Human arm motion prediction in human-robot interaction based on a modified minimum jerk model

Jing Zhao, Shiqiu Gong, Biyun Xie, Yaxing Duan, Ziqiang Zhang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Accurate prediction of human motion is essential to ensure the efficiency and safety of human-robot interaction (HRI), especially when humans and robots interact closely in a shared environment. A novel method is developed in this work to address three fundamental problems in human arm motion prediction, i.e. given the early-stage fingertip trajectory of a human arm reaching motion, how to predict the motion duration, the motion destination, and the remaining fingertip trajectory. First, a modified minimum jerk model (MMJM), containing three input parameters—the motion duration, the motion destination, and the early-stage fingertip trajectory, is developed to express and predict the remaining fingertip trajectory. Next, these unknown parameters are determined by determining the optimal starting time of motion prediction and employing Gaussian process regression models (GPRs). Finally, the proposed human arm motion prediction method is validated by simulations and HRI experiments.

Original languageEnglish
Pages (from-to)205-218
Number of pages14
JournalAdvanced Robotics
Volume35
Issue number3-4
DOIs
StatePublished - 2021

Bibliographical note

Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.

Keywords

  • Gaussian process regression
  • Human-robot interaction
  • minimum jerk model
  • motion prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Computer Science Applications

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