Paper Info
Title
Prediction Of Human Arm Target For Robot Reaching Movements
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.
Year
DOI
Venue
2019
10.1109/IROS40897.2019.8968559
2019 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Computer vision,Human operator,Human arm,Computer science,Jerk,Industrial robot,RGB color model,Artificial intelligence,Robot,Artificial neural network,Trajectory
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Chiara Talignani Landi1203.75
Yujiao Cheng221.41
Federica Ferraguti38810.65
Marcello Bonfé46612.78
Cristian Secchi597781.94
Masayoshi Tomizuka6127.44