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Open AccessArticle

Assessment of Handover Prediction Models in Estimation of Cycle Times for Manual Assembly Tasks in a Human–Robot Collaborative Environment

1
Department of Industrial Engineering and System Management, Feng Chia University, Taichung 407, Taiwan
2
International School of Technology and Management, Feng Chia University, Taichung 407, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 556; https://doi.org/10.3390/app10020556
Received: 30 September 2019 / Revised: 2 January 2020 / Accepted: 10 January 2020 / Published: 12 January 2020
(This article belongs to the Special Issue Industrial Engineering and Management: Current Issues and Trends)
The accuracy and fluency of a handover task affects the work efficiency of human–robot collaboration. A precise and proactive estimation of handover time points by robots when handing over assembly parts to humans can minimize waiting times and maximize efficiency. This study investigated and compared the cycle time, waiting time, and operators’ subjective preference of a human–robot collaborative assembly task when three handover prediction models were applied: traditional method-time measurement (MTM), Kalman filter, and trigger sensor approaches. The scenarios of a general repetitive assembly task and repetitive assembly under a learning curve were investigated. The results revealed that both the Kalman filter prediction model and the trigger sensor method were superior to the MTM fixed-time model in both scenarios in terms of cycle time and subjective preference. The Kalman filter prediction model could adjust the handover timing according to the operator’s current speed and reduce the waiting time of the robot and operator, thereby improving the subjective preference of the operator. Moreover, the trigger sensor method’s inherent flexibility concerning random single interruptions on the operator’s side earned it the highest scores in the satisfaction assessment. View Full-Text
Keywords: human–robot collaboration; handover task; waiting time; handover time prediction method; Kalman filter; MTM human–robot collaboration; handover task; waiting time; handover time prediction method; Kalman filter; MTM
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MDPI and ACS Style

Tang, K.-H.; Ho, C.-F.; Mehlich, J.; Chen, S.-T. Assessment of Handover Prediction Models in Estimation of Cycle Times for Manual Assembly Tasks in a Human–Robot Collaborative Environment. Appl. Sci. 2020, 10, 556.

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