Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace.
Sensors (Basel)
; 22(18)2022 Sep 14.
Article
em En
| MEDLINE
| ID: mdl-36146296
ABSTRACT
Industry 4.0 transforms classical industrial systems into more human-centric and digitized systems. Close human-robot collaboration is becoming more frequent, which means security and efficiency issues need to be carefully considered. In this paper, we propose to equip robots with exteroceptive sensors and online motion generation so that the robot is able to perceive and predict human trajectories and react to the motion of the human in order to reduce the occurrence of the collisions. The dataset for training is generated in a real environment in which a human and a robot are sharing their workspace. An Encoder-Decoder based network is proposed to predict the human hand trajectories. A Model Predictive Control (MPC) framework is also proposed, which is able to plan a collision-free trajectory in the shared workspace based on this human motion prediction. The proposed framework is validated in a real environment that ensures collision free collaboration between humans and robots in a shared workspace.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Robótica
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
França