Trajectory online adaption based on human motion prediction for teleoperation

Luo, Jing; Huang, Darong; Li, Yanan; Yang, Chenguang

Abstract

In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot's motion trajectory can be updated in real time through updating the parameters of the AR model. In the teleoperated robot's control loop, a virtual force model is defined to describe the interaction profile and to correct the robot's motion trajectory in real time. The proposed human motion prediction algorithm acts as a feedforward model to update the robot's motion and to revise this motion in the process of human-robot interaction (HRI). The convergence of this method is analyzed theoretically. Comparative studies demonstrate the enhanced performance of the proposed approach.

Citation

Luo, J., Huang, D., Li, Y., & Yang, C. (2022). Trajectory online adaption based on human motion prediction for teleoperation. IEEE Transactions on Automation Science and Engineering, 19(4), 3184-3191. https://doi.org/10.1109/tase.2021.3111678

• Journal Article Type: Article
• Acceptance Date: Sep 5, 2021
• Online Publication Date: Sep 20, 2021
• Publication Date: Oct 1, 2022
• Deposit Date: Sep 22, 2021
• Publicly Available Date: Sep 23, 2021
• Journal: IEEE Transactions on Automation Science and Engineering
• Print ISSN: 1545-5955
• Electronic ISSN: 1558-3783
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 19
• Issue: 4
• Pages: 3184-3191
• DOI: https://doi.org/10.1109/tase.2021.3111678
• Keywords: Electrical and Electronic Engineering; Control and Systems Engineering
• Public URL: https://uwe-repository.worktribe.com/output/7830885
• Publisher URL: https://ieeexplore.ieee.org/document/9541105