Ceballos, Iker

Human behaviour prediction is important to enableproper and safe collaboration between humans and robotsworking within the same workspace. In an industrial environmentwhere not all the tasks can be automated, a human worker anda robot can be performing tasks simultaneously in a commonworkspace e.g. picking pieces from the same conveyor belt. In thiscase the conventional approach of stopping the robot wheneverit is going to collide with the worker is not an optimal solution asit decreases the actual cycle time of the robot and therefore theproductivity. In this paper a way of learning the intentions of thehuman worker, so that the robot can still operate safely, is soughtbased on 3D-sensor feedback, a skeleton model of the human anda supervised classification algorithm. Partitioning Around Medoids(PAM) is used to separate the recorded tracks into classes andthen SVM is trained with these data. Subsequently, real-timerecording is classified with the trained model to allow earlyprediction of the action the operator is performing

Learning Operator Intetions Using Supervised Learning for Safe Human-Robot Collaboration

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