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A human-robot collaboration model empowered by object detection-driven AR assistance and operator behavior prediction
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DOI：10.1080/0951192X.2026.2649614.png)
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Abstract
En 中文
Human-robot collaboration (HRC) technology can facilitate high-variety, low-volume (HVLV) assembly systems. However, simultaneously empowering both the operator and the cobot to adopt changes remains challenging. This study proposes a bidirectional HRC empowerment model to achieve high team fluency in the HVLV assembly scenario. Deep learning-based augmented reality (AR) assembly assistance is offered to empower operators for a wide range of assembly tasks, and a hidden semi-Markov model (HSMM)-based prediction is proposed to enhance the robot’s cognitive capability in relation to the operator. An AR and HSMM empowered HRC assembly station, featuring two agents (the operator and the cobot), is implemented using the current model. The team fluency metrics and the NASA-TLX mental workload instrument are measured for four typical assembly scenarios: manual assembly, basic HRC, HSMM-assisted HRC, and HSMM and AR-assisted HRC assembly. The experiment results confirm that the proposed model increases assembly efficiency by 13% and decreases the overall workload by 9.6%. The proposed bidirectional HRC empowerment model, which integrates AR technology and the HSMM algorithm, contributes to both fluency and mental workload in diverse HRC assembly scenarios within the HVLV systems.
Keywords:
Augmented reality
assembly system
human-robot collaboration
hidden semi-Markov model
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