Intelligent disassembly scenario understanding for human behavior and intention recognition towards self-perception human-robot collaboration system

The recycling of end-of-life (EOL) products poses significant challenges due to inefficient and unsafe disassembly processes. To address this, we propose a novel self-perception human-robot collaboration (HRC) system that enhances disassembly efficiency and safety through multi-modal human intention...

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Veröffentlicht in:Journal of manufacturing systems Jg. 83; S. 937 - 962
Hauptverfasser: Xiao, Jinhua, Wang, Bo, Huang, Kaile, Terzi, Sergio, Wang, Wei, Macchi, Marco
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2025
Schlagworte:
Behavioral research
Collaborative robots
Convolutional networks
Disassembly system
Disassembly systems
End-of-life product
End-of-life products
Human robot collaboration
Inference engines
Intelligent robots
Intent recognition
Intention recognition
Man machine systems
Network algorithms
Pattern recognition
Spatial temporals
Spatial-temporal graph convolutional network algorithm
ST-GCN algorithm
Temporal graphs
Virtual Production Development (VPD)
Intent recognition
Disassembly system
ST-GCN algorithm
Human robot collaboration
End-of-life product
ISSN:0278-6125, 1878-6642
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Zusammenfassung:The recycling of end-of-life (EOL) products poses significant challenges due to inefficient and unsafe disassembly processes. To address this, we propose a novel self-perception human-robot collaboration (HRC) system that enhances disassembly efficiency and safety through multi-modal human intention recognition. Our core methodological innovation lies in the real-time fusion of three key perception modules: action recognition using a Spatial-Temporal Graph Convolutional Network (ST-GCN), disassembly tool detection based on an enhanced YOLO algorithm, and facial angle recognition for operator awareness inference. A dedicated dataset for retired power battery disassembly was constructed to support this research, encompassing human skeletal data for action recognition, labeled images for tool detection, and facial expression detection. The proposed system was validated on a physical HRC disassembly platform. Experimental results demonstrate a marked improvement, with our integrated intention recognition method achieving an accuracy of approximately 85 %, significantly outperforming traditional single-modality approaches. Furthermore, the HRC disassembly operation was completed in 238 s, which is 60 s (or 20 %) faster than purely manual disassembly. This work provides a robust and efficient HRC disassembly framework for intelligent disassembly scenario understanding, contributing to advancing circular manufacturing. •We propose a self-perception human-robot collaborative (HRC) system to recognize the human action, face angle, and tool detection.•A real disassembly system platform provides an experimental design to verify the feasibility of the proposed method.•The integrated ST-GCN algorithm has been applied in action recognition with faster speed and higher accuracy•We deeply analyze the target detection for disassembly tool and face angle recognition in real-time.•The results demonstrate that the accuracy of the HRC intent recognition method reaches 85 %, outperforming traditional intent recognition.
ISSN:0278-6125
1878-6642
DOI:10.1016/j.jmsy.2025.11.012