally hard to resolve analytically.