Learning Operator Intetions Using Supervised Learning for Safe Human-Robot Collaboration
Translated title
Learning Operator Intentions Using Supervised Learning for Safe Human-Robot Collaboration
Author
Term
4. term
Education
Publication year
2017
Submitted on
2017-06-01
Abstract
Human behaviour prediction is important to enable proper and safe collaboration between humans and robots working within the same workspace. In an industrial environment where not all the tasks can be automated, a human worker and a robot can be performing tasks simultaneously in a common workspace e.g. picking pieces from the same conveyor belt. In this case the conventional approach of stopping the robot whenever it is going to collide with the worker is not an optimal solution as it decreases the actual cycle time of the robot and therefore the productivity. In this paper a way of learning the intentions of the human worker, so that the robot can still operate safely, is sought based on 3D-sensor feedback, a skeleton model of the human and a supervised classification algorithm. Partitioning Around Medoids (PAM) is used to separate the recorded tracks into classes and then SVM is trained with these data. Subsequently, real-time recording is classified with the trained model to allow early prediction of the action the operator is performing.
Keywords
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