Human arm motion prediction in human-robot interaction based on a modified minimum jerk model. (16th February 2021)

• Record Type: Journal Article
• Authors: Zhao, Jing; Gong, Shiqiu; Xie, Biyun; Duan, Yaxing; Zhang, Ziqiang
• Is Part Of: Advanced robotics. Volume 35:Number 3/4(2021)
• Page Start: 205
• Page End: 218
• Publication Date: 2021-02-16
• Subjects: Human-robot interaction -- motion prediction -- minimum jerk model -- Gaussian process regression; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2020.1840432
• Languages: English
• ISSNs: 0169-1864

Abstract

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. GRAPHICAL ABSTRACT: UF0001