Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace

Pu Zheng (1) , Pierre-Brice Wieber (2) , Junaid Baber (1) , Olivier Aycard (1)

1 LIG - Laboratoire d'Informatique de Grenoble
2 Inria Grenoble - Rhône-Alpes

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.

Mots clés: human robot collaboration; human motion prediction; collision avoidance

Domaines: Automatique / Robotique; Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Pu Zheng, Pierre-Brice Wieber, Junaid Baber, Olivier Aycard. Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace. Sensors, 2022, 22 (18), pp.6951. ⟨10.3390/s22186951⟩. ⟨hal-03789355⟩