Video prediction for driving scenes with a memory differential motion network model

Li, Chenming; Chen, Xiuhong

doi:10.1007/s10489-022-03813-9

Cited by 4 publications

(1 citation statement)

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“…The model is evaluated based on MAE (mean absolute error), which is a widely used metric for evaluating the prediction based models [45][46][47][48]. The MAE is basically the sum of absolute difference between ground-truth 3D position and predicted position.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Human Arm Motion Prediction for Collision Avoidance in a Shared Workspace

Zheng

Wieber

Baber

et al. 2022

Sensors

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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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Section: Experiments and Resultsmentioning

confidence: 99%