A learning-based inverse kinematics solver for a multi-segment continuum robot in robot-independent mapping

Abstract

Inverse kinematics (IK) is one of the most fundamental problems in robotics, as it makes use of the kinematics equations to determine the joint configurations necessary to reach a desired end-effector pose. In the field of continuum robot, solving the IK is relatively challenging, owing to kinematic redundancy with infinite number of solutions.In this paper, we present a simplified model to represent a multi-segment continuum robot using virtual rigid links. Based on the model, its IK can be solved using a multilayer perceptron (MLP), a class of feedforward neural network (FNN). The transformation between virtual joint space to task space is described using Denavit-Hartenberg (D-H) convention. Using 20,000 established training data for supervised learning, the MLP reaches a mean squared error of 0.022 for a dual-segment continuum robot. The trained MLP is then used to find the joints for different end-effector positions, and the results show a mean relative error of 2.90% can be on the robot configuration. Hence, this simplified model and its MLP provide a simple method to evaluate the IK solution of a two-segment continuum robot, which can also be further generalized and implemented in multi-segment cases.