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

Jiewen Lai, Kaicheng Huang, Henry K. Chu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationIEEE International Conference on Robotics and Biomimetics, ROBIO 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781728163215
Publication statusPublished - Dec 2019
Event2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019 - Dali, China
Duration: 6 Dec 20198 Dec 2019

Publication series

NameIEEE International Conference on Robotics and Biomimetics, ROBIO 2019


Conference2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019


  • Continuum robots
  • Inverse kinematics
  • Artificial intelligence (AI)
  • Control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Mechanical Engineering
  • Control and Optimization

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