Prediction of Human Arm Target for Robot Reaching Movements
Chiara Talignani Landi, Yujiao Cheng, Federica Ferraguti, Marcello Bonfè, Cristian Secchi, Masayoshi Tomizuka
Abstract
The raise of collaborative robotics has allowed to create new spaces where robots and humans work in proximity. Consequently, to predict human movements and his/her final intention becomes crucial to anticipate robot next move, preserving safety and increasing efficiency. In this paper we propose a human-arm prediction algorithm that allows to infer if the human operator is moving towards the robot to intentionally interact with it. The human hand position is tracked by an RGB-D camera online. By combining the Minimum Jerk model with Semi-Adaptable Neural Networks we obtain a reliable prediction of the human hand trajectory and final target in a short amount of time. The proposed algorithm was tested in a multi-movements scenario with FANUC LR Mate 200iD/7L industrial robot.
BibTeX
@inproceedings{iros2019_predictionofhuma,
title = {Prediction of Human Arm Target for Robot Reaching Movements},
author = {Chiara Talignani Landi and Yujiao Cheng and Federica Ferraguti and Marcello Bonfè and Cristian Secchi and Masayoshi Tomizuka},
booktitle = {IROS 2019},
year = {2019}
}