Fast Prediction of a Worker’s Reaching Motion Without a Skeleton Model (F-PREMO)

This paper proposes a fast and highly accurate prediction method for the reaching motions performed by a worker in cooperative work with a robot, called fast prediction of reaching motion (F-PREMO). Cooperative work is permitted by a 2011 ISO revision under the condition that risk assessment has been performed. To increase production effectivity, it is essential to predict the worker's movement and control the robot based on the prediction. This paper focuses on the movement of a worker's hand, called the reaching motion, which is relevant to many types of assembling tasks. The most common existing methods require attaching markers to the workers or installing three-dimensional (3D) sensors in front of the workers since these methods require the real-time estimation of skeleton models of the workers. These requirements lead to difficulties in introducing prediction methods at production sites. To solve this problem, we propose a prediction method for the reaching motion that neither requires the attachment of markers nor limits the placement of the 3D sensor. The proposed method first computes a feature vector of the reaching motion and then performs the prediction using random forest with the computed feature vector as input. Experimental results show that the proposed method can predict the reaching motion from the initial 50% of the movement of the worker with more than 80% accuracy in less than 5.2 [ms].